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false | 2014-01-22 17:36:53 | Neo4j Backup: Store copy and consistency check | [
"neo4j"
] | [
"neo4j"
] | One of the lesser known things about the http://docs.neo4j.org/chunked/milestone/backup-embedded-and-server.html[Neo4j online backup tool], which I http://www.markhneedham.com/blog/2014/01/19/neo4j-backup-java-lang-classcastexception-org-jboss-netty-buffer-bigendianheapchannelbuffer-cannot-be-cast-to-org-neo4j-cluster-com-message-message/[wrote about last week], is that conceptually there are two parts to it:
. Copying the store files to a location of your choice
. Verifying that those store files are consistent.
By default both of these run when you run the 'neo4j-backup' script but sometimes it's useful to be able to run them separately.
If we want to just run the copying the store files part of the process we can tell the backup tool to skip the consistency check by using the 'https://github.com/neo4j/neo4j/blob/2.0-maint/enterprise/backup/src/main/java/org/neo4j/backup/BackupTool.java#L97[verify]' flag:
[source,bash]
----
$ pwd
/Users/markneedham/Downloads/neo4j-enterprise-2.0.0
----
[source,bash]
----
$ ./bin/neo4j-backup -from single://127.0.0.1 -to /tmp/foo -verify false
Performing full backup from 'single://127.0.0.1'
Files copied
................ done
Done
----
If we ran that without the 'verify' flag we'd see the output of the consistency checker as well:
[source,bash]
----
$ ./bin/neo4j-backup -from single://127.0.0.1 -to /tmp/foo
Performing full backup from 'single://127.0.0.1'
Files copied
................ done
Full consistency check
.................... 10%
.................... 20%
.................... 30%
.................... 40%
.................... 50%
.................... 60%
.................... 70%
.................... 80%
.................... 90%
.................... 100%
Done
----
If we already have a backup and only want to run the consistency checker we can run the following command:
[source,bash]
----
$ java -cp 'lib/*:system/lib/*' org.neo4j.consistency.ConsistencyCheckTool /tmp/foo
Full consistency check
.................... 10%
.................... 20%
.................... 30%
.................... 40%
.................... 50%
.................... 60%
.................... 70%
.................... 80%
.................... 90%
.................... 100%
----
The consistency tool itself takes a 'https://github.com/neo4j/neo4j/blob/2.0-maint/enterprise/consistency-check/src/main/java/org/neo4j/consistency/ConsistencyCheckTool.java#L149[config]' flag which gives you some control over what things you want to consistency check.
The various options are defined in +++<cite>+++https://github.com/neo4j/neo4j/blob/2.0-maint/enterprise/consistency-check/src/main/java/org/neo4j/consistency/ConsistencyCheckSettings.java#L52[org.neo4j.consistency.ConsistencyCheckSettings]+++</cite>+++.
For example, if we want to change the file that the consistency check report is written to we could add the following property to our config file:
[source,text]
----
$ tail -n 1 conf/neo4j.properties
consistency_check_report_file=/tmp/foo.txt
----
And then run the consistency tool like so:
[source,bash]
----
$ java -cp 'lib/*:system/lib/*' org.neo4j.consistency.ConsistencyCheckTool -config conf/neo4j.properties /tmp/foo
----
If there are any inconsistencies they'll now be written to that file rather than to a file in the store directory.
You can also pass that 'config' flag to the backup tool and it will make use of it when it runs the consistency check. e.g.
[source,bash]
----
$ ./bin/neo4j-backup -from single://127.0.0.1 -to /tmp/foo -config conf/neo4j.properties
----
Most of the time you don't need to worry too much about either of these commands but I always forget what the various options are so I thought I'd better write it up while it's fresh in my mind. | null | null | [
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] | neo4j-backup-store-copy-and-consistency-check | https://markhneedham.com/blog/2014/01/22/neo4j-backup-store-copy-and-consistency-check |
false | 2014-06-27 10:51:25 | Neo4j: Cypher - Separation of concerns | [
"neo4j"
] | [
"neo4j"
] | While preparing https://skillsmatter.com/skillscasts/5385-analysing-london-s-nosql-meetups-using-clojure-neocons-luminus[my talk on building Neo4j backed applications with Clojure] I realised that some of the queries I'd written were incredibly complicated and went against anything I'd learnt about separating different concerns.
One example of this was the query I used to generate the data for the following page of the meetup application I've been working on:
image::{{<siteurl>}}/uploads/2014/06/2014-06-27_08-19-34.png[2014 06 27 08 19 34,600]
image::{{<siteurl>}}/uploads/2014/06/2014-06-27_08-31-13.png[2014 06 27 08 31 13,600]
Depending on the selected tab you can choose to see the people signed up for the meetup and the date that they signed up or the topics that those people are interested in.
For reference, this is an outline of the schema of the graph behind the application:
image::{{<siteurl>}}/uploads/2014/06/2014-06-27_11-51-00.png[2014 06 27 11 51 00,600]
This was my initial query to get the data:
[source,cypher]
----
MATCH (event:Event {id: {eventId}})-[:HELD_AT]->(venue)
OPTIONAL MATCH (event)<-[:TO]-(rsvp)<-[:RSVPD]-(person)
OPTIONAL MATCH (person)-[:INTERESTED_IN]->(topic) WHERE ()-[:HAS_TOPIC]->(topic)
WITH event, venue, rsvp, person, COLLECT(topic) as topics ORDER BY rsvp.time
OPTIONAL MATCH (rsvp)<-[:NEXT]-(initial)
WITH event, venue, COLLECT({rsvp: rsvp, initial: initial, person: person, topics: topics}) AS responses
WITH event, venue,
[response in responses WHERE response.initial is null AND response.rsvp.response = "yes"] as attendees,
[response in responses WHERE NOT response.initial is null] as dropouts, responses
UNWIND([response in attendees | response.topics]) AS topics
UNWIND(topics) AS topic
WITH event, venue, attendees, dropouts, {id: topic.id, name:topic.name, freq:COUNT(*)} AS t
RETURN event, venue, attendees, dropouts, COLLECT(t) AS topics
----
The first two lines of the query works out which people have RSVP'd to a particular event, the 3rd line captures the topics they're interested in as long as the topic is linked to at least one of the NoSQL London groups.
We then optionally capture their initial RSVP in case they've changed it before doing a bit of data manipulation to group everything together.
If we run a slight variation of that which only shows a few of the topics, attendees and dropouts this is the type of result we get:
[source,bash]
----
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| event.name | venue.name | [a IN attendees[0..5] | a.person.name] | [d in dropouts[0..5] | d.person.name] | topics[0..5] |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "Building Neo4j backed web applications" | "Skills Matter" | ["Mark Needham","Alistair Jones","Jim Webber","Axel Morgner","Ramesh"] | ["Frank Gibson","Keith Hinde","Richard Mason","Ollie Glass","Tom"] | [{id -> 10538, name -> "Business Intelligence", freq -> 3},{id -> 61680, name -> "HBase", freq -> 3},{id -> 61679, name -> "Hive", freq -> 2},{id -> 193021, name -> "Graph Databases", freq -> 12},{id -> 85951, name -> "JavaScript Frameworks", freq -> 10}] |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
----
The problem is we've mixed together two different concerns - the *attendees* to a meetup and the *topics* they're interested in - which made the query quite hard to understand when I came back to it a couple of months later.
Instead what we can do is split the query in two and make two different calls to the server. We then end up with the following:
[source,cypher]
----
// Get the event + attendees + dropouts
MATCH (event:Event {id: {eventId}})-[:HELD_AT]->(venue)
OPTIONAL MATCH (event)<-[:TO]-(rsvp)<-[:RSVPD]-(person)
WITH event, venue, rsvp, person ORDER BY rsvp.time
OPTIONAL MATCH (rsvp)<-[:NEXT]-(initial)
WITH event, venue, COLLECT({rsvp: rsvp, initial: initial, person: person}) AS responses
WITH event, venue,
[response in responses WHERE response.initial is null
AND response.rsvp.response = "yes"] as attendees,
[response in responses WHERE NOT response.initial is null] as dropouts
RETURN event, venue, attendees, dropouts
----
[source,cypher]
----
// Get the topics the attendees are interested in
MATCH (event:Event {id: {eventId}})
MATCH (event)<-[:TO]-(rsvp {response: "yes"})<-[:RSVPD]-(person)-[:INTERESTED_IN]->(topic)
WHERE ()-[:HAS_TOPIC]->(topic)
RETURN topic.id AS id, topic.name AS name, COUNT(*) AS freq
----
The first query is still a bit complex but that's because there's a bit of tricky logic to distinguish people who signed up and dropped out. However, the second query is now quite easy to read and expresses it's intent very clearly. | null | null | [
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] | neo4j-cypher-separation-of-concerns | https://markhneedham.com/blog/2014/06/27/neo4j-cypher-separation-of-concerns |
false | 2014-06-29 02:56:53 | Neo4j's Cypher vs Clojure - Group by and Sorting | [
"clojure",
"neo4j",
"cypher"
] | [
"Clojure",
"neo4j"
] | One of the points that I emphasised during my talk on https://skillsmatter.com/skillscasts/5385-analysing-london-s-nosql-meetups-using-clojure-neocons-luminus[building Neo4j backed applications using Clojure] last week is understanding when to use Cypher to solve a problem and when to use the programming language.
A good example of this is in the meetup application I've been working on. I have a collection of events and want to display past events in descending order and future events in ascending order.
First let's create some future and some past events based on the current timestamp of 1404006050535:
[source,cypher]
----
CREATE (event1:Event {name: "Future Event 1", timestamp: 1414002772427 })
CREATE (event2:Event {name: "Future Event 2", timestamp: 1424002772427 })
CREATE (event3:Event {name: "Future Event 3", timestamp: 1416002772427 })
CREATE (event4:Event {name: "Past Event 1", timestamp: 1403002772427 })
CREATE (event5:Event {name: "Past Event 2", timestamp: 1402002772427 })
----
If we return all the events we see the following:
[source,cypher]
----
$ MATCH (e:Event) RETURN e;
==> +------------------------------------------------------------+
==> | e |
==> +------------------------------------------------------------+
==> | Node[15414]{name:"Future Event 1",timestamp:1414002772427} |
==> | Node[15415]{name:"Future Event 2",timestamp:1424002772427} |
==> | Node[15416]{name:"Future Event 3",timestamp:1416002772427} |
==> | Node[15417]{name:"Past Event 1",timestamp:1403002772427} |
==> | Node[15418]{name:"Past Event 2",timestamp:1402002772427} |
==> +------------------------------------------------------------+
==> 5 rows
==> 13 ms
----
We can achieve the desired grouping and sorting with the following cypher query:
[source,lisp]
----
(def sorted-query "MATCH (e:Event)
WITH COLLECT(e) AS events
WITH [e IN events WHERE e.timestamp <= timestamp()] AS pastEvents,
[e IN events WHERE e.timestamp > timestamp()] AS futureEvents
UNWIND pastEvents AS pastEvent
WITH pastEvent, futureEvents ORDER BY pastEvent.timestamp DESC
WITH COLLECT(pastEvent) as orderedPastEvents, futureEvents
UNWIND futureEvents AS futureEvent
WITH futureEvent, orderedPastEvents ORDER BY futureEvent.timestamp
RETURN COLLECT(futureEvent) AS orderedFutureEvents, orderedPastEvents")
----
We then use the following function to call through to the Neo4j server using the excellent https://github.com/michaelklishin/neocons[neocons] library:
[source,lisp]
----
(ns neo4j-meetup.db
(:require [clojure.walk :as walk])
(:require [clojurewerkz.neocons.rest.cypher :as cy])
(:require [clojurewerkz.neocons.rest :as nr]))
(def NEO4J_HOST "http://localhost:7521/db/data/")
(defn cypher
([query] (cypher query {}))
([query params]
(let [conn (nr/connect! NEO4J_HOST)]
(->> (cy/tquery query params)
walk/keywordize-keys))))
----
We call that function and grab the first row since we know there won't be any other rows in the result:
[source,lisp]
----
(def query-result (->> ( db/cypher sorted-query) first))
----
Now we need to extract the past and future collections so that we can display them on the page which we can do like so:
[source,lisp]
----
> (map #(% :data) (query-result :orderedPastEvents))
({:timestamp 1403002772427, :name "Past Event 1"} {:timestamp 1402002772427, :name "Past Event 2"})
> (map #(% :data) (query-result :orderedFutureEvents))
({:timestamp 1414002772427, :name "Future Event 1"} {:timestamp 1416002772427, :name "Future Event 3"} {:timestamp 1424002772427, :name "Future Event 2"})
----
An alternative approach is to return the events from cypher and then handle the grouping and sorting in clojure. In that case our query is much simpler:
[source,lisp]
----
(def unsorted-query "MATCH (e:Event) RETURN e")
----
We'll use the https://github.com/clj-time/clj-time[clj-time] library to determine the current time:
[source,lisp]
----
(def now (clj-time.coerce/to-long (clj-time.core/now)))
----
First let's split the events into past and future:
[source,lisp]
----
> (def grouped-by-events
(->> (db/cypher unsorted-query)
(map #(->> % :e :data))
(group-by #(> (->> % :timestamp) now))))
> grouped-by-events
{true [{:timestamp 1414002772427, :name "Future Event 1"} {:timestamp 1424002772427, :name "Future Event 2"} {:timestamp 1416002772427, :name "Future Event 3"}],
false [{:timestamp 1403002772427, :name "Past Event 1"} {:timestamp 1402002772427, :name "Past Event 2"}]}
----
And finally we sort appropriately using these functions:
[source,lisp]
----
(defn time-descending [row] (* -1 (->> row :timestamp)))
(defn time-ascending [row] (->> row :timestamp))
----
[source,lisp]
----
> (sort-by time-descending (get grouped-by-events false))
({:timestamp 1403002772427, :name "Past Event 1"} {:timestamp 1402002772427, :name "Past Event 2"})
> (sort-by time-ascending (get grouped-by-events true))
({:timestamp 1414002772427, :name "Future Event 1"} {:timestamp 1416002772427, :name "Future Event 3"} {:timestamp 1424002772427, :name "Future Event 2"})
----
I used Clojure to do the sorting and grouping in my project because the query to get the events was a bit more complicated and became very difficult to read with the sorting and grouping mixed in.
Unfortunately cypher doesn't provide an easy way to sort within a collection so we need our sorting in the row context and then collect the elements back again afterwards. | null | null | [
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] | neo4j-cypher-vs-clojure-for-group-by-and-sorting | https://markhneedham.com/blog/2014/06/29/neo4j-cypher-vs-clojure-for-group-by-and-sorting |
false | 2014-06-29 08:45:34 | Neo4j: Set Based Operations with the experimental Cypher optimiser | [
"neo4j"
] | [
"neo4j"
] | A few months ago I wrote about http://www.markhneedham.com/blog/2014/02/20/neo4j-cypher-set-based-operations/[cypher queries which look for a missing relationship] and showed how you could optimise them by re-working the query slightly.
To refresh, we wanted to find all the people in the London office that I hadn't worked with given this model\...
image:{{<siteurl>}}/uploads/2014/02/2014-02-18_17-04-01.png[]</img>
\...and this initial query:
[source,cypher]
----
MATCH (p:Person {name: "me"})-[:MEMBER_OF]->(office {name: "London Office"})<-[:MEMBER_OF]-(colleague)
WHERE NOT (p-[:COLLEAGUES]->(colleague))
RETURN COUNT(colleague)
----
This took on average 7.46 seconds to execute using https://github.com/mneedham/cypher-query-tuning[cypher-query-tuning] so we came up with the following version which took 150 ms on average:
[source,cypher]
----
MATCH (p:Person {name: "me"})-[:COLLEAGUES]->(colleague)
WITH p, COLLECT(colleague) as marksColleagues
MATCH (colleague)-[:MEMBER_OF]->(office {name: "London Office"})<-[:MEMBER_OF]-(p)
WHERE NOT (colleague IN marksColleagues)
RETURN COUNT(colleague)
----
With the http://neo4j.com/blog/neo4j-2-1-graph-etl/[release of Neo4j 2.1] we can now make use of Ronja - the experimental Cypher optimiser - which performs much better for certain types of queries. I thought I'd give it a try against this one.
We can use the experimental optimiser by prefixing our query like so:
[source,cypher]
----
cypher 2.1.experimental MATCH (p:Person {name: "me"})-[:MEMBER_OF]->(office {name: "London Office"})<-[:MEMBER_OF]-(colleague)
WHERE NOT (p-[:COLLEAGUES]->(colleague))
RETURN COUNT(colleague)
----
If we run that through the query tuner we get the following results:
[source,bash]
----
$ python set-based.py
cypher 2.1.experimental MATCH (p:Person {name: "me"})-[:MEMBER_OF]->(office {name: "London Office"})<-[:MEMBER_OF]-(colleague)
WHERE NOT (p-[:COLLEAGUES]->(colleague))
RETURN COUNT(colleague)
Min 0.719580888748 50% 0.723278999329 95% 0.741609430313 Max 0.743646144867
MATCH (p:Person {name: "me"})-[:COLLEAGUES]->(colleague)
WITH p, COLLECT(colleague) as marksColleagues
MATCH (colleague)-[:MEMBER_OF]->(office {name: "London Office"})<-[:MEMBER_OF]-(p)
WHERE NOT (colleague IN marksColleagues)
RETURN COUNT(colleague)
Min 0.706955909729 50% 0.715770959854 95% 0.731880950928 Max 0.733670949936
----
As you can see there's not much in it - our original query now runs as quickly as the optimised one. Ronja #ftw!
Give it a try on your slow queries and see how it gets on. There'll certainly be some cases where it's slower but over time it should be faster for a reasonable chunk of queries. | null | null | [
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] | neo4j-set-based-operations-with-the-experimental-cypher-optimiser | https://markhneedham.com/blog/2014/06/29/neo4j-set-based-operations-with-the-experimental-cypher-optimiser |
false | 2014-06-28 23:35:25 | Data Science: Mo' Data Mo' Problems | [
"data-science-2"
] | [
"Data Science"
] | Over the last couple of years I've worked on several proof of concept style Neo4j projects and on a lot of them people have wanted to work with their entire data set which I don't think makes sense so early on.
In the early parts of a project we're trying to *prove out our approach* rather than prove we can handle big data - something that http://www.markhneedham.com/blog/2012/09/30/data-science-making-sense-of-the-data/[Ashok taught me a couple of years ago on a project we worked on together].
In a Neo4j project that means coming up with an effective way to model and query our data and if we lose track of this it's very easy to get sucked into working on the big data problem.
This could mean optimising our import scripts to deal with huge amounts of data or working out how to handle different aspects of the data (e.g. variability in shape or encoding) that only seem to reveal themselves at scale.
These are certainly problems that we need to solve but in my experience they end up taking much more time than expected and therefore aren't the best problem to tackle when time is limited. Early on we want to create some momentum and keep the feedback cycle fast.
We probably want to tackle the data size problem as part of the http://thinkrelevance.com/blog/2012/04/04/big-data-reference-model[implementation/production stage] of the project to use Michael Nygaard's terminology.
At this stage we'll have some confidence that our approach makes sense and then we can put aside the time to set things up properly.
I'm sure there are some types of projects where this approach doesn't make sense so I'd love to hear about them in the comments so I can spot them in future. | null | null | [
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] | data-science-mo-data-mo-problems | https://markhneedham.com/blog/2014/06/28/data-science-mo-data-mo-problems |
false | 2014-06-28 11:12:30 | Neo4j: Cypher - Finding movies by decade | [
"neo4j"
] | [
"neo4j"
] | I was recently asked how to find the *number of movies produced per decade* in the movie data set that comes with the Neo4j browser and can be imported with the following command:
[source,bash]
----
:play movies
----
We want to get one row per decade and have a count alongside so the easiest way is to start with one decade and build from there.
[source,cypher]
----
MATCH (movie:Movie)
WHERE movie.released >= 1990 and movie.released <= 1999
RETURN 1990 + "-" + 1999 as years, count(movie) AS movies
ORDER BY years
----
Note that we're doing a label scan of all nodes of type +++<cite>+++Movie+++</cite>+++ as there are no indexes for range queries. In this case it's fine as we have few movies but If we had 100s of thousands of movies then we'd want to optimise the WHERE clause to make use of an http://docs.neo4j.org/chunked/stable/query-where.html#query-where-patterns[IN] which would then use any indexes.
If we run the query we get the following result:
[source,bash]
----
==> +----------------------+
==> | years | movies |
==> +----------------------+
==> | "1990-1999" | 21 |
==> +----------------------+
==> 1 row
----
Let's pull out the start and end years so they're explicitly named:
[source,cypher]
----
WITH 1990 AS startDecade, 1999 AS endDecade
MATCH (movie:Movie)
WHERE movie.released >= startDecade and movie.released <= endDecade
RETURN startDecade + "-" + endDecade as years, count(movie)
ORDER BY years
----
Now we need to create a collection of start and end years so we can return more than one. We can use the http://docs.neo4j.org/chunked/stable/query-unwind.html[UNWIND] function to take a collection of decades and run them through the rest of the query:
[source,cypher]
----
UNWIND [{start: 1970, end: 1979}, {start: 1980, end: 1989}, {start: 1980, end: 1989}, {start: 1990, end: 1999}, {start: 2000, end: 2009}, {start: 2010, end: 2019}] AS row
WITH row.start AS startDecade, row.end AS endDecade
MATCH (movie:Movie)
WHERE movie.released >= startDecade and movie.released <= endDecade
RETURN startDecade + "-" + endDecade as years, count(movie)
ORDER BY years
----
[source,bash]
----
==> +----------------------------+
==> | years | count(movie) |
==> +----------------------------+
==> | "1970-1979" | 2 |
==> | "1980-1989" | 2 |
==> | "1990-1999" | 21 |
==> | "2000-2009" | 13 |
==> | "2010-2019" | 1 |
==> +----------------------------+
==> 5 rows
----
https://twitter.com/apcj[Alistair] pointed out that we can simplify this even further by using the http://docs.neo4j.org/chunked/stable/syntax-collections.html#_collections_in_general[RANGE] function:
[source,cypher]
----
UNWIND range(1970,2010,10) as startDecade
WITH startDecade, startDecade + 9 as endDecade
MATCH (movie:Movie)
WHERE movie.released >= startDecade and movie.released <= endDecade
RETURN startDecade + "-" + endDecade as years, count(movie)
ORDER BY years
----
And here's a http://gist.neo4j.org/?84dd2c6729b08f674b3b[graph gist] for you to play with. | null | null | [
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] | neo4j-cypher-finding-movies-by-decade | https://markhneedham.com/blog/2014/06/28/neo4j-cypher-finding-movies-by-decade |
false | 2014-06-17 23:41:35 | Neo4j: LOAD CSV - Handling conditionals | [
"neo4j",
"cypher"
] | [
"neo4j"
] | While http://worldcup.neo4j.org/extending-the-world-cup-graph-domain-model/[building up the Neo4j World Cup Graph] I've been making use of the +++<cite>+++http://neo4j.com/blog/neo4j-2-1-graph-etl[LOAD CSV]+++</cite>+++ function and I frequently found myself needing to do different things depending on the value in one of the columns.
For example I have https://raw.githubusercontent.com/mneedham/neo4j-worldcup/master/data/import/events.csv[one CSV file] which contains the different events that can happen in a football match:
[source,bash]
----
match_id,player,player_id,time,type
"1012","Antonin Panenka","174835",21,"penalty"
"1012","Faisal Al Dakhil","2204",57,"goal"
"102","Roger Milla","79318",106,"goal"
"102","Roger Milla","79318",108,"goal"
"102","Bernardo Redin","44555",115,"goal"
"102","Andre Kana-biyik","174649",44,"yellow"
----
If the +++<cite>+++type+++</cite>+++ is 'penalty', 'owngoal' or 'goal' then I want to create a SCORED_GOAL relationship whereas if it's 'yellow', 'yellowred' or 'red' then I want to create a RECEIVED_CARD relationship instead.
I learnt - from reading https://github.com/cleishm/opendisclosure/blob/master/neo4j/import.cyp#L31[a cypher script written by Chris Leishman] - that we can make FOREACH mimic a conditional by creating a collection with one item in to represent 'true' and an empty collection to represent 'false'.
In this case we'd end up with something like this to handle the case where a row represents a goal:
[source,cypher]
----
LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/mneedham/neo4j-worldcup/master/data/import/events.csv" AS csvLine
// removed for conciseness
// goals
FOREACH(n IN (CASE WHEN csvLine.type IN ["penalty", "goal", "owngoal"] THEN [1] else [] END) |
FOREACH(t IN CASE WHEN team = home THEN [home] ELSE [away] END |
MERGE (stats)-[:SCORED_GOAL]->(penalty:Goal {time: csvLine.time, type: csvLine.type})
)
)
----
And equally when we want to process a row that represents a card we'd have this:
[source,cypher]
----
// cards
FOREACH(n IN (CASE WHEN csvLine.type IN ["yellow", "red", "yellowred"] THEN [1] else [] END) |
FOREACH(t IN CASE WHEN team = home THEN [home] ELSE [away] END |
MERGE (stats)-[:RECEIVED_CARD]->(card {time: csvLine.time, type: csvLine.type})
)
)
----
And if we put everything together we get this:
[source,cypher]
----
USING PERIODIC COMMIT 1000
LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/mneedham/neo4j-worldcup/master/data/import/events.csv" AS csvLine
MATCH (home)<-[:HOME_TEAM]-(match:Match {id: csvLine.match_id})-[:AWAY_TEAM]->(away)
MATCH (player:Player {id: csvLine.player_id})-[:IN_SQUAD]->(squad)<-[:NAMED_SQUAD]-(team)
MATCH (player)-[:STARTED|:SUBSTITUTE]->(stats)-[:IN_MATCH]->(match)
// goals
FOREACH(n IN (CASE WHEN csvLine.type IN ["penalty", "goal", "owngoal"] THEN [1] else [] END) |
FOREACH(t IN CASE WHEN team = home THEN [home] ELSE [away] END |
MERGE (stats)-[:SCORED_GOAL]->(penalty:Goal {time: csvLine.time, type: csvLine.type})
)
)
// cards
FOREACH(n IN (CASE WHEN csvLine.type IN ["yellow", "red", "yellowred"] THEN [1] else [] END) |
FOREACH(t IN CASE WHEN team = home THEN [home] ELSE [away] END |
MERGE (stats)-[:RECEIVED_CARD]->(card {time: csvLine.time, type: csvLine.type})
)
)
;
----
You can have a look at the https://github.com/mneedham/neo4j-worldcup/blob/master/data/import/loadEvents.cyp[code on github] or https://github.com/mneedham/neo4j-worldcup#importing-into-neo4j[follow the instructions to get all the World Cup graph into your own local Neo4j].
Feedback welcome as always. | null | null | [
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] | neo4j-load-csv-handling-conditionals | https://markhneedham.com/blog/2014/06/17/neo4j-load-csv-handling-conditionals |
false | 2014-06-30 21:44:14 | R: Order by data frame column and take top 10 rows | [
"r-2"
] | [
"R"
] | I've been doing some http://www.markhneedham.com/blog/2014/06/30/neo4jr-grouping-meetup-members-by-join-timestamp/[ad-hoc analysis of the Neo4j London meetup group using R and Neo4j] and having worked out how to group by certain keys the next step was to order the rows of the data frame.
I wanted to drill into the days on which people join the group and see whether they join it at a specific time of day. My feeling was that most people would join on a Monday morning.
The first step was to run the query using https://github.com/nicolewhite/Rneo4j[RNeo4j] and then group by day and http://stat.ethz.ch/R-manual/R-devel/library/base/html/as.POSIXlt.html[hour]:
[source,r]
----
library(Rneo4j)
query = "MATCH (:Person)-[:HAS_MEETUP_PROFILE]->()-[:HAS_MEMBERSHIP]->(membership)-[:OF_GROUP]->(g:Group {name: \"Neo4j - London User Group\"})
RETURN membership.joined AS joinDate"
timestampToDate <- function(x) as.POSIXct(x / 1000, origin="1970-01-01")
meetupMembers = cypher(graph, query)
meetupMembers$joined <- timestampToDate(meetupMembers$joinDate)
groupBy = function(dates, format) {
dd = aggregate(dates, by= list(format(dates, format)), function(x) length(x))
colnames(dd) = c("key", "count")
dd
}
byDayTime = groupBy(meetupMembers$joined, "%A %H:00")
----
This returned quite a few rows so we'll just display a subset of them:
[source,r]
----
> byDayTime[12:25,]
key count
12 Friday 14:00 12
13 Friday 15:00 8
14 Friday 16:00 11
15 Friday 17:00 10
16 Friday 18:00 3
17 Friday 19:00 1
18 Friday 20:00 3
19 Friday 21:00 4
20 Friday 22:00 7
21 Friday 23:00 2
22 Monday 00:00 3
23 Monday 01:00 1
24 Monday 03:00 1
25 Monday 05:00 3
----
The next step was to order by the 'count' column which wasn't too difficult:
[source,r]
----
> byDayTime[order(byDayTime$count),][1:10,]
key count
2 Friday 03:00 1
3 Friday 04:00 1
4 Friday 05:00 1
5 Friday 07:00 1
17 Friday 19:00 1
23 Monday 01:00 1
24 Monday 03:00 1
46 Saturday 03:00 1
66 Sunday 06:00 1
67 Sunday 07:00 1
----
If we run the order function on its own we'll see that it returns the order in which the current rows in the data frame should appear:
[source,r]
----
> order(byDayTime$count)
[1] 2 3 4 5 17 23 24 46 66 67 109 128 129 1 21 44 47 48 81 86 87 88 108 130 16 18 22 25 45 53 64 71 75 107 19 26 49 51 55 56 58 59 61
[44] 65 68 77 79 85 106 110 143 50 52 54 82 84 101 127 146 27 57 60 62 63 69 70 73 99 103 126 145 6 20 76 83 89 105 122 131 144 7 13 40 43 72 80
[87] 102 39 78 100 132 147 15 94 121 123 142 14 42 74 104 137 140 12 38 92 93 111 124 8 9 11 90 96 125 139 10 32 34 36 95 97 98 28 135 136 33 35 112
[130] 113 116 134 91 141 41 115 120 133 37 119 138 31 117 118 30 114 29
----
The first 4 rows in our sorted data frame will be rows 2-5 from the initial data frame, which are:
[source,r]
----
key count
2 Friday 03:00 1
3 Friday 04:00 1
4 Friday 05:00 1
5 Friday 07:00 1
----
So that makes sense! In our case we want to http://www.statmethods.net/management/sorting.html[sort in descending order] which we can do by prefixing the sorting variable with a minus sign:
[source,r]
----
> byDayTime[order(-byDayTime$count),][1:10,]
key count
29 Monday 09:00 34
30 Monday 10:00 28
114 Tuesday 11:00 28
31 Monday 11:00 27
117 Tuesday 14:00 27
118 Tuesday 15:00 27
138 Wednesday 14:00 23
119 Tuesday 16:00 22
37 Monday 17:00 21
115 Tuesday 12:00 20
----
As expected Monday morning makes a strong showing although Tuesday afternoon is also popular which is unexpected. We'll need to do some more investigation to figure out what's going on there. | null | null | [
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] | r-order-by-data-frame-column-and-take-top-10-rows | https://markhneedham.com/blog/2014/06/30/r-order-by-data-frame-column-and-take-top-10-rows |
false | 2014-06-30 22:47:44 | R: Aggregate by different functions and join results into one data frame | [
"r-2"
] | [
"R"
] | In continuing http://www.markhneedham.com/blog/2014/06/30/neo4jr-grouping-meetup-members-by-join-timestamp/[my] http://www.markhneedham.com/blog/2014/06/30/r-order-by-data-frame-column-and-take-top-10-rows/[analysis] of the London Neo4j meetup group using R I wanted to see which days of the week we organise meetups and how many people RSVP affirmatively by the day.
I started out with this query which returns each event and the number of 'yes' RSVPS:
[source,r]
----
library(Rneo4j)
timestampToDate <- function(x) as.POSIXct(x / 1000, origin="1970-01-01")
query = "MATCH (g:Group {name: \"Neo4j - London User Group\"})-[:HOSTED_EVENT]->(event)<-[:TO]-({response: 'yes'})<-[:RSVPD]-()
WHERE (event.time + event.utc_offset) < timestamp()
RETURN event.time + event.utc_offset AS eventTime, COUNT(*) AS rsvps"
events = cypher(graph, query)
events$datetime <- timestampToDate(events$eventTime)
----
[source,r]
----
eventTime rsvps datetime
1 1.314815e+12 3 2011-08-31 19:30:00
2 1.337798e+12 13 2012-05-23 19:30:00
3 1.383070e+12 29 2013-10-29 18:00:00
4 1.362474e+12 5 2013-03-05 09:00:00
5 1.369852e+12 66 2013-05-29 19:30:00
6 1.385572e+12 67 2013-11-27 17:00:00
7 1.392142e+12 35 2014-02-11 18:00:00
8 1.364321e+12 23 2013-03-26 18:00:00
9 1.372183e+12 22 2013-06-25 19:00:00
10 1.401300e+12 60 2014-05-28 19:00:00
----
I wanted to get a data frame which had these columns:
[source,bash]
----
Day of Week | RSVPs | Number of Events
----
Getting the number of events for a given day was quite easy as I could use the groupBy function I wrote last time:
[source,r]
----
groupBy = function(dates, format) {
dd = aggregate(dates, by=list(format(dates, format)), function(x) length(x))
colnames(dd) = c("key", "count")
dd
}
> groupBy(events$datetime, "%A")
key count
1 Thursday 9
2 Tuesday 24
3 Wednesday 35
----
The next step is to get the sum of RSVPs by the day which we can get with the following code:
[source,r]
----
dd = aggregate(events$rsvps, by=list(format(events$datetime, "%A")), FUN=sum)
colnames(dd) = c("key", "count")
----
The difference between this and our previous use of the aggregate function is that we're passing in the number of RSVPs for each event and then grouping by the day and http://stackoverflow.com/questions/1660124/how-to-group-columns-by-sum-in-r[summing up the values for each day] rather than counting how many occurrences there are.
If we evaluate 'dd' we get the following:
[source,r]
----
> dd
key count
1 Thursday 194
2 Tuesday 740
3 Wednesday 1467
----
We now have two data tables with a very similar shape and it turns out there's a function called http://stat.ethz.ch/R-manual/R-devel/library/base/html/merge.html[merge] which makes it very easy to convert these two data frames into a single one:
[source,r]
----
x = merge(groupBy(events$datetime, "%A"), dd, by = "key")
colnames(x) = c("day", "events", "rsvps")
----
[source,r]
----
> x
day events rsvps
1 Thursday 9 194
2 Tuesday 24 740
3 Wednesday 35 1467
----
We could now choose to order our new data frame by number of events descending:
[source,r]
----
> x[order(-x$events),]
day events rsvps
3 Wednesday 35 1467
2 Tuesday 24 740
1 Thursday 9 194
----
We might also add an extra column to calculate the average number of RSVPs per day:
[source,r]
----
> x$rsvpsPerEvent = x$rsvps / x$events
> x
day events rsvps rsvpsPerEvent
1 Thursday 9 194 21.55556
2 Tuesday 24 740 30.83333
3 Wednesday 35 1467 41.91429
----
I'm still getting the hang of it but already it seems like the combination of https://github.com/nicolewhite/Rneo4j[R and Neo4j] allows us to quickly get insights into our data and I've barely scratched the surface! | null | null | [
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] | r-aggregate-by-different-functions-and-join-results-into-one-data-frame | https://markhneedham.com/blog/2014/06/30/r-aggregate-by-different-functions-and-join-results-into-one-data-frame |
false | 2014-06-30 00:06:54 | Neo4j/R: Grouping meetup members by join timestamp | [
"neo4j",
"r-2"
] | [
"neo4j",
"R"
] | I wanted to do some ad-hoc analysis on the join date of members of the http://www.meetup.com/graphdb-london/[Neo4j London meetup group] and since cypher doesn't yet have functions for dealings with dates I thought I'd give R a try.
I started off by executing a cypher query which returned the join timestamp of all the group members using Nicole White's https://github.com/nicolewhite/Rneo4j[RNeo4j] package:
[source,r]
----
> library(Rneo4j)
> query = "match (:Person)-[:HAS_MEETUP_PROFILE]->()-[:HAS_MEMBERSHIP]->(membership)-[:OF_GROUP]->(g:Group {name: \"Neo4j - London User Group\"})
RETURN membership.joined AS joinDate"
> meetupMembers = cypher(graph, query)
> meetupMembers[1:5,]
[1] 1.389107e+12 1.376572e+12 1.379491e+12 1.349454e+12 1.383127e+12
----
I realised that if I was going to do any date manipulation I'd need to translate the timestamp into an R friendly format so I wrote the following function to help me do that:
[source,r]
----
> timestampToDate <- function(x) as.POSIXct(x / 1000, origin="1970-01-01")
----
I added another column to the data frame with this date representation:
[source,r]
----
> meetupMembers$joined <- timestampToDate(meetupMembers$joinDate)
> meetupMembers[1:5,]
joinDate joined
1 1.389107e+12 2014-01-07 15:08:40
2 1.376572e+12 2013-08-15 14:13:40
3 1.379491e+12 2013-09-18 08:55:11
4 1.349454e+12 2012-10-05 17:28:04
5 1.383127e+12 2013-10-30 09:59:03
----
Next I wanted to group those timestamps by the combination of month + year for which the http://www.statmethods.net/management/aggregate.html[aggregate] and http://www.statmethods.net/input/dates.html[format] functions came in handy:
[source,r]
----
> dd = aggregate(meetupMembers$joined, by=list(format(meetupMembers$joined, "%m-%Y")), function(x) length(x))
> colnames(dd) = c("month", "count")
> dd
month count
1 01-2012 4
2 01-2013 52
3 01-2014 88
4 02-2012 7
5 02-2013 52
6 02-2014 91
7 03-2012 12
8 03-2013 23
9 03-2014 93
10 04-2012 3
11 04-2013 34
12 04-2014 119
13 05-2012 9
14 05-2013 69
15 05-2014 102
16 06-2011 14
17 06-2012 5
18 06-2013 39
19 06-2014 114
20 07-2011 4
21 07-2012 16
22 07-2013 20
23 08-2011 2
24 08-2012 34
25 08-2013 50
26 09-2012 14
27 09-2013 52
28 10-2011 2
29 10-2012 29
30 10-2013 42
31 11-2011 2
32 11-2012 31
33 11-2013 34
34 12-2012 7
35 12-2013 19
----
I wanted to be able to group by different date formats so I created the following function to make life easier:
[source,r]
----
groupBy = function(dates, format) {
dd = aggregate(dates, by= list(format(dates, format)), function(x) length(x))
colnames(dd) = c("key", "count")
dd
}
----
Now we can find the join dates grouped by year:
[source,r]
----
> groupBy(meetupMembers$joined, "%Y")
key count
1 2011 24
2 2012 171
3 2013 486
4 2014 607
----
or by day:
[source,r]
----
> groupBy(meetupMembers$joined, "%A")
key count
1 Friday 135
2 Monday 287
3 Saturday 80
4 Sunday 102
5 Thursday 187
6 Tuesday 286
7 Wednesday 211
----
or by month:
[source,r]
----
> groupBy(meetupMembers$joined, "%m")
key count
1 01 144
2 02 150
3 03 128
4 04 156
5 05 180
6 06 172
7 07 40
8 08 86
9 09 66
10 10 73
11 11 67
12 12 26
----
I found the 'by day' grouping interesting as I had the impression that the huge majority of people joined meetup groups on a Monday but the difference between Monday and Tuesday isn't significant. 60% of the joins happen between Monday and Wednesday.
The 'by month' grouping is a bit skewed by the fact we're only half way into 2014 and there have been a lot more people joining this year than in previous years.
If we https://stat.ethz.ch/pipermail/r-help/2012-July/319446.html[exclude this year] then the spread is more uniform with a slight dip in December:
[source,r]
----
> groupBy(meetupMembers$joined[format(meetupMembers$joined, "%Y") != 2014], "%m")
key count
1 01 56
2 02 59
3 03 35
4 04 37
5 05 78
6 06 58
7 07 40
8 08 86
9 09 66
10 10 73
11 11 67
12 12 26
----
Next up I think I need to get some charts going on and perhaps compare the distributions of join dates of various London meetup groups against each other.
I'm an absolute R newbie so if anything I've done is stupid and can be done better please let me know. | null | null | [
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] | neo4jr-grouping-meetup-members-by-join-timestamp | https://markhneedham.com/blog/2014/06/30/neo4jr-grouping-meetup-members-by-join-timestamp |
false | 2014-06-08 01:34:03 | Ruby: Regex - Matching the Trademark ™ character | [
"ruby"
] | [
"Ruby"
] | I've been http://gist.neo4j.org/?6e24a3bb09766e62b0b1[playing around with some World Cup data] and while cleaning up the data I wanted to strip out the year and host country for a world cup.
I started with a string like this which I was reading from a file:
[source,text]
----
1930 FIFA World Cup Uruguay ™
----
And I wanted to be able to extract just the 'Uruguay' bit without getting the trademark or the space preceding it. I initially tried the following to match all parts of the line and extract my bit:
[source,ruby]
----
p text.match(/\d{4} FIFA World Cup (.*?) ™/)[1]
----
Unfortunately that doesn't actually compile:
[source,text]
----
tm.rb:4: syntax error, unexpected $end, expecting ')'
p text.match(/\d{4} FIFA World Cup (.*?) ™/)[1]
^
----
I was initially able to work around the problem by http://books.google.co.uk/books?id=0Msuh5Vq-uYC&pg=PT102&lpg=PT102&dq=ruby+regex+trademark+character&source=bl&ots=fIXwQaJOaK&sig=hH8mmjxhHGe8iyvXT0CakRv8Ods&hl=en&sa=X&ei=_1aTU_aGGcWcyAScuYCYDQ&ved=0CCYQ6AEwAA#v=onepage&q=ruby%20regex%20trademark%20character&f=false[matching the unicode code point] instead:
[source,text]
----
p text.match(/\d{4} FIFA World Cup (.*?) \u2122/)[1]
----
While working on this blog post I also remembered that you can http://stackoverflow.com/questions/1739836/invalid-multibyte-char-us-ascii-with-rails-and-ruby-1-9[specify the character set of your Ruby file] and by default it's ASCII which would explain why it doesn't like the ™ character.
If we add the following line at the top of the file then we can happily use the ™ character in our regex:
[source,ruby]
----
# encoding: utf-8
# ...
p text.match(/\d{4} FIFA World Cup (.*?) ™/)[1]
# returns "Uruguay"
----
This post therefore ends up being more of a reminder for future Mark when he comes across this problem again having forgotten about Ruby character sets! | null | null | [
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] | ruby-regex-matching-the-trademark-character | https://markhneedham.com/blog/2014/06/08/ruby-regex-matching-the-trademark-character |
false | 2014-12-04 06:31:02 | R: Applying a function to every row of a data frame | [
"r-2",
"rstats"
] | [
"R"
] | In my continued exploration of London's meetups I wanted to calculate the distance from meetup venues to a centre point in London.
I've created https://gist.github.com/mneedham/7e926a213bf76febf5ed[a gist containing the coordinates of some of the venues that host NoSQL meetups in London town] if you want to follow along:
[source,r]
----
library(dplyr)
# https://gist.github.com/mneedham/7e926a213bf76febf5ed
venues = read.csv("/tmp/venues.csv")
venues %>% head()
## venue lat lon
## 1 Skills Matter 51.52482 -0.099109
## 2 Skinkers 51.50492 -0.083870
## 3 Theodore Bullfrog 51.50878 -0.123749
## 4 The Skills Matter eXchange 51.52452 -0.099231
## 5 The Guardian 51.53373 -0.122340
## 6 White Bear Yard 51.52227 -0.109804
----
Now to do the calculation. I've chosen the Centre Point building in Tottenham Court Road as our centre point. We can use the +++<cite>+++distHaversine+++</cite>+++ function in the +++<cite>+++geosphere+++</cite>+++ library allows us to do the calculation:
[source,r]
----
options("scipen"=100, "digits"=4)
library(geosphere)
centre = c(-0.129581, 51.516578)
aVenue = venues %>% slice(1)
aVenue
## venue lat lon
## 1 Skills Matter 51.52 -0.09911
----
Now we can calculate the distance from Skillsmatter to our centre point:</pr> ~~~r distHaversine(c(aVenue$lon, aVenue$lat), centre) ## [1] 2302 ~~~
That works pretty well so now we want to apply it to every row in the venues data frame and add an extra column containing that value.
This was my first attempt\... ~~~r venues %>% mutate(distHaversine(c(lon,lat),centre)) ## Error in .pointsToMatrix(p1): Wrong length for a vector, should be 2 ~~~
\...which didn't work quite as I'd imagined!
I eventually found my way to the by function which allows you to 'apply a function to a data frame split by factors'. In this case I wouldn't be grouping rows by a factor - I'd apply the function to each row separately.
I wired everything up like so: ~~~r distanceFromCentre = by(venues, 1:nrow(venues), function(row) { distHaversine(c(row$lon, row$lat), centre) }) distanceFromCentre %>% head() ## 1:nrow(venues) ## 1 2 3 4 5 6 ## 2301.6 3422.6 957.5 2280.6 1974.1 1509.5 ~~~ We can now add the distances to our venues data frame: ~~~r venuesWithCentre = venues %>% mutate(distanceFromCentre = by(venues, 1:nrow(venues), function(row) { distHaversine(c(row$lon, row$lat), centre) })) venuesWithCentre %>% head() ## venue lat lon distanceFromCentre ## 1 Skills Matter 51.52 -0.09911 2301.6 ## 2 Skinkers 51.50 -0.08387 3422.6 ## 3 Theodore Bullfrog 51.51 -0.12375 957.5 ## 4 The Skills Matter eXchange 51.52 -0.09923 2280.6 ## 5 The Guardian 51.53 -0.12234 1974.1 ## 6 White Bear Yard 51.52 -0.10980 1509.5 ~~~
Et voila! | null | null | [
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] | r-applying-a-function-to-every-row-of-a-data-frame | https://markhneedham.com/blog/2014/12/04/r-applying-a-function-to-every-row-of-a-data-frame |
false | 2014-12-11 19:03:06 | R: data.table/dplyr/lubridate - Error in wday(date, label = TRUE, abbr = FALSE) : unused arguments (label = TRUE, abbr = FALSE) | [
"r-2",
"rstats"
] | [
"R"
] | I spent a couple of hours playing around with http://cran.r-project.org/web/packages/data.table/index.html[data.table] this evening and tried changing some code written using a data frame to use a data table instead.
I started off by building a data frame which contains all the weekends between 2010 and 2015\...
[source,r]
----
> library(lubridate)
> library(dplyr)
> dates = data.frame(date = seq( dmy("01-01-2010"), to=dmy("01-01-2015"), by="day" ))
> dates = dates %>% filter(wday(date, label = TRUE, abbr = FALSE) %in% c("Saturday", "Sunday"))
----
\...which works fine:
[source,r]
----
> dates %>% head()
date
1: 2010-01-02
2: 2010-01-03
3: 2010-01-09
4: 2010-01-10
5: 2010-01-16
6: 2010-01-17
----
I then tried to change the code to use a data table instead which led to the following error:
[source,r]
----
> library(data.table)
> dates = data.table(date = seq( dmy("01-01-2010"), to=dmy("01-01-2015"), by="day" ))
> dates = dates %>% filter(wday(date, label = TRUE, abbr = FALSE) %in% c("Saturday", "Sunday"))
Error in wday(date, label = TRUE, abbr = FALSE) :
unused arguments (label = TRUE, abbr = FALSE)
----
I wasn't sure what was going on so I went back to the data frame version to check if that still worked\...
[source,r]
----
> dates = data.frame(date = seq( dmy("01-01-2010"), to=dmy("01-01-2015"), by="day" ))
> dates = dates %>% filter(wday(date, label = TRUE, abbr = FALSE) %in% c("Saturday", "Sunday"))
Error in wday(c(1262304000, 1262390400, 1262476800, 1262563200, 1262649600, :
unused arguments (label = TRUE, abbr = FALSE)
----
\...except it now didn't work either! I decided to check what +++<cite>+++wday+++</cite>+++ was referring to\...
____
Help on topic 'wday' was found in the following packages: Integer based date class (in package data.table in library /Library/Frameworks/R.framework/Versions/3.1/Resources/library) Get/set days component of a date-time. (in package lubridate in library /Library/Frameworks/R.framework/Versions/3.1/Resources/library)
____
\...and realised that data.table has its own wday function - I'd been caught out by R's global scoping of all the things!
We can probably work around that by the order in which we require the various libraries but for now I'm just prefixing the call to wday and all is well:
[source,r]
----
dates = dates %>% filter(lubridate::wday(date, label = TRUE, abbr = FALSE) %in% c("Saturday", "Sunday"))
---- | null | null | [
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] | r-data-tabledplyrlubridate-error-in-wdaydate-label-true-abbr-false-unused-arguments-label-true-abbr-false | https://markhneedham.com/blog/2014/12/11/r-data-tabledplyrlubridate-error-in-wdaydate-label-true-abbr-false-unused-arguments-label-true-abbr-false |
false | 2014-12-28 04:28:25 | Neo4j 2.1.6 - Cypher: FOREACH slowness | [
"neo4j",
"cypher"
] | [
"neo4j"
] | A common problem that people have when using Neo4j for social network applications is updating a person with their newly imported friends.
We'll have an array of friends that we want to connect to a single Person node. Assuming the following schema...
[source,cypher]
----
$ schema
Indexes
ON :Person(id) ONLINE
No constraints
----
...a simplified version would look like this:
[source,cypher]
----
WITH range (2,1002) AS friends
MERGE (p:Person {id: 1})
FOREACH(f IN friends |
MERGE (friend:Person {id: f})
MERGE (friend)-[:FRIENDS]->p);
----
If we execute that on an empty database we'll see something like this:
[source,text]
----
+-------------------+
| No data returned. |
+-------------------+
Nodes created: 1002
Relationships created: 1001
Properties set: 1002
Labels added: 1002
19173 ms
----
This took much longer than we'd expect so let's have a look at the PROFILE output:
[source,text]
----
EmptyResult
|
+UpdateGraph(0)
|
+Eager
|
+UpdateGraph(1)
|
+Extract
|
+Null
+----------------+------+---------+-------------+--------------------------------------+
| Operator | Rows | DbHits | Identifiers | Other |
+----------------+------+---------+-------------+--------------------------------------+
| EmptyResult | 0 | 0 | | |
| UpdateGraph(0) | 1 | 3015012 | | Foreach |
| Eager | 1 | 0 | | |
| UpdateGraph(1) | 1 | 5 | p, p | MergeNode; { AUTOINT2}; :Person(id) |
| Extract | 1 | 0 | | friends |
| Null | ? | ? | | |
+----------------+------+---------+-------------+--------------------------------------+
----
The <cite>DbHits</cite> value on the 2nd row seems suspiciously high suggesting that FOREACH might not be making use of the <cite>Person#id</cite> index and is instead scanning all <cite>Person</cite> nodes each time.
I'm not sure how to drill into that further but an alternative approach is to try out the same query but using UNWIND instead and checking the profile output of that:
[source,cypher]
WITH range (2,1002) AS friends
MERGE (p:Person {id: 1})
WITH p, friends
UNWIND friends AS f
MERGE (friend:Person {id: f})
MERGE (friend)-[:FRIENDS]->p;
----
[source,text]
----
+-------------------+
| No data returned. |
+-------------------+
Nodes created: 1002
Relationships created: 1001
Properties set: 1002
Labels added: 1002
343 ms
----
[source,text]
----
EmptyResult
|
+UpdateGraph(0)
|
+Eager(0)
|
+UpdateGraph(1)
|
+UNWIND
|
+Eager(1)
|
+UpdateGraph(2)
|
+Extract
|
+Null
+----------------+------+--------+-------------------------+--------------------------------------+
| Operator | Rows | DbHits | Identifiers | Other |
+----------------+------+--------+-------------------------+--------------------------------------+
| EmptyResult | 0 | 0 | | |
| UpdateGraph(0) | 1001 | 0 | friend, p, UNNAMED136 | MergePattern |
| Eager(0) | 1001 | 0 | | |
| UpdateGraph(1) | 1001 | 5005 | friend, friend | MergeNode; f; :Person(id) |
| UNWIND | 1001 | 0 | | |
| Eager(1) | 1 | 0 | | |
| UpdateGraph(2) | 1 | 5 | p, p | MergeNode; { AUTOINT2}; :Person(id) |
| Extract | 1 | 0 | | friends |
| Null | ? | ? | | |
+----------------+------+--------+-------------------------+--------------------------------------+
----
That's much quicker and doesn't touch as many nodes as FOREACH was. I expect the index issue will be sorted out in future but until then UNWIND is our friend. | null | null | [
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] | neo4j-2-1-6-cypher-foreach-slowness | https://markhneedham.com/blog/2014/12/28/neo4j-2-1-6-cypher-foreach-slowness |
false | 2014-12-28 21:55:23 | R: Featuring engineering for a linear model | [
"r-2"
] | [
"R"
] | I previously wrote about http://www.markhneedham.com/blog/2014/09/30/r-a-first-attempt-at-linear-regression/[a linear model] I created to predict how many people would RSVP 'yes' to a meetup event and having not found much correlation between any of my independent variables and RSVPs was a bit stuck.
As luck would have it I bumped into https://twitter.com/tonkouts[Antonios] at a meetup a month ago and he offered to take a look at what I'd tried so far and give me some tips on how to progress.
The first thing he pointed out is that that all my features were related to date/time and that I should try and generate some other features. He suggested I start with the following:
* info about organisers (quantify popularity of organisers, how many people work for them)
* info about the venue (how many people fit there, how far it is from the centre of the city)
* number of tweets for the event, during X days before the event
I'd read a lot on http://www.kaggle.com/c/acquire-valued-shoppers-challenge/forums/t/7688/feature-engineering-and-beat-the-benchmark-0-59347[Kaggle] http://www.quora.com/What-do-top-Kaggle-competitors-focus-on[forums] about how feature engineering was the most important part of building statistical models but it didn't click what that meant until Antonios pointed it out.
The first thing I decided to do was bring in the data for all London's NoSQL meetups rather than just the Neo4j one to give myself a bit more data to work with.
== Group Membership
Having done that, it seemed from visual inspection that the meetup groups with the most members (i.e. Data Science London, Big Data London) seemed to get the biggest turnouts.
I thought it'd be interesting to see what the correlation was between group membership and RSVPs so this was the first new feature I added.
I generated this feature by a combination of a Neo4j query and R code which resulted in https://gist.github.com/mneedham/6be0c807674f229ad34c[this data frame as CSV file].
We can quickly preview it to see some of the events and the group membership at that time:
[source,r]
----
> df = read.csv("/tmp/membersWithGroupCounts.csv")
> df$eventTime = as.POSIXct(df$eventTime)
> df %>% sample_n(10) %>% select(event.name, g.name, eventTime, groupMembers, rsvps)
event.name g.name eventTime groupMembers rsvps
23 Scoring Models, Apache Drill for querying structured & unstructured data Data Science London 2014-09-18 18:30:00 3466 159
421 London Office Hours London MongoDB User Group 2012-08-22 17:00:00 468 6
304 MongoDB University Study Group London Meet up London MongoDB User Group 2014-07-16 17:00:00 1256 23
43 December Meetup London ElasticSearch User Group 2014-12-10 18:30:00 721 126
222 Intro to Graphs Neo4j - London User Group 2014-09-03 18:30:00 1453 39
207 Intro to Machine Learning with Scikit-Learn Women in Data 2014-11-11 18:15:00 574 41
168 NoSQL panel and LevelDB + Node.js London NoSQL 2014-04-15 18:30:00 183 51
443 London Office Hours London MongoDB User Group 2012-11-29 17:00:00 590 3
79 Apache Cassandra 1.2 with Jonathan Ellis Cassandra London 2013-03-06 19:00:00 399 95
362 Span conference Span: scalable and distributed computing 2014-10-28 09:00:00 67 13
----
One thing I found difficult was finding features specific to an event - I'm not sure how much that matters. I generated features for the venue or group much more easily.
First let's see if there's actually any correlation between these two variables by plotting them:
[source,r]
----
ggplot(aes(x = groupMembers, y = rsvps), data = df) +
geom_point()
----
image::{{<siteurl>}}/uploads/2014/12/2014-12-28_21-02-21.png[2014 12 28 21 02 21,515]
It looks like there's a positive correlation between these two variables but let's create a single variable linear model to see how much of the variation is explained:
[source,r]
----
> fit = lm(rsvps ~ groupMembers, data = df)
> fit$coef
(Intercept) groupMembers
20.03579637 0.05382738
----
Our linear model equation is therefore:
____
rsvps = 20.03579637 + 0.05382738(groupMembers)
____
Let's see how well correlated our predicted RSVPs and actual RSVPs are:
[source,r]
----
> df$predictedRSVPs = predict(fit, df)
> with(df, cor(rsvps, predictedRSVPs))
[1] 0.6263096
----
Not too bad! There is quite a strong correlation between these variables although it's not perfect.
== Hours into the day
In my first model I'd treated time as a categorical variable but Antonios pointed out that it's often easier to understand the relationship between variables if they're both continuos so I transformed the event time like so:
[source,r]
----
df$hoursIntoDay = as.numeric(df$eventTime - trunc(df$eventTime, "day"), units="hours")
----
Let's see how that plots against the RSVP count:
[source,r]
----
ggplot(aes(x = hoursIntoDay, y = rsvps), data = df) +
geom_point()
----
image::{{<siteurl>}}/uploads/2014/12/2014-12-28_21-27-48.png[2014 12 28 21 27 48,510]
It's a bit more difficult to see a trend here as there are quite discrete times at which events happen and the majority start at 6.30 or 7.00. Nevertheless let's build a linear model with just this variable:
[source,r]
----
> fit = lm(rsvps ~ hoursIntoDay, data = df)
> fit$coef
(Intercept) hoursIntoDay
-18.79895 4.12984
>
> df$predictedRSVPs = predict(fit, df)
> with(df, cor(rsvps, predictedRSVPs))
[1] 0.181472
----
== Distance from the centre of London
Next up I tried a feature based on the location of the venue that the events were held at. The hypothesis was that if a venue was closer to the centre of London then people would be more likely to attend.
To calculate this distance I used the +++<cite>+++distHaversine+++</cite>+++ function from the geosphere package http://www.markhneedham.com/blog/2014/12/22/r-vectorising-all-the-things/[as shown in a previous blog post].
Let's have a look at the graph for that variable:
[source,r]
----
ggplot(aes(x = distanceFromCentre, y = rsvps), data = df) +
geom_point()
----
image::{{<siteurl>}}/uploads/2014/12/2014-12-28_21-37-41.png[2014 12 28 21 37 41,512]
It's hard to tell much from this plot, mainly because a majority of the points are clustered around the 2,500 metre mark which represents Shoreditch venues. Let's plug it into a linear model and see what we come up with:
[source,r]
----
> fit = lm(rsvps ~ distanceFromCentre, data = df)
> fit$coef
(Intercept) distanceFromCentre
57.243646619 -0.001310492
>
> df$predictedRSVPs = predict(fit, df)
> with(df, cor(rsvps, predictedRSVPs))
[1] 0.02999708
----
Interestingly there's barely any correlation here which was surprising to me. I tried combining this variable in a multiple variable model with the others but it still didn't have much impact so I think we'll park this one for now.
This is as much as I've done at the moment and despite spending quite a bit of time on it I still haven't really explained very much of the variation in RSVP rates!
I have managed to identify some ways that I was able to come up with new features to try out though:
* Read what other people are doing e.g. I have some ideas for lag variables (e.g. how many people went to your previous meetup) having read about http://people.duke.edu/~rnau/regexbaseball.htm[this baseball linear model]
* Talk to other people about your model - they often have ideas you wouldn't think of being too deep into the problem.
* Look at what data you already have and try and incorporate that and see where it leads
+
</ul>
+
The next avenue I started exploring is topic modelling as I have a hypothesis that people RSVP for events based on the content of talks but I'm not sure of the best way to go about that.
+
My current thinking is to pull out some topics/terms by following the example from https://github.com/johnmyleswhite/ML_for_Hackers/blob/master/06-Regularization/chapter06.R[Chapter 6] of http://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/dp/1449303714[Machine Learning for Hackers]. | null | null | [
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] | r-featuring-engineering-for-a-linear-model | https://markhneedham.com/blog/2014/12/28/r-featuring-engineering-for-a-linear-model |
false | 2014-12-07 19:29:01 | R: String to Date or NA | [
"r-2",
"rstats"
] | [
"R"
] | I've been trying to clean up a CSV file which contains some rows with dates and some not - I only want to keep the cells which do have dates so I've been trying to work out how to do that.
My first thought was that I'd try and find a function which would convert the contents of the cell into a date if it was in date format and NA if not. I could then filter out the NA values using the +++<cite>+++http://www.statmethods.net/input/missingdata.html[is.na]+++</cite>+++ function.
I started out with the +++<cite>+++http://www.statmethods.net/input/dates.html[as.Date]+++</cite>+++ function\...
[source,r]
----
> as.Date("2014-01-01")
[1] "2014-01-01"
> as.Date("foo")
Error in charToDate(x) :
character string is not in a standard unambiguous format
----
\...but that throws an error if we have a non date value so it's not so useful in this case.
Instead we can make use of the +++<cite>+++http://stackoverflow.com/questions/14755425/what-are-the-standard-unambiguous-date-formats[strptime]+++</cite>+++ function which does exactly what we want:
[source,r]
----
> strptime("2014-01-01", "%Y-%m-%d")
[1] "2014-01-01 GMT"
> strptime("foo", "%Y-%m-%d")
[1] NA
----
We can then feed those values into +++<cite>+++is.na+++</cite>+++..
[source,r]
----
> strptime("2014-01-01", "%Y-%m-%d") %>% is.na()
[1] FALSE
> strptime("foo", "%Y-%m-%d") %>% is.na()
[1] TRUE
----
\...and we have exactly the behaviour we were looking for. | null | null | [
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] | r-string-to-date-or-na | https://markhneedham.com/blog/2014/12/07/r-string-to-date-or-na |
false | 2014-12-09 18:14:45 | R: Cleaning up and plotting Google Trends data | [
"r-2",
"rstats"
] | [
"R"
] | I recently came across an excellent article written by Stian Haklev in which he describes http://reganmian.net/blog/2014/10/14/starting-data-analysiswrangling-with-r-things-i-wish-id-been-told/[things he wishes he'd been told before starting out with R], one being to *do all data clean up in code* which I thought I'd give a try.
____
My goal is to leave the raw data completely unchanged, and do all the transformation in code, which can be rerun at any time. While I'm writing the scripts, I'm often jumping around, selectively executing individual lines or code blocks, running commands to inspect the data in the REPL (read-evaluate-print-loop, where each command is executed as soon as you type enter, in the picture above it's the pane to the right), etc. But I try to make sure that when I finish up, the script is runnable by itself.
____
I thought the http://www.google.com/trends/explore#q=neo4j[Google Trends data set] would be an interesting one to play around with as it gives you a CSV containing several different bits of data of which I'm only interested in 'interest over time'.
It's not very easy to automate the download of the CSV file so I did that bit manually and automated everything from there onwards.
The first step was to read the CSV file and explore some of the rows to see what it contained:
[source,r]
----
> library(dplyr)
> googleTrends = read.csv("/Users/markneedham/Downloads/report.csv", row.names=NULL)
> googleTrends %>% head()
## row.names Web.Search.interest..neo4j
## 1 Worldwide; 2004 - present
## 2 Interest over time
## 3 Week neo4j
## 4 2004-01-04 - 2004-01-10 0
## 5 2004-01-11 - 2004-01-17 0
## 6 2004-01-18 - 2004-01-24 0
> googleTrends %>% sample_n(10)
## row.names Web.Search.interest..neo4j
## 109 2006-01-08 - 2006-01-14 0
## 113 2006-02-05 - 2006-02-11 0
## 267 2009-01-18 - 2009-01-24 0
## 199 2007-09-30 - 2007-10-06 0
## 522 2013-12-08 - 2013-12-14 88
## 265 2009-01-04 - 2009-01-10 0
## 285 2009-05-24 - 2009-05-30 0
## 318 2010-01-10 - 2010-01-16 0
## 495 2013-06-02 - 2013-06-08 79
## 28 2004-06-20 - 2004-06-26 0
> googleTrends %>% tail()
## row.names Web.Search.interest..neo4j
## 658 neo4j example Breakout
## 659 neo4j graph database Breakout
## 660 neo4j java Breakout
## 661 neo4j node Breakout
## 662 neo4j rest Breakout
## 663 neo4j tutorial Breakout
----
We only want to keep the rows which contain (week, interest) pairs so the first thing we'll do is rename the columns:
[source,r]
----
names(googleTrends) = c("week", "score")
----
Now we want to strip out the rows which don't contain (week, interest) pairs. The easiest way to do this is to look for rows which don't contain date values in the 'week' column.
First we need to split the start and end dates in that column by using the +++<cite>+++strsplit+++</cite>+++ function.
I found it much easier to apply the function to each row individually rather than passing in a list of values so I created a dummy column with a row number in to allow me to do that (a trick https://twitter.com/tonkouts[Antonios] showed me):
[source,r]
----
> googleTrends %>%
mutate(ind = row_number()) %>%
group_by(ind) %>%
mutate(dates = strsplit(week, " - "),
start = dates[[1]][1] %>% strptime("%Y-%m-%d") %>% as.character(),
end = dates[[1]][2] %>% strptime("%Y-%m-%d") %>% as.character()) %>%
head()
## Source: local data frame [6 x 6]
## Groups: ind
##
## week score ind dates start end
## 1 Worldwide; 2004 - present 1 1 <chr[2]> NA NA
## 2 Interest over time 1 2 <chr[1]> NA NA
## 3 Week 90 3 <chr[1]> NA NA
## 4 2004-01-04 - 2004-01-10 3 4 <chr[2]> 2004-01-04 2004-01-10
## 5 2004-01-11 - 2004-01-17 3 5 <chr[2]> 2004-01-11 2004-01-17
## 6 2004-01-18 - 2004-01-24 3 6 <chr[2]> 2004-01-18 2004-01-24
----
Now we need to get rid of the rows which have an NA value for 'start' or 'end':
[source,r]
----
> googleTrends %>%
mutate(ind = row_number()) %>%
group_by(ind) %>%
mutate(dates = strsplit(week, " - "),
start = dates[[1]][1] %>% strptime("%Y-%m-%d") %>% as.character(),
end = dates[[1]][2] %>% strptime("%Y-%m-%d") %>% as.character()) %>%
filter(!is.na(start) | !is.na(end)) %>%
head()
## Source: local data frame [6 x 6]
## Groups: ind
##
## week score ind dates start end
## 1 2004-01-04 - 2004-01-10 3 4 <chr[2]> 2004-01-04 2004-01-10
## 2 2004-01-11 - 2004-01-17 3 5 <chr[2]> 2004-01-11 2004-01-17
## 3 2004-01-18 - 2004-01-24 3 6 <chr[2]> 2004-01-18 2004-01-24
## 4 2004-01-25 - 2004-01-31 3 7 <chr[2]> 2004-01-25 2004-01-31
## 5 2004-02-01 - 2004-02-07 3 8 <chr[2]> 2004-02-01 2004-02-07
## 6 2004-02-08 - 2004-02-14 3 9 <chr[2]> 2004-02-08 2004-02-14
----
Next we'll get rid of 'week', 'ind' and 'dates' as we aren't going to need those anymore:
[source,r]
----
> cleanGoogleTrends = googleTrends %>%
mutate(ind = row_number()) %>%
group_by(ind) %>%
mutate(dates = strsplit(week, " - "),
start = dates[[1]][1] %>% strptime("%Y-%m-%d") %>% as.character(),
end = dates[[1]][2] %>% strptime("%Y-%m-%d") %>% as.character()) %>%
filter(!is.na(start) | !is.na(end)) %>%
ungroup() %>%
select(-c(ind, dates, week))
> cleanGoogleTrends %>% head()
## Source: local data frame [6 x 3]
##
## score start end
## 1 3 2004-01-04 2004-01-10
## 2 3 2004-01-11 2004-01-17
## 3 3 2004-01-18 2004-01-24
## 4 3 2004-01-25 2004-01-31
## 5 3 2004-02-01 2004-02-07
## 6 3 2004-02-08 2004-02-14
> cleanGoogleTrends %>% sample_n(10)
## Source: local data frame [10 x 3]
##
## score start end
## 1 8 2010-09-26 2010-10-02
## 2 73 2013-11-17 2013-11-23
## 3 52 2012-07-01 2012-07-07
## 4 3 2005-06-19 2005-06-25
## 5 3 2004-12-12 2004-12-18
## 6 3 2009-09-06 2009-09-12
## 7 71 2014-09-14 2014-09-20
## 8 3 2004-12-26 2005-01-01
## 9 62 2013-03-03 2013-03-09
## 10 3 2006-03-19 2006-03-25
> cleanGoogleTrends %>% tail()
## Source: local data frame [6 x 3]
##
## score start end
## 1 80 2014-10-19 2014-10-25
## 2 80 2014-10-26 2014-11-01
## 3 84 2014-11-02 2014-11-08
## 4 81 2014-11-09 2014-11-15
## 5 83 2014-11-16 2014-11-22
## 6 2 2014-11-23 2014-11-29
----
Ok now we're ready to plot. This was my first attempt:
[source,r]
----
> library(ggplot2)
> ggplot(aes(x = start, y = score), data = cleanGoogleTrends) +
geom_line(size = 0.5)
## geom_path: Each group consist of only one observation. Do you need to adjust the group aesthetic?
----
image::{{<siteurl>}}/uploads/2014/12/2014-12-09_17-57-49.png[2014 12 09 17 57 49,511]
As you can see, not too successful! The first mistake I've made is not telling ggplot that the 'start' column is a date and so it can use that ordering when plotting:
[source,r]
----
> cleanGoogleTrends = cleanGoogleTrends %>% mutate(start = as.Date(start))
> ggplot(aes(x = start, y = score), data = cleanGoogleTrends) +
geom_line(size = 0.5)
----
image::{{<siteurl>}}/uploads/2014/12/2014-12-09_18-00-03.png[2014 12 09 18 00 03,510]
My next mistake is that 'score' is not being treated as a continuous variable and so we're ending up with this very strange looking chart. We can see that if we call the +++<cite>+++class+++</cite>+++ function:
[source,r]
----
> class(cleanGoogleTrends$score)
## [1] "factor"
----
Let's fix that and plot again:
[source,r]
----
> cleanGoogleTrends = cleanGoogleTrends %>% mutate(score = as.numeric(score))
> ggplot(aes(x = start, y = score), data = cleanGoogleTrends) +
geom_line(size = 0.5)
----
image::{{<siteurl>}}/uploads/2014/12/2014-12-09_18-02-39.png[2014 12 09 18 02 39,504]
That's much better but there is quite a bit of noise in the week to week scores which we can flatten a bit by plotting a http://www.markhneedham.com/blog/2014/09/13/r-calculating-rolling-or-moving-averages/[rolling mean of the last 4 weeks instead]:
[source,r]
----
> library(zoo)
> cleanGoogleTrends = cleanGoogleTrends %>%
mutate(rolling = rollmean(score, 4, fill = NA, align=c("right")),
start = as.Date(start))
> ggplot(aes(x = start, y = rolling), data = cleanGoogleTrends) +
geom_line(size = 0.5)
----
image::{{<siteurl>}}/uploads/2014/12/2014-12-09_18-05-26.png[2014 12 09 18 05 26,518]
Here's the full code if you want to reproduce:
[source,r]
----
library(dplyr)
library(zoo)
library(ggplot2)
googleTrends = read.csv("/Users/markneedham/Downloads/report.csv", row.names=NULL)
names(googleTrends) = c("week", "score")
cleanGoogleTrends = googleTrends %>%
mutate(ind = row_number()) %>%
group_by(ind) %>%
mutate(dates = strsplit(week, " - "),
start = dates[[1]][1] %>% strptime("%Y-%m-%d") %>% as.character(),
end = dates[[1]][2] %>% strptime("%Y-%m-%d") %>% as.character()) %>%
filter(!is.na(start) | !is.na(end)) %>%
ungroup() %>%
select(-c(ind, dates, week)) %>%
mutate(start = as.Date(start),
score = as.numeric(score),
rolling = rollmean(score, 4, fill = NA, align=c("right")))
ggplot(aes(x = start, y = rolling), data = cleanGoogleTrends) +
geom_line(size = 0.5)
----
My next step is to plot the Google Trends scores against my meetup data set to see if there's any interesting correlations going on.
_As an aside I made use of http://yihui.name/knitr/[knitr] while putting together this post - it works really well for checking that you've included all the steps and that it actually works!_ | null | null | [
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] | r-cleaning-up-plotting-google-trends-data | https://markhneedham.com/blog/2014/12/09/r-cleaning-up-plotting-google-trends-data |
false | 2014-12-08 19:02:46 | R: dplyr - mutate with strptime (incompatible size/wrong result size) | [
"r-2",
"rstats"
] | [
"R"
] | Having worked out http://www.markhneedham.com/blog/2014/12/07/r-string-to-date-or-na/[how to translate a string into a date or NA if it wasn't the appropriate format] the next thing I wanted to do was store the result of the transformation in my data frame.
I started off with this:
[source,r]
----
data = data.frame(x = c("2014-01-01", "2014-02-01", "foo"))
> data
x
1 2014-01-01
2 2014-02-01
3 foo
----
And when I tried to do the date translation ran into the following error:
[source,r]
----
> data %>% mutate(y = strptime(x, "%Y-%m-%d"))
Error: wrong result size (11), expected 3 or 1
----
As I understand it this error is telling us that we are trying to put a value into the data frame which represents 11 rows rather than 3 rows or 1 row.
It turns out that https://github.com/hadley/dplyr/issues/179[storing POSIXlts in a data frame isn't such a good idea!] In this case we can use the +++<cite>+++as.character+++</cite>+++ function to create a character vector which can be stored in the data frame:</a>
[source,r]
----
> data %>% mutate(y = strptime(x, "%Y-%m-%d") %>% as.character())
x y
1 2014-01-01 2014-01-01
2 2014-02-01 2014-02-01
3 foo <NA>
----
We can then get rid of the NA row by using the +++<cite>+++is.na+++</cite>+++ function:
[source,r]
----
> data %>% mutate(y = strptime(x, "%Y-%m-%d") %>% as.character()) %>% filter(!is.na(y))
x y
1 2014-01-01 2014-01-01
2 2014-02-01 2014-02-01
----
And a final tweak so that we have 100% pipelining goodness:
[source,r]
----
> data %>%
mutate(y = x %>% strptime("%Y-%m-%d") %>% as.character()) %>%
filter(!is.na(y))
x y
1 2014-01-01 2014-01-01
2 2014-02-01 2014-02-01
---- | null | null | [
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] | r-dplyr-mutate-with-strptime-incompatible-sizewrong-result-size | https://markhneedham.com/blog/2014/12/08/r-dplyr-mutate-with-strptime-incompatible-sizewrong-result-size |
false | 2014-12-13 19:58:13 | R: Numeric representation of date time | [
"r-2",
"rstats"
] | [
"R"
] | I've been playing around with date times in R recently and I wanted to derive a numeric representation for a given value to make it easier to see the correlation between time and another variable.
e.g. December 13th 2014 17:30 should return 17.5 since it's 17.5 hours since midnight.
Using the standard R libraries we would write the following code:
[source,r]
----
> december13 = as.POSIXlt("2014-12-13 17:30:00")
> as.numeric(december13 - trunc(december13, "day"), units="hours")
[1] 17.5
----
That works pretty well but https://twitter.com/tonkouts[Antonios] recently introduced me to the http://cran.r-project.org/web/packages/lubridate/index.html[lubridate] so I thought I'd give that a try as well.
The first nice thing about lubridate is that we can use the date we created earlier and call the +++<cite>+++floor_date+++</cite>+++ function rather than +++<cite>+++truncate+++</cite>+++:
[source,r]
----
> (december13 - floor_date(december13, "day"))
Time difference of 17.5 hours
----
That gives us back a +++<cite>+++difftime+++</cite>+++\...
[source,r]
----
> class((december13 - floor_date(december13, "day")))
[1] "difftime"
----
\...which we can divide by different units to get the granularity we want:
[source,r]
----
> diff = (december13 - floor_date(december13, "day"))
> diff / dhours(1)
[1] 17.5
> diff / ddays(1)
[1] 0.7291667
> diff / dminutes(1)
[1] 1050
----
Pretty neat!
lubridate also has some nice functions for creating dates/date times. e.g.
</p>
[source,r]
----
> ymd_hms("2014-12-13 17:00:00")
[1] "2014-12-13 17:00:00 UTC"
> ymd_hm("2014-12-13 17:00")
[1] "2014-12-13 17:00:00 UTC"
> ymd_h("2014-12-13 17")
[1] "2014-12-13 17:00:00 UTC"
> ymd("2014-12-13")
[1] "2014-12-13 UTC"
----
And if you want a different time zone that's pretty easy too:
[source,r]
----
> with_tz(ymd("2014-12-13"), "GMT")
[1] "2014-12-13 GMT"
---- | null | null | [
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] | r-numeric-representation-of-date-time | https://markhneedham.com/blog/2014/12/13/r-numeric-representation-of-date-time |
false | 2014-12-13 20:38:22 | R: Time to/from the weekend | [
"r-2",
"rstats"
] | [
"R"
] | In my last post I showed some examples using http://www.markhneedham.com/blog/2014/12/13/r-numeric-representation-of-date-time/[R's lubridate] package and another problem it made really easy to solve was working out how close a particular date time was to the weekend.
I wanted to write a function which would return the previous Sunday or upcoming Saturday depending on which was closer.
lubridate's +++<cite>+++floor_date+++</cite>+++ and +++<cite>+++ceiling_date+++</cite>+++ functions make this quite simple.
e.g. if we want to round the 18th December down to the beginning of the week and up to the beginning of the next week we could do the following:
[source,r]
----
> library(lubridate)
> floor_date(ymd("2014-12-18"), "week")
[1] "2014-12-14 UTC"
> ceiling_date(ymd("2014-12-18"), "week")
[1] "2014-12-21 UTC"
----
For the date in the future we actually want to grab the Saturday rather than the Sunday so we'll subtract one day from that:
[source,r]
----
> ceiling_date(ymd("2014-12-18"), "week") - days(1)
[1] "2014-12-20 UTC"
----
Now let's put that together into a function which finds the closest weekend for a given date:
[source,r]
----
findClosestWeekendDay = function(dateToLookup) {
before = floor_date(dateToLookup, "week") + hours(23) + minutes(59) + seconds(59)
after = ceiling_date(dateToLookup, "week") - days(1)
if((dateToLookup - before) < (after - dateToLookup)) {
before
} else {
after
}
}
> findClosestWeekendDay(ymd_hms("2014-12-13 13:33:29"))
[1] "2014-12-13 UTC"
> findClosestWeekendDay(ymd_hms("2014-12-14 18:33:29"))
[1] "2014-12-14 23:59:59 UTC"
> findClosestWeekendDay(ymd_hms("2014-12-15 18:33:29"))
[1] "2014-12-14 23:59:59 UTC"
> findClosestWeekendDay(ymd_hms("2014-12-17 11:33:29"))
[1] "2014-12-14 23:59:59 UTC"
> findClosestWeekendDay(ymd_hms("2014-12-17 13:33:29"))
[1] "2014-12-20 UTC"
> findClosestWeekendDay(ymd_hms("2014-12-19 13:33:29"))
[1] "2014-12-20 UTC"
----
I've set the Sunday date at 23:59:59 so that I can use this date in the next step where we want to calculate how how many hours it is from the current date to the nearest weekend.
I ended up with this function:
[source,r]
----
distanceFromWeekend = function(dateToLookup) {
before = floor_date(dateToLookup, "week") + hours(23) + minutes(59) + seconds(59)
after = ceiling_date(dateToLookup, "week") - days(1)
timeToBefore = dateToLookup - before
timeToAfter = after - dateToLookup
if(timeToBefore < 0 || timeToAfter < 0) {
0
} else {
if(timeToBefore < timeToAfter) {
timeToBefore / dhours(1)
} else {
timeToAfter / dhours(1)
}
}
}
> distanceFromWeekend(ymd_hms("2014-12-13 13:33:29"))
[1] 0
> distanceFromWeekend(ymd_hms("2014-12-14 18:33:29"))
[1] 0
> distanceFromWeekend(ymd_hms("2014-12-15 18:33:29"))
[1] 18.55833
> distanceFromWeekend(ymd_hms("2014-12-17 11:33:29"))
[1] 59.55833
> distanceFromWeekend(ymd_hms("2014-12-17 13:33:29"))
[1] 58.44194
> distanceFromWeekend(ymd_hms("2014-12-19 13:33:29"))
[1] 10.44194
----
While this works it's quite slow when you run it over a data frame which contains a lot of rows.
There must be a clever R way of doing the same thing (perhaps using matrices) which I haven't figured out yet so if you know how to speed it up do let me know. | null | null | [
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] | r-time-tofrom-the-weekend | https://markhneedham.com/blog/2014/12/13/r-time-tofrom-the-weekend |
false | 2014-12-22 11:46:25 | R: Vectorising all the things | [
"r-2",
"rstats"
] | [
"R"
] | After my last post about http://www.markhneedham.com/blog/2014/12/13/r-time-tofrom-the-weekend/[finding the distance a date/time is from the weekend] Hadley Wickham suggested I could improve the function by vectorising it\...
+++
<blockquote class="twitter-tweet"><p lang="en" dir="ltr"><a href="https://twitter.com/markhneedham?ref_src=twsrc%5Etfw">@markhneedham</a> vectorise with pmin(pmax(dateToLookup - before, 0), pmax(after - dateToLookup, 0)) / dhours(1)</p>— Hadley Wickham (@hadleywickham) <a href="https://twitter.com/hadleywickham/status/544122663573000192?ref_src=twsrc%5Etfw">December 14, 2014</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
+++
...so I thought I'd try and vectorise some of the other functions I've written recently and show the two versions.
I found the following articles useful for explaining vectorisation and why you might want to do it:
* http://www.noamross.net/blog/2014/4/16/vectorization-in-r--why.html[Vectorisation in R: Why?]
* http://www.burns-stat.com/pages/Tutor/R_inferno.pdf[Chapters 3 and 4 of R Inferno]
* http://nesterko.com/blog/2011/04/29/drastic-r-speed-ups-via-vectorization-and-bug-fixes/[Drastic R speed ups via vectorisation and bug fixes]
Let's get started.
== Distance from the weekend
We want to find out how many hours away from the weekend i.e. nearest Saturday/Sunday a particular date/time is. We'll be using the following libraries and set of date/times:
[source,r]
----
library(dplyr)
library(lubridate)
library(geosphere)
options("scipen"=100, "digits"=4)
times = ymd_hms("2002-01-01 17:00:00") + c(0:99) * hours(1)
data = data.frame(time = times)
----
[source,r]
----
> data %>% head()
time
1 2002-01-01 17:00:00
2 2002-01-01 18:00:00
3 2002-01-01 19:00:00
4 2002-01-01 20:00:00
5 2002-01-01 21:00:00
6 2002-01-01 22:00:00
----
Let's have a look at the non vectorised version first:
[source,r]
----
distanceFromWeekend = function(dateToLookup) {
before = floor_date(dateToLookup, "week") + hours(23) + minutes(59) + seconds(59)
after = ceiling_date(dateToLookup, "week") - days(1)
timeToBefore = dateToLookup - before
timeToAfter = after - dateToLookup
if(timeToBefore < 0 || timeToAfter < 0) {
0
} else {
if(timeToBefore < timeToAfter) {
timeToBefore / dhours(1)
} else {
timeToAfter / dhours(1)
}
}
}
----
Now let's run it against our data frame:
[source,r]
----
> system.time(
data %>% mutate(ind = row_number()) %>% group_by(ind) %>% mutate(dist = distanceFromWeekend(time))
)
user system elapsed
1.837 0.020 1.884
----
And now for Hadley's vectorised version:
[source,r]
----
distanceFromWeekendVectorised = function(dateToLookup) {
before = floor_date(dateToLookup, "week") + hours(23) + minutes(59) + seconds(59)
after = ceiling_date(dateToLookup, "week") - days(1)
pmin(pmax(dateToLookup - before, 0), pmax(after - dateToLookup, 0)) / dhours(1)
}
> system.time(data %>% mutate(dist = distanceFromWeekendVectorised(time)))
user system elapsed
0.020 0.001 0.023
----
== Extracting start date
My next example was from http://www.markhneedham.com/blog/2014/12/09/r-cleaning-up-plotting-google-trends-data/[cleaning up Google Trends data] and extracting the start date from a cell inside a CSV file.
We'll use this data frame:
[source,r]
----
googleTrends = read.csv("/Users/markneedham/Downloads/report.csv", row.names=NULL)
names(googleTrends) = c("week", "score")
----
[source,r]
----
> googleTrends %>% head(10)
week score
1 Worldwide; 2004 - present
2 Interest over time
3 Week neo4j
4 2004-01-04 - 2004-01-10 0
5 2004-01-11 - 2004-01-17 0
6 2004-01-18 - 2004-01-24 0
7 2004-01-25 - 2004-01-31 0
8 2004-02-01 - 2004-02-07 0
9 2004-02-08 - 2004-02-14 0
10 2004-02-15 - 2004-02-21 0
----
The non vectorised version looked like this:
[source,r]
----
> system.time(
googleTrends %>%
mutate(ind = row_number()) %>%
group_by(ind) %>%
mutate(dates = strsplit(week, " - "),
start = dates[[1]][1] %>% strptime("%Y-%m-%d") %>% as.character())
)
user system elapsed
0.215 0.000 0.214
----
In this case it's actually not possible to vectorise the code using the +++<cite>+++http://stackoverflow.com/questions/3054612/r-strsplit-and-vectorization[strsplit]+++</cite>+++ so we need to use something else. https://twitter.com/tonkouts[Antonios] showed me how to do so using +++<cite>+++substr+++</cite>+++:
[source,r]
----
> system.time(googleTrends %>% mutate(start = substr(week, 1, 10) %>% ymd()))
user system elapsed
0.018 0.000 0.017
----
== Calculating haversine distance
I wanted to work out the great circular distance from a collection of venues to a centre point in London. I started out with this data frame:
[source,r]
----
centre = c(-0.129581, 51.516578)
venues = read.csv("/tmp/venues.csv")
> venues %>% head()
venue lat lon
1 Skills Matter 51.52 -0.09911
2 Skinkers 51.50 -0.08387
3 Theodore Bullfrog 51.51 -0.12375
4 The Skills Matter eXchange 51.52 -0.09923
5 The Guardian 51.53 -0.12234
6 White Bear Yard 51.52 -0.10980
----
My non vectorised version looked like this:
[source,r]
----
> system.time(venues %>%
mutate(distanceFromCentre = by(venues, 1:nrow(venues), function(row) { distHaversine(c(row$lon, row$lat), centre) }))
)
user system elapsed
0.034 0.000 0.033
----
It's pretty quick but we can do better - the +++<cite>+++distHaversine+++</cite>+++ function allows us to calculate multiple distances if the first argument ot it is a matrix of lon/lat values rather than a vector:
[source,r]
----
> system.time(
venues %>% mutate(distanceFromCentre = distHaversine(cbind(venues$lon, venues$lat), centre))
)
user system elapsed
0.001 0.000 0.001
----
== One I can't figure out\...
And finally I have a function which I can't figure out how to vectorise but maybe someone with more R skillz than me can?
I have a data frame containing the https://gist.github.com/mneedham/6ec5651f0402fd4ca22e[cumulative member counts of various NoSQL London groups]:
[source,r]
----
cumulativeMeetupMembers = read.csv("/tmp/cumulativeMeetupMembers.csv")
> cumulativeMeetupMembers %>% sample_n(10)
g.name dayMonthYear n
4734 Hadoop Users Group UK 2013-10-26 1144
4668 Hadoop Users Group UK 2013-08-03 979
4936 Hadoop Users Group UK 2014-07-31 1644
5150 Hive London 2012-10-15 109
8020 Neo4j - London User Group 2014-03-15 826
7666 Neo4j - London User Group 2012-08-06 78
1030 Big Data London 2013-03-01 1416
6500 London MongoDB User Group 2013-09-21 952
8290 Oracle Big Data 4 the Enterprise 2012-06-04 61
2584 Data Science London 2012-03-20 285
----
And I want to find out the number of members for a group on a specific date. e.g. given the following data\...
[source,r]
----
> cumulativeMeetupMembers %>% head(10)
g.name dayMonthYear n
1 Big Data / Data Science / Data Analytics Jobs 2013-01-29 1
2 Big Data / Data Science / Data Analytics Jobs 2013-02-06 15
3 Big Data / Data Science / Data Analytics Jobs 2013-02-07 28
4 Big Data / Data Science / Data Analytics Jobs 2013-02-10 31
5 Big Data / Data Science / Data Analytics Jobs 2013-02-18 33
6 Big Data / Data Science / Data Analytics Jobs 2013-03-27 38
7 Big Data / Data Science / Data Analytics Jobs 2013-04-16 41
8 Big Data / Data Science / Data Analytics Jobs 2013-07-17 53
9 Big Data / Data Science / Data Analytics Jobs 2013-08-28 58
10 Big Data / Data Science / Data Analytics Jobs 2013-11-11 63
----
\...the number of members for the 'Big Data / Data Science / Data Analytics Jobs' group on the 10th November 2013 should be 58.
I created this data frame of groups and random dates:
[source,R]
----
dates = ymd("2014-09-01") + c(0:9) * weeks(1)
groups = cumulativeMeetupMembers %>% distinct(g.name) %>% select(g.name)
groupsOnDate = merge(dates, groups)
names(groupsOnDate) = c('date', 'name')
> groupsOnDate %>% sample_n(10)
date name
156 2014-10-06 GridGain London
153 2014-09-15 GridGain London
70 2014-11-03 Couchbase London
185 2014-09-29 Hadoop Users Group UK
105 2014-09-29 Data Science London
137 2014-10-13 Equal Experts Technical Meetup Group
360 2014-11-03 Scale Warriors of London
82 2014-09-08 Data Science & Business Analytics London Meetup
233 2014-09-15 London ElasticSearch User Group
84 2014-09-22 Data Science & Business Analytics London Meetup
----
The non vectorised version looks like this:
[source,r]
----
memberCount = function(meetupMembers) {
function(groupName, date) {
(meetupMembers %>%
filter(g.name == groupName & dayMonthYear < date) %>% do(tail(., 1)))$n
}
}
findMemberCount = memberCount(cumulativeMeetupMembers)
> system.time(groupsOnDate %>% mutate(groupMembers = by(groupsOnDate, 1:nrow(groupsOnDate), function(row) {
findMemberCount(row$name, as.character(row$date))
}) %>%
cbind() %>%
as.vector() ))
user system elapsed
2.259 0.005 2.269
----
The output looks like this:
[source,R]
----
date name groupMembers
116 2014-10-06 DeNormalised London 157
322 2014-09-08 OpenCredo Tech Workshops 7
71 2014-09-01 Data Enthusiasts London
233 2014-09-15 London ElasticSearch User Group 614
171 2014-09-01 HPC & GPU Supercomputing Group of London 80
109 2014-10-27 Data Science London 3632
20 2014-11-03 Big Data Developers in London 708
42 2014-09-08 Big Data Week London Meetup 96
127 2014-10-13 Enterprise Search London Meetup 575
409 2014-10-27 Women in Data 548
----
I've tried many different approaches but haven't been able to come up with a version that lets me pass in all the rows to +++<cite>+++memberCount+++</cite>+++ and calculate the count for each row in one go.
Any ideas/advice/hints welcome! | null | null | [
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] | r-vectorising-all-the-things | https://markhneedham.com/blog/2014/12/22/r-vectorising-all-the-things |
false | 2022-03-17 02:44:37 | Apache Pinot: Failed to generate segment - Input path {} does not exist | [
"pinot"
] | [
"pinot"
] | In this blog post we're going to learn how to work around a bug when trying to ingest CSV files with the same name into Apache Pinot.
I came across this issue while writing a recipe showing how to https://dev.startree.ai/docs/pinot/recipes/import-data-files-different-directories[import data files from different directories^].
.Apache Pinot: Failed to generate segment - Input path {} does not exist
image::{{<siteurl>}}/uploads/2022/03/no-import-banner.png[]
== Setup
We're going to spin up a local instance of Pinot and Kafka using the following Docker compose config:
.docker-compose.yml
[source, json]
----
include::content/2022/03/17/docker/docker-compose.yml[]
----
We can launch all the components by running the following command:
[source, bash]
----
docker-compose up
----
== Create Schema
We're going to use the following schema:
./config/schema.json
[source, json]
----
include::content/2022/03/17/docker/config/schema.json[]
----
We can create the schema by running the following command:
[source, bash]
----
docker exec -it pinot-controller-csv bin/pinot-admin.sh AddSchema \
-schemaFile /config/schema.json -exec
----
== Create Table
Now let's create an offline table based on that schema:
./config/table.json
[source, json]
----
include::content/2022/03/17/docker/config/table.json[]
----
We can create the table by running the following command:
[source, bash]
----
docker exec -it pinot-controller-csv bin/pinot-admin.sh AddTable \
-tableConfigFile /config/table.json \
-exec
----
== Ingest CSV files
Now we're going to ingest some CSV files into this table.
The files are in the `input` directory:
[source, bash]
----
tree input/
----
.Output
[source,text]
----
input/
├── 2000_2009
│ └── movies.csv
├── 2010_2019
│ └── movies.csv
└── 2020_present
└── movies.csv
3 directories, 3 files
----
We have three files in different directories, all with the same name.
Let's now try to import them using the following ingestion spec:
./config/job-spec.yml
[source, yaml]
----
include::content/2022/03/17/docker/config/job-spec.yml[]
----
Run the following command:
[source, bash]
----
docker exec -it pinot-controller-csv bin/pinot-admin.sh LaunchDataIngestionJob \
-jobSpecFile /config/job-spec.yml
----
We'll see the following output:
.Output
[source, text]
----
java.lang.IllegalStateException: Input path {} does not exist. [/tmp/pinot-957bc026-8f77-4458-85df-d50d0559c159/input/movies.csv]
at shaded.com.google.common.base.Preconditions.checkState(Preconditions.java:518) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
at org.apache.pinot.segment.spi.creator.SegmentGeneratorConfig.setInputFilePath(SegmentGeneratorConfig.java:416) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
at org.apache.pinot.plugin.ingestion.batch.common.SegmentGenerationTaskRunner.run(SegmentGenerationTaskRunner.java:112) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
at org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner.lambda$submitSegmentGenTask$1(SegmentGenerationJobRunner.java:263) ~[pinot-batch-ingestion-standalone-0.9.3-shaded.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515) [?:?]
at java.util.concurrent.FutureTask.run(FutureTask.java:264) [?:?]
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) [?:?]
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) [?:?]
at java.lang.Thread.run(Thread.java:829) [?:?]
2022/03/10 14:15:59.187 ERROR [SegmentGenerationJobRunner] [pool-2-thread-1] Failed to generate Pinot segment for file - file:/input/2010_2019/movies.csv
java.lang.IllegalStateException: Input path {} does not exist. [/tmp/pinot-957bc026-8f77-4458-85df-d50d0559c159/input/movies.csv]
at shaded.com.google.common.base.Preconditions.checkState(Preconditions.java:518) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
at org.apache.pinot.segment.spi.creator.SegmentGeneratorConfig.setInputFilePath(SegmentGeneratorConfig.java:416) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
at org.apache.pinot.plugin.ingestion.batch.common.SegmentGenerationTaskRunner.run(SegmentGenerationTaskRunner.java:112) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
at org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner.lambda$submitSegmentGenTask$1(SegmentGenerationJobRunner.java:263) ~[pinot-batch-ingestion-standalone-0.9.3-shaded.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515) [?:?]
at java.util.concurrent.FutureTask.run(FutureTask.java:264) [?:?]
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) [?:?]
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) [?:?]
at java.lang.Thread.run(Thread.java:829) [?:?]
----
The reason we get this error is that Pinot unpacks all the input files into the same directory.
It then creates a job per input file that runs on another thread(s).
Once the thread has finished processing a file it deletes it, which is what leads to this error message.
Even if we didn't run into this error we wouldn't end up with all our CSV files ingested since the contents of the input file is overwritten by the next one.
There is a https://github.com/apache/pinot/pull/8337[PR pending to fix this bug^], but in the mean time we need to make sure that our input files have unique names.
In this case we could use the following names:
.Output
[source,text]
----
input/
├── 2000_2009
│ └── 2000_2009.csv
├── 2010_2019
│ └── 2010_2019.csv
└── 2020_present
└── 2020_present.csv
---- | In this post we'll learn how to work out a problem when trying to import files with the same name into Apache Pinot. | uploads/2022/03/no-import-banner.png | [
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] | apache-pinot-failed-to-generated-segment-input-path-does-not-exist | https://markhneedham.com/blog/2022/03/17/apache-pinot-failed-to-generated-segment-input-path-does-not-exist |
false | 2022-04-05 02:44:37 | Apache Pinot: Lookup Join - 500 Error - Unsupported function: lookup with 4 parameters | [
"pinot"
] | [
"pinot"
] | I'm currently working on an Apache Pinot demo using data from https://docs.cryptowat.ch/rest-api/[Crypto Watch^], in which I was using the https://dev.startree.ai/docs/pinot/recipes/lookup-joins[lookup function^] and had a bug in my query that didn't return the clearest error message.
In this blog post we'll have a look at the query and how to fix it.
.Apache Pinot: Lookup Join - 500 Error - Unsupported function: lookup with 4 parameters
image::{{<siteurl>}}/uploads/2022/04/lookup-unsupported-function-banner.png[]
The query that I was writing was using the lookup function to return the name of the base asset in a transaction:
[source, sql]
----
select currencyPairId, orderSide, sum(amount) AS totalAmount,
lookUp('pairs', 'baseName', 'id', currencyPairId) AS baseName
from trades
group by currencyPairId, orderSide
order by totalAmount DESC
----
When I ran this query, I got the following error:
.Output
[source, text]
----
ProcessingException(errorCode:150, message:PQLParsingError:
org.apache.pinot.sql.parsers.SqlCompilationException: 'orderSide' should appear in GROUP BY clause.
at org.apache.pinot.sql.parsers.CalciteSqlParser.validateGroupByClause(CalciteSqlParser.java:130)
at org.apache.pinot.sql.parsers.CalciteSqlParser.validate(CalciteSqlParser.java:115)
at org.apache.pinot.sql.parsers.CalciteSqlParser.queryRewrite(CalciteSqlParser.java:376)
at org.apache.pinot.sql.parsers.CalciteSqlParser.compileCalciteSqlToPinotQuery(CalciteSqlParser.java:367))
----
If we look in the broker's logs, we'll see the following output:
[source, text]
----
pinot-broker-crypto | 2022/04/04 08:49:33.068 ERROR [PinotClientRequest] [jersey-server-managed-async-executor-15] Caught exception while processing POST request
pinot-broker-crypto | java.lang.IllegalArgumentException: Unsupported function: lookup with 4 parameters
pinot-broker-crypto | at shaded.com.google.common.base.Preconditions.checkArgument(Preconditions.java:359) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.apache.pinot.core.query.postaggregation.PostAggregationFunction.<init>(PostAggregationFunction.java:42) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.apache.pinot.core.query.reduce.PostAggregationHandler$PostAggregationValueExtractor.<init>(PostAggregationHandler.java:221) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.apache.pinot.core.query.reduce.PostAggregationHandler.getValueExtractor(PostAggregationHandler.java:120) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.apache.pinot.core.query.reduce.PostAggregationHandler.<init>(PostAggregationHandler.java:70) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.apache.pinot.core.query.reduce.GroupByDataTableReducer.setSQLGroupByInResultTable(GroupByDataTableReducer.java:210) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.apache.pinot.core.query.reduce.GroupByDataTableReducer.reduceAndSetResults(GroupByDataTableReducer.java:128) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.apache.pinot.core.query.reduce.BrokerReduceService.reduceOnDataTable(BrokerReduceService.java:286) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.apache.pinot.broker.requesthandler.SingleConnectionBrokerRequestHandler.processBrokerRequest(SingleConnectionBrokerRequestHandler.java:115) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.apache.pinot.broker.requesthandler.BaseBrokerRequestHandler.handleSQLRequest(BaseBrokerRequestHandler.java:495) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.apache.pinot.broker.requesthandler.BaseBrokerRequestHandler.handleRequest(BaseBrokerRequestHandler.java:197) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.apache.pinot.broker.requesthandler.BaseBrokerRequestHandler.handleRequest(BaseBrokerRequestHandler.java:102) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.apache.pinot.broker.api.resources.PinotClientRequest.processSqlQueryPost(PinotClientRequest.java:191) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at jdk.internal.reflect.GeneratedMethodAccessor22.invoke(Unknown Source) ~[?:?]
pinot-broker-crypto | at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) ~[?:?]
pinot-broker-crypto | at java.lang.reflect.Method.invoke(Method.java:566) ~[?:?]
pinot-broker-crypto | at org.glassfish.jersey.server.model.internal.ResourceMethodInvocationHandlerFactory.lambda$static$0(ResourceMethodInvocationHandlerFactory.java:52) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher$1.run(AbstractJavaResourceMethodDispatcher.java:124) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher.invoke(AbstractJavaResourceMethodDispatcher.java:167) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.server.model.internal.JavaResourceMethodDispatcherProvider$VoidOutInvoker.doDispatch(JavaResourceMethodDispatcherProvider.java:159) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher.dispatch(AbstractJavaResourceMethodDispatcher.java:79) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.server.model.ResourceMethodInvoker.invoke(ResourceMethodInvoker.java:469) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.server.model.ResourceMethodInvoker.lambda$apply$0(ResourceMethodInvoker.java:381) ~[pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.server.ServerRuntime$AsyncResponder$2$1.run(ServerRuntime.java:819) [pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.internal.Errors$1.call(Errors.java:248) [pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.internal.Errors$1.call(Errors.java:244) [pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.internal.Errors.process(Errors.java:292) [pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.internal.Errors.process(Errors.java:274) [pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.internal.Errors.process(Errors.java:244) [pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.process.internal.RequestScope.runInScope(RequestScope.java:265) [pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at org.glassfish.jersey.server.ServerRuntime$AsyncResponder$2.run(ServerRuntime.java:814) [pinot-all-0.9.3-jar-with-dependencies.jar:0.9.3-e23f213cf0d16b1e9e086174d734a4db868542cb]
pinot-broker-crypto | at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515) [?:?]
pinot-broker-crypto | at java.util.concurrent.FutureTask.run(FutureTask.java:264) [?:?]
pinot-broker-crypto | at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) [?:?]
pinot-broker-crypto | at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) [?:?]
pinot-broker-crypto | at java.lang.Thread.run(Thread.java:829) [?:?]
----
This error suggests that the lookup function isn't recognised, which is misleading.
I figured out the actual problem after passing an invalid first parameter to the `lookUp` function, as shown below:
[source, sql]
----
select currencyPairId, orderSide, sum(amount) AS totalAmount,
lookUp(pairs, 'baseName', 'id', currencyPairId) AS baseName
from trades
group by currencyPairId, orderSide
order by totalAmount DESC
----
.Output
[source, text]
----
ProcessingException(errorCode:150, message:PQLParsingError:
org.apache.pinot.sql.parsers.SqlCompilationException: 'AS(LOOKUP(pairs, null, null, currencyPairId), baseName)' should appear in GROUP BY clause.
at org.apache.pinot.sql.parsers.CalciteSqlParser.validateGroupByClause(CalciteSqlParser.java:130)
at org.apache.pinot.sql.parsers.CalciteSqlParser.validate(CalciteSqlParser.java:115)
at org.apache.pinot.sql.parsers.CalciteSqlParser.queryRewrite(CalciteSqlParser.java:376)
at org.apache.pinot.sql.parsers.CalciteSqlParser.compileCalciteSqlToPinotQuery(CalciteSqlParser.java:367))
----
The error message this time indicates that we need to include the lookup join field in the `GROUP BY` clause, which we've forgotten to do.
If we update our original query to do that:
[source, sql]
----
select currencyPairId, orderSide, sum(amount) AS totalAmount,
lookUp('pairs', 'baseName', 'id', currencyPairId) AS baseName
from trades
group by currencyPairId, orderSide, baseName
order by totalAmount DESC
----
The query now works as expected. | In this post we'll learn how to solve a problem when using Apache Pinot's lookup join function. | uploads/2022/04/lookup-unsupported-function-banner.png | [
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] | apache-pinot-lookup-join-internal-error-unsupported-function | https://markhneedham.com/blog/2022/04/05/apache-pinot-lookup-join-internal-error-unsupported-function |
false | 2022-04-08 02:44:37 | Apache Pinot: Speeding up queries with IdSets | [
"pinot"
] | [
"pinot"
] | As I continue to build an Apache Pinot demo using https://docs.cryptowat.ch/rest-api/[CryptoWatch^] data, I found myself needing to optimise some queries so that the real-time dashboard would render more quickly.
I did this using https://docs.pinot.apache.org/users/user-guide-query/filtering-with-idset[IdSets^] and in this blog post we'll learn about those and how to use them.
.Apache Pinot: Speeding up queries with IdSets
image::{{<siteurl>}}/uploads/2022/04/id-set-banner.png[]
== Pinot Schema
For the purpose of this blog post we don't need to know how to configure the Pinot schema and tables, but we do need to know that we're working with `trades` and `pairs` tables, whose schemas are shown below:
.trades and pairs schemas
image::{{<siteurl>}}/uploads/2022/04/trades-pairs.png[]
* `trades` is a real-time table that streams data in from a CryptoWatch live stream via a Kafka topic.
* `pairs` is a offline dimension table that contains look up data of the available currency pairs used by CryptoWatch
`pairs` has `id` as its primary key and we'll be using Pinot's https://dev.startree.ai/docs/pinot/recipes/lookup-joins[lookup join functionality^] to return the human friendly quotes and bases referred to by the `currencyPairId`.
## Querying pairs
From our dashboard we want to return the trades for a give base and quote.
A base is the asset being purchased and a quote is that asset used to buy it.
So to find all the matching rows for buying Bitcoin with US Dollars, we'd write the following query:
[source, sql]
----
select count(*)
FROM pairs
WHERE baseName = 'Bitcoin'
AND quoteName = 'United States Dollar'
----
.Results
[options="header"]
|===
|count(*)
|286
|===
From the output we learn that there are 286 different currency pairs representing the purchase of Bitcoin using US Dollars.
## Lookup join of pairs and trades
Now let's say we want to find all the trades of Bitcoin using US Dollars.
We might write the following query:
[source, sql]
----
select avg(price) AS avgPrice, max(price) as maxPrice, min(price) AS minPrice,
count(*) AS count, sum(amount) AS amountTraded
from trades
WHERE lookUp('pairs', 'baseName', 'id', currencyPairId) = 'Bitcoin'
AND lookUp('pairs', 'quoteName', 'id', currencyPairId) = 'United States Dollar'
----
If we run this query and look at the query stats for this query, we see the following:
.Query Stats
[source, json]
----
{
"numServersQueried": 1,
"numServersResponded": 1,
"numSegmentsQueried": 37,
"numSegmentsProcessed": 37,
"numSegmentsMatched": 37,
"numConsumingSegmentsQueried": 1,
"numDocsScanned": 310757,
"numEntriesScannedInFilter": 32212707,
"numEntriesScannedPostFilter": 621514,
"numGroupsLimitReached": false,
"totalDocs": 16155043,
"timeUsedMs": 440
}
----
I ran this query a few times and it takes between 380ms - 700ms each time.
It's not an ideal query because we're having to call the `lookUp` function twice per row to check whether it matches the base and quote name.
Another approach to finding the matching trades would be to first query the `pairs` table, get all the matching ids, and then use those ids as part of a `WHERE currencyPairId IN ()` clause in a query against the `trades` table.
It does seem a bit clunky though.
== Querying with IdSets
Enter the https://docs.pinot.apache.org/users/user-guide-query/filtering-with-idset[IdSet^]!
IdSets are compressed representations of a set of values.
For ints Pinot uses a RoaringBitmap, for longs a Roaring64NavigableMap, and for other types a Bloom Filter.
The simplest way to generate an IdSet is to use the `ID_SET` function, as shown below:
[source, sql]
----
SELECT ID_SET(id)
FROM pairs
WHERE baseName = 'Bitcoin'
AND quoteName = 'United States Dollar'
----
++++
<style>
table td {
overflow-wrap: anywhere;
}
</style>
++++
.Results
[options="header"]
|===
|idset(id)
|AgAAAAABAAAAADowAAACAAAAAAAGAAIAFgEYAAAAJgAAAAkAHwAlAEsAbQCvAFYB6rHwsfixHbInsyizMLM1s02zTrNPs1CzUbNSs1OzVLNVs7Ozt7PEs8izybPNs9Gz17PYs9mz4LPis+Sz6rPzs/az97P4s/mz/rP/swq0C7QRtBK0FbQWtCG0JbQttD60RbRGtEe0SLRLtCe1aLVutXW1drV3tXi1erWDtYe1irWLtZG1krWTtZa1nrX2tQe2DbYWthe2GbYctkm2XLZgtmO2a7Zstm62b7Z+toG2ibaXtpq2m7adtp62oba5tsC2xbbHtsy20bbSttm227bituq2Wrdot5m3mreit6m3vbcFuBG4GbgouCm4LLg1uIq4pbituNW45LjluO+4BbkbuSq5QrlUuVi5Y7luuXW5drmEuYi5mrmgubS5vbnFuca5zrnXueK56rnxuQW6GrobuiC6JrosujG6Nbo2uj+6RbpGukq6T7pWumu6dbqZuqO6pLquurO6xLrNuuW6/LoHuw27DrsTuxq7J7szu0m7Wbtou2m7crt6u5+7s7u6u7+7ybvKu8671LvXu+C75bvyuw68D7wVvDq8Q7xbvG68e7yNvJG8lryXvJ28oLyjvLa8x7zOvNW81rzZvN+857z6vBC+GL40vh+/Lb/Kv2TAaMBuwEfBSsGyxLzEv8TCxMXEyMTLxM7E1MT7xCHFacWnxanFrsW3xX3GXshtyIHI+8hRyQzMwc7EzsjOcc91z/DPBtA40DnQZdC70KzSDNMZ0x/TLNOJ05rTCtRJ1ErU
|===
We could then use this value to query the `trades` table, like this:
[source, sql]
----
select avg(price) AS avgPrice, max(price) as maxPrice, min(price) AS minPrice,
count(*) AS count, sum(amount) AS amountTraded
from trades
WHERE IN_ID_SET(
currencyPairId,
'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'
) = 1
----
The query stats for this query are shown below:
.Query Stats
[source, json]
----
{
"numServersQueried": 1,
"numServersResponded": 1,
"numSegmentsQueried": 38,
"numSegmentsProcessed": 37,
"numSegmentsMatched": 37,
"numConsumingSegmentsQueried": 1,
"numDocsScanned": 310757,
"numEntriesScannedInFilter": 16155043,
"numEntriesScannedPostFilter": 621514,
"numGroupsLimitReached": false,
"totalDocs": 16155043,
"timeUsedMs": 125
}
----
This is about 3x faster than our original query - a pretty good improvement!
It's still a bit annoying though as we need to pass in that really long string value representing all the ids.
Luckily Pinot lets us write sub queries for IdSets, using the `IN_SUBQUERY` function, as shown below:
[source, sql]
----
select avg(price) AS avgPrice, max(price) as maxPrice, min(price) AS minPrice,
count(*) AS count, sum(amount) AS amountTraded
from trades
WHERE IN_SUBQUERY(
currencyPairId,
'SELECT ID_SET(id)
FROM pairs
WHERE baseName = ''Bitcoin''
AND quoteName = ''United States Dollar'''
) = 1
----
We can see the query stats from running this query below:
.Query Stats
[source, json]
----
{
"numServersQueried": 1,
"numServersResponded": 1,
"numSegmentsQueried": 38,
"numSegmentsProcessed": 37,
"numSegmentsMatched": 37,
"numConsumingSegmentsQueried": 1,
"numDocsScanned": 310757,
"numEntriesScannedInFilter": 16155043,
"numEntriesScannedPostFilter": 621514,
"numGroupsLimitReached": false,
"totalDocs": 16155043,
"timeUsedMs": 132
}
---- | In this post we'll learn how to use Apache Pinot's IdSets to speed up our queries. | uploads/2022/04/id-set-banner.png | [
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] | apache-pinot-speeding-up-queries-id-set | https://markhneedham.com/blog/2022/04/08/apache-pinot-speeding-up-queries-id-set |
false | 2022-04-23 02:44:37 | Dash: Configurable dcc.Interval | [
"dash",
"plotly"
] | [
"dash"
] | :icons: font
As I mentioned in my blog post about https://www.startree.ai/blogs/building-real-time-crypto-dashboard-pinot-dash[building a Real-Time Crypto Dashboard^], I've recently been working with the https://dash.plotly.com/[Dash^] low-code framework for building interactive data apps.
I was using the https://dash.plotly.com/dash-core-components/interval[`dcc.Interval`^] component to automatically refresh components on the page and wanted to make the refresh interval configurable.
In this blog post we'll learn how to do that.
.Dash: Configurable dcc.Interval
image::{{<siteurl>}}/uploads/2022/04/dash-banner.png[]
== Setup
Let's first setup our Python environment:
[source, bash]
----
python -m venv .env
source .env/bin/activate
----
And install the dash library:
[source, bash]
----
pip install dash
----
== Building a refreshing dashboard
Now we're going to build a simple dashboard that refreshes a timestamp on the page once every 1,000 milliseconds i.e. once per second.
The code to do this is shown below:
.dashboard.py
[source, python]
----
include::content/2022/04/23/dashboard.py[]
----
[source, bash]
----
python dashboard.py
----
If we navigate to http://localhost:8050, we'll see the timestamp updating once per second, as shown in the following animation:
.Refreshing every second
image::{{<siteurl>}}/uploads/2022/04/dash-once-second.gif[]
The timestamp updates once every second, just as we'd expect.
To make this value configurable we will use `dcc.Slider` to set the new refresh rate and we'll add a new callback that updates the refresh interval.
.dashboard.py
[source, python]
----
include::content/2022/04/23/dashboard2.py[]
----
<1> `dcc.Slider` lets us configure a new refresh rate
<2> This callback updates the value of `interval-component`
Now if we navigate back to our web browser, we can adjust the interval to be whatever we like:
.Refreshing at different intervals
image::{{<siteurl>}}/uploads/2022/04/adjustable-interval.gif[] | In this post we'll learn how to make the timeout on Dash's dcc.Interval component configurable. | uploads/2022/04/dash-banner.png | [
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] | dash-configurable-dcc-interval | https://markhneedham.com/blog/2022/04/23/dash-configurable-dcc-interval |
false | 2022-02-19 02:44:37 | Streamlit: Overwrite previous value in a loop | [
"streamlit"
] | [
"streamlit"
] | I was recently building a Streamlit app in which I was looping through a stream of values and wanted to only print out the most recent value.
In this blog post we'll learn how to do that.
.Streamlit: Overwrite previous value in a loop
image::{{<siteurl>}}/uploads/2022/02/streamlit-banner.png[]
== Setup
If you want to play along you'll need to create a virtual environment and install Streamlit:
[source, bash]
----
python -m venv env
source venv/bin/activate
pip install streamlit
----
== Streamlit App
Now, create a file `app.py` that contains the following:
.app.py
[source, python]
----
import streamlit as st
st.markdown("#### Numbers one after the other")
for i in range(0,10):
st.write(i)
----
And run the following command from the terminal:
[source, bash]
----
streamlit run app.py
----
If we run this command it will open a web browser at http://localhost:8501, and we'll see the following output:
.Numbers one after the other
image::{{<siteurl>}}/uploads/2022/02/numbers-one-after-the-other.png[width="300px"]
The numbers are all printed out, which isn't exactly what we want.
In this example we're only printing out 10 numbers, but if we had a bigger stream of values it would eventually crash the browser!
Instead we can use a single element container (https://docs.streamlit.io/library/api-reference/layout/st.empty[`st.empty`]) and populate that on each loop.
Let's update `app.py` to look like this:
.app.py
[source, python]
----
import streamlit as st
left, right = st.columns(2)
with left:
st.markdown("#### Numbers one after the other")
for i in range(0,10):
st.write(i)
with right:
st.markdown("#### Numbers overwriting each other")
numbers = st.empty()
for i in range(0,10):
with numbers.container():
st.write(i)
----
Now if we go back to our Streamlit app, we'll see the following:
.Numbers overwriting each other
image::{{<siteurl>}}/uploads/2022/02/numbers-overwriting-each-other.png[width="600px"]
We can only see the number 9 because that's the last value that got printed.
If we want to see how the output progresses, we'll need to pause the loop for a little bit after each iteration.
Update `app.py` to look like this:
.app.py
[source, python]
----
import streamlit as st
import time
left, right = st.columns(2)
with left:
st.markdown("#### Numbers one after the other")
for i in range(0,10):
st.write(i)
with right:
st.markdown("#### Numbers overwriting each other")
numbers = st.empty()
for i in range(0,10):
with numbers.container():
st.write(i)
time.sleep(0.5)
----
And now if we refresh the page, we'll see it update like this:
.Numbers overwriting each other animation
image::{{<siteurl>}}/uploads/2022/02/sleep-numbers.gif[width="600px"]
Exactly what I wanted - good work Streamlit! | In this post we'll learn how to write out to the screen only the latest value in a loop of values using Streamlit. | uploads/2022/02/streamlit-banner.png | [
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] | streamlit-overwrite-previous-value-loop | https://markhneedham.com/blog/2022/02/19/streamlit-overwrite-previous-value-loop |
false | 2022-02-21 02:44:37 | Apache Pinot: Deleting instances in a bad state | [
"pinot"
] | [
"pinot"
] | Sometimes when I start up a local Pinot cluster after doing a hard shutdown (by restarting my computer) I noticed that the Pinot Data Explorer shows controllers, brokers, or servers in a bad state.
In this blog post we'll see how to get rid of those bad instances.
.Apache Pinot: Deleting instances in a bad state
image::{{<siteurl>}}/uploads/2022/02/bad-state-banner.png[]
The screenshot below shows several instances in the bad state.
.Instances in a bad state
image::{{<siteurl>}}/uploads/2022/02/bad-instances.png[]
I'm not entirely sure why this happens, but I assume it's something to do with my local IP address changing as far as Pinot's concerned.
Having these instances in a bad state doesn't actually cause me any problems, they're more of an irritation.
Luckily we can get rid of that irritation with help from the REST API's drop instance endpoint, shown in the screen shot below:
.Drop instance endpoint
image::{{<siteurl>}}/uploads/2022/02/drop-instance-endpoint.png[]
So, what does calling this end point actually do?
I had a quick look at the code and learnt that it deletes the following Zookeeper entries:
* `INSTANCES/<instanceName>`
* `/CONFIGS/PARTICIPANT/<instanceName>`
We can see an example of what that part of our Zookeeper metadata looks like below:
.Zookeeper meta
image::{{<siteurl>}}/uploads/2022/02/zookeeper-metadata.png[]
We want to remove the following instances:
* Controller_172.21.0.4_9000
* Controller_172.21.0.2_9000
* Controller_172.21.0.5_9000
* Server_172.21.0.3_8098
* Server_172.21.0.4_8098
* Broker_172.21.0.3_8099
Let's try to remove those instances, by running the following command:
[source, bash]
----
for instance in "Controller_172.21.0.4_9000" "Controller_172.21.0.2_9000" "Controller_172.21.0.5_9000" "Server_172.21.0.3_8098" "Server_172.21.0.4_8098" "Broker_172.21.0.3_8099"; do
curl -X DELETE "http://localhost:9000/instances/${instance}" \
-H "accept: application/json" 2>/dev/null;
printf "\n"
done
----
.Output
[source, json]
----
{"status":"Successfully dropped instance"}
{"status":"Successfully dropped instance"}
{"status":"Successfully dropped instance"}
{"status":"Successfully dropped instance"}
{"_code":409,"_error":"Failed to drop instance Server_172.21.0.4_8098 - Instance Server_172.21.0.4_8098 exists in ideal state for races_REALTIME"}
{"_code":409,"_error":"Failed to drop instance Broker_172.21.0.3_8099 - Instance Broker_172.21.0.3_8099 exists in ideal state for brokerResource"}
----
We were able to remove four of the instances, but it looks like two of them are still in use.
Let's see if we can figure out what's going on.
We can return the ideal state for the `brokerResource` by running the following command:
[source, bash]
----
curl -X GET "http://localhost:9000/zk/get?path=%2FPinotCluster%2FIDEALSTATES%2FbrokerResource" \
-H "accept: text/plain" 2>/dev/null
----
.Output
[source, json]
----
{
"id" : "brokerResource",
"simpleFields" : {
"BATCH_MESSAGE_MODE" : "false",
"IDEAL_STATE_MODE" : "CUSTOMIZED",
"NUM_PARTITIONS" : "3",
"REBALANCE_MODE" : "CUSTOMIZED",
"REPLICAS" : "0",
"STATE_MODEL_DEF_REF" : "BrokerResourceOnlineOfflineStateModel",
"STATE_MODEL_FACTORY_NAME" : "DEFAULT"
},
"mapFields" : {
"courses_OFFLINE" : {
"Broker_172.21.0.3_8099" : "ONLINE",
"Broker_172.21.0.5_8099" : "ONLINE"
},
"parkrun_REALTIME" : {
"Broker_172.21.0.3_8099" : "ONLINE",
"Broker_172.21.0.5_8099" : "ONLINE"
},
"races_REALTIME" : {
"Broker_172.21.0.3_8099" : "ONLINE",
"Broker_172.21.0.5_8099" : "ONLINE"
}
},
"listFields" : { }
}
----
Let's create a file, `newBrokerState.json`, that removes the `Broker_172.21.0.3_8099` entry.
The file should look like this:
.newBrokerState.json
[source, json]
----
include::content/2022/02/21/newBrokerState.json[]
----
Now navigate to the Zookeeper browser at http://localhost:9000/#/zookeeper and navigate down to `Pinot Cluster -> IDEALSTATES -> brokerResource`.
Click on the edit button, paste in this JSON document, and click *Update*.
.Updating the brokerResouce ideal state
image::{{<siteurl>}}/uploads/2022/02/update-ideal-state.gif[]
And now let's do the same thing for `races_REALTIME`.
The ideal state at the moment looks like this:
[source, bash]
----
curl -X GET "http://localhost:9000/zk/get?path=%2FPinotCluster%2FIDEALSTATES%2Fraces_REALTIME" \
-H "accept: text/plain" 2>/dev/null
----
.Output
[source, json]
----
{
"id" : "races_REALTIME",
"simpleFields" : {
"BATCH_MESSAGE_MODE" : "false",
"IDEAL_STATE_MODE" : "CUSTOMIZED",
"INSTANCE_GROUP_TAG" : "races_REALTIME",
"MAX_PARTITIONS_PER_INSTANCE" : "1",
"NUM_PARTITIONS" : "11",
"REBALANCE_MODE" : "CUSTOMIZED",
"REPLICAS" : "1",
"STATE_MODEL_DEF_REF" : "SegmentOnlineOfflineStateModel",
"STATE_MODEL_FACTORY_NAME" : "DEFAULT"
},
"mapFields" : {
"races__0__0__20220127T1647Z" : {
"Server_172.21.0.6_8098" : "ONLINE"
},
"races__0__10__20220218T1304Z" : {
"Server_172.21.0.4_8098" : "CONSUMING"
},
"races__0__1__20220202T1635Z" : {
"Server_172.21.0.6_8098" : "ONLINE"
},
"races__0__2__20220203T1636Z" : {
"Server_172.21.0.6_8098" : "ONLINE"
},
"races__0__3__20220209T1442Z" : {
"Server_172.21.0.6_8098" : "ONLINE"
},
"races__0__4__20220210T1442Z" : {
"Server_172.21.0.6_8098" : "ONLINE"
},
"races__0__5__20220212T1807Z" : {
"Server_172.21.0.6_8098" : "ONLINE"
},
"races__0__6__20220214T1304Z" : {
"Server_172.21.0.6_8098" : "ONLINE"
},
"races__0__7__20220215T1304Z" : {
"Server_172.21.0.6_8098" : "ONLINE"
},
"races__0__8__20220216T1304Z" : {
"Server_172.21.0.6_8098" : "ONLINE"
},
"races__0__9__20220217T1304Z" : {
"Server_172.21.0.6_8098" : "ONLINE"
}
},
"listFields" : { }
}
----
And now let's replace `Server_172.21.0.4_8098` with `Server_172.21.0.6_8098` for the consuming segment.
.newServerState.json
[source, json]
----
include::content/2022/02/21/newServerState.json[]
----
We'll update that node in Zookeeper the same way that we did with the `brokerResource`, and now we can re-run our command to delete these two instances:
[source, bash]
----
for instance in "Controller_172.21.0.4_9000" "Controller_172.21.0.2_9000" "Controller_172.21.0.5_9000" "Server_172.21.0.3_8098" "Server_172.21.0.4_8098" "Broker_172.21.0.3_8099"; do
curl -X DELETE "http://localhost:9000/instances/${instance}" \
-H "accept: application/json" 2>/dev/null;
printf "\n"
done
----
.Output
[source, json]
----
{"_code":404,"_error":"Instance Controller_172.21.0.4_9000 not found"}
{"_code":404,"_error":"Instance Controller_172.21.0.2_9000 not found"}
{"_code":404,"_error":"Instance Controller_172.21.0.5_9000 not found"}
{"_code":404,"_error":"Instance Server_172.21.0.3_8098 not found"}
{"status":"Successfully dropped instance"}
{"status":"Successfully dropped instance"}
----
The first 4 instances return a 404 status since we've already deleted them, but the last two have now been deleted!
Now if we navigate back to the home page of the Pinot Data Explorer, we'll see that there are no bad instances anymore:
.No instances in a bad state
image::{{<siteurl>}}/uploads/2022/02/no-more-bad.png[] | In this post we'll learn how to write out to the screen only the latest value in a loop of values using Streamlit. | uploads/2022/02/bad-state-banner.png | [
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] | apache-pinot-delete-instances-bad-state | https://markhneedham.com/blog/2022/02/21/apache-pinot-delete-instances-bad-state |
false | 2022-11-18 02:44:37 | Apache Pinot: Inserts from SQL - Unable to get tasks states map - ClassNotFoundException: 'org.apache.pinot.plugin.filesystem.S3PinotFS' | [
"pinot"
] | [
"pinot"
] | I recently wrote a post on the StarTre blog describing the https://startree.ai/blog/apache-pinot-0-11-inserts-from-sql[inserts from SQL^] feature that was added in Apache Pinot 0.11, and while writing it I came across some interesting exceptions due to configuration mistakes I'd made.
In this post we're going to describe one of those exceptions.
To recap, I was trying to ingest a bunch of JSON files from an S3 bucket using the following SQL query:
[source, sql]
----
INSERT INTO "events"
FROM FILE 's3://marks-st-cloud-bucket/events/*.json'
OPTION(
taskName='myTask-s3',
input.fs.className='org.apache.pinot.plugin.filesystem.S3PinotFS',
input.fs.prop.accessKey='AKIARCOCT6DWLUB7F77Z',
input.fs.prop.secretKey='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',
input.fs.prop.region='eu-west-2'
);
----
[NOTE]
====
Don't worry, those credentials were deactivated and deleted several days ago.
====
I tried to run the query using an existing Docker setup that I had and got the following exception:
[source, text]
----
[
{
"message": "QueryExecutionError:\norg.apache.commons.httpclient.HttpException: Unable to get tasks states map. Error code 500, Error message: {\"code\":500,\"error\":\"Failed to create adhoc task: java.lang.ClassNotFoundException: 'org.apache.pinot.plugin.filesystem.S3PinotFS'\\n\\tat java.base/java.net.URLClassLoader.findClass(URLClassLoader.java:476)\\n\\tat java.base/java.lang.ClassLoader.loadClass(ClassLoader.java:589)\\n\\tat org.apache.pinot.spi.plugin.PluginClassLoader.loadClass(PluginClassLoader.java:104)\\n\\tat org.apache.pinot.spi.plugin.PluginManager.createInstance(PluginManager.java:354)\\n\\tat org.apache.pinot.spi.plugin.PluginManager.createInstance(PluginManager.java:325)\\n\\tat org.apache.pinot.spi.plugin.PluginManager.createInstance(PluginManager.java:306)\\n\\tat org.apache.pinot.plugin.minion.tasks.segmentgenerationandpush.SegmentGenerationAndPushTaskUtils.getInputPinotFS(SegmentGenerationAndPushTaskUtils.java:47)\\n\\tat org.apache.pinot.plugin.minion.tasks.segmentgenerationandpush.SegmentGenerationAndPushTaskGenerator.getInputFilesFromDirectory(SegmentGenerationAndPushTaskGenerator.java:326)\\n\\tat org.apache.pinot.plugin.minion.tasks.segmentgenerationandpush.SegmentGenerationAndPushTaskGenerator.generateTasks(SegmentGenerationAndPushTaskGenerator.java:211)\\n\\tat org.apache.pinot.controller.helix.core.minion.PinotTaskManager.createTask(PinotTaskManager.java:194)\\n\\tat org.apache.pinot.controller.api.resources.PinotTaskRestletResource.executeAdhocTask(PinotTaskRestletResource.java:542)\\n\\tat jdk.internal.reflect.GeneratedMethodAccessor257.invoke(Unknown Source)\\n\\tat java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\\n\\tat java.base/java.lang.reflect.Method.invoke(Method.java:566)\\n\\tat org.glassfish.jersey.server.model.internal.ResourceMethodInvocationHandlerFactory.lambda$static$0(ResourceMethodInvocationHandlerFactory.java:52)\\n\\tat org.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher$1.run(AbstractJavaResourceMethodDispatcher.java:124)\\n\\tat org.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher.invoke(AbstractJavaResourceMethodDispatcher.java:167)\\n\\tat org.glassfish.jersey.server.model.internal.JavaResourceMethodDispatcherProvider$VoidOutInvoker.doDispatch(JavaResourceMethodDispatcherProvider.java:159)\\n\\tat org.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher.dispatch(AbstractJavaResourceMethodDispatcher.java:79)\\n\\tat org.glassfish.jersey.server.model.ResourceMethodInvoker.invoke(ResourceMethodInvoker.java:475)\\n\\tat org.glassfish.jersey.server.model.ResourceMethodInvoker.lambda$apply$0(ResourceMethodInvoker.java:387)\\n\\tat org.glassfish.jersey.server.ServerRuntime$AsyncResponder$2$1.run(ServerRuntime.java:816)\\n\\tat org.glassfish.jersey.internal.Errors$1.call(Errors.java:248)\\n\\tat org.glassfish.jersey.internal.Errors$1.call(Errors.java:244)\\n\\tat org.glassfish.jersey.internal.Errors.process(Errors.java:292)\\n\\tat org.glassfish.jersey.internal.Errors.process(Errors.java:274)\\n\\tat org.glassfish.jersey.internal.Errors.process(Errors.java:244)\\n\\tat org.glassfish.jersey.process.internal.RequestScope.runInScope(RequestScope.java:265)\\n\\tat org.glassfish.jersey.server.ServerRuntime$AsyncResponder$2.run(ServerRuntime.java:811)\\n\\tat java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515)\\n\\tat java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)\\n\\tat java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)\\n\\tat java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)\\n\\tat java.base/java.lang.Thread.run(Thread.java:829)\\n\"}\n\tat org.apache.pinot.common.minion.MinionClient.executeTask(MinionClient.java:123)\n\tat org.apache.pinot.core.query.executor.sql.SqlQueryExecutor.executeDMLStatement(SqlQueryExecutor.java:102)\n\tat org.apache.pinot.controller.api.resources.PinotQueryResource.executeSqlQuery(PinotQueryResource.java:145)\n\tat org.apache.pinot.controller.api.resources.PinotQueryResource.handlePostSql(PinotQueryResource.java:103)",
"errorCode": 200
}
]
----
The mistake I've made here is that those option key/value pairs shouldn't be in quotes. If we update it to read this like this:
[source, sql]
----
INSERT INTO "events"
FROM FILE 's3://marks-st-cloud-bucket/events/*.json'
OPTION(
taskName=myTask-s3,
input.fs.className=org.apache.pinot.plugin.filesystem.S3PinotFS,
input.fs.prop.accessKey=AKIARCOCT6DWLUB7F77Z,
input.fs.prop.secretKey=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,
input.fs.prop.region=eu-west-2
);
----
It'll work perfectly fine! | In this post we'll learn how to fix the Unable to get tasks states map - Failed to create adhoc task ClassNotFoundException error when using Apache Pinot's inserts from SQL feature. | uploads/2022/11/class-not-found-banner.png | [
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] | apache-pinot-inserts-sql-unable-get-tasks-states-map-classnotfoundexception-s3pinotfs | https://markhneedham.com/blog/2022/11/18/apache-pinot-inserts-sql-unable-get-tasks-states-map-classnotfoundexception-s3pinotfs |
false | 2022-11-18 02:44:37 | Apache Pinot: Inserts from SQL - Unable to get tasks states map - NullPointerException | [
"pinot"
] | [
"pinot"
] | I recently wrote a post on the StarTre blog describing the https://startree.ai/blog/apache-pinot-0-11-inserts-from-sql[inserts from SQL^] feature that was added in Apache Pinot 0.11, and while writing it I came across some interesting exceptions due to configuration mistakes I'd made.
In this post we're going to describe one of those exceptions.
To recap, I was trying to ingest a bunch of JSON files from an S3 bucket using the following SQL query:
[source, sql]
----
INSERT INTO "events"
FROM FILE 's3://marks-st-cloud-bucket/events/*.json'
OPTION(
taskName=myTask-s3,
input.fs.className=org.apache.pinot.plugin.filesystem.S3PinotFS,
input.fs.prop.accessKey=AKIARCOCT6DWLUB7F77Z,
input.fs.prop.secretKey=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,
input.fs.prop.region=eu-west-2
);
----
[NOTE]
====
Don't worry, those credentials were deactivated and deleted several days ago.
====
I tried to run the query using an existing Docker setup that I had and got the following exception:
[source, text]
----
[
{
"message": "QueryExecutionError:\norg.apache.commons.httpclient.HttpException: Unable to get tasks states map. Error code 500, Error message: {\"code\":500,\"error\":\"Failed to create adhoc task: java.lang.NullPointerException\\n\\tat java.base/java.util.HashMap.putMapEntries(HashMap.java:497)\\n\\tat java.base/java.util.HashMap.putAll(HashMap.java:781)\\n\\tat org.apache.pinot.plugin.minion.tasks.segmentgenerationandpush.SegmentGenerationAndPushTaskGenerator.generateTasks(SegmentGenerationAndPushTaskGenerator.java:202)\\n\\tat org.apache.pinot.controller.helix.core.minion.PinotTaskManager.createTask(PinotTaskManager.java:194)\\n\\tat org.apache.pinot.controller.api.resources.PinotTaskRestletResource.executeAdhocTask(PinotTaskRestletResource.java:542)\\n\\tat jdk.internal.reflect.GeneratedMethodAccessor257.invoke(Unknown Source)\\n\\tat java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\\n\\tat java.base/java.lang.reflect.Method.invoke(Method.java:566)\\n\\tat org.glassfish.jersey.server.model.internal.ResourceMethodInvocationHandlerFactory.lambda$static$0(ResourceMethodInvocationHandlerFactory.java:52)\\n\\tat org.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher$1.run(AbstractJavaResourceMethodDispatcher.java:124)\\n\\tat org.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher.invoke(AbstractJavaResourceMethodDispatcher.java:167)\\n\\tat org.glassfish.jersey.server.model.internal.JavaResourceMethodDispatcherProvider$VoidOutInvoker.doDispatch(JavaResourceMethodDispatcherProvider.java:159)\\n\\tat org.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher.dispatch(AbstractJavaResourceMethodDispatcher.java:79)\\n\\tat org.glassfish.jersey.server.model.ResourceMethodInvoker.invoke(ResourceMethodInvoker.java:475)\\n\\tat org.glassfish.jersey.server.model.ResourceMethodInvoker.lambda$apply$0(ResourceMethodInvoker.java:387)\\n\\tat org.glassfish.jersey.server.ServerRuntime$AsyncResponder$2$1.run(ServerRuntime.java:816)\\n\\tat org.glassfish.jersey.internal.Errors$1.call(Errors.java:248)\\n\\tat org.glassfish.jersey.internal.Errors$1.call(Errors.java:244)\\n\\tat org.glassfish.jersey.internal.Errors.process(Errors.java:292)\\n\\tat org.glassfish.jersey.internal.Errors.process(Errors.java:274)\\n\\tat org.glassfish.jersey.internal.Errors.process(Errors.java:244)\\n\\tat org.glassfish.jersey.process.internal.RequestScope.runInScope(RequestScope.java:265)\\n\\tat org.glassfish.jersey.server.ServerRuntime$AsyncResponder$2.run(ServerRuntime.java:811)\\n\\tat java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515)\\n\\tat java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)\\n\\tat java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)\\n\\tat java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)\\n\\tat java.base/java.lang.Thread.run(Thread.java:829)\\n\"}\n\tat org.apache.pinot.common.minion.MinionClient.executeTask(MinionClient.java:123)\n\tat org.apache.pinot.core.query.executor.sql.SqlQueryExecutor.executeDMLStatement(SqlQueryExecutor.java:102)\n\tat org.apache.pinot.controller.api.resources.PinotQueryResource.executeSqlQuery(PinotQueryResource.java:145)\n\tat org.apache.pinot.controller.api.resources.PinotQueryResource.handlePostSql(PinotQueryResource.java:103)",
"errorCode": 200
}
]
----
A bit of debugging of the https://github.com/apache/pinot[Pinot source code^] made me realise that I was missing the `SegmentGenerationAndPushTask` definition in my table config.
This means that the task to do the data ingestion can't be scheduled.
The missing configuration is shown below:
[source, json]
----
"task": {
"taskTypeConfigsMap": {
"SegmentGenerationAndPushTask": {
}
}
}
----
Once I added that everything was happy again and the files were ingested into Pinot. | In this post we'll learn how to fix the Unable to get tasks states map - Failed to create adhoc task NullPointerException error when using Apache Pinot's inserts from SQL feature. | uploads/2022/11/npe-banner.png | [
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] | apache-pinot-inserts-sql-unable-get-tasks-states-map-nullpointerexception | https://markhneedham.com/blog/2022/11/18/apache-pinot-inserts-sql-unable-get-tasks-states-map-nullpointerexception |
false | 2022-11-18 02:44:37 | Apache Pinot: Inserts from SQL - Unable to get tasks states map - No task is generated for table | [
"pinot"
] | [
"pinot"
] | I recently wrote a post on the StarTre blog describing the https://startree.ai/blog/apache-pinot-0-11-inserts-from-sql[inserts from SQL^] feature that was added in Apache Pinot 0.11, and while writing it I came across some interesting exceptions due to configuration mistakes I'd made.
In this post we're going to describe one of those exceptions.
To recap, I was trying to ingest a bunch of JSON files from an S3 bucket using the following SQL query:
[source, sql]
----
INSERT INTO "events"
FROM FILE 's3://marks-st-cloud-bucket/events/*.json'
OPTION(
taskName=myTask-s3,
input.fs.className=org.apache.pinot.plugin.filesystem.S3PinotFS,
input.fs.prop.accessKey=AKIARCOCT6DWLUB7F77Z,
input.fs.prop.secretKey=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,
input.fs.prop.region=eu-west-2
);
----
[NOTE]
====
Don't worry, those credentials were deactivated and deleted several days ago.
====
When I ran this query against a Pinot cluster that contained a controller, broker, and server, I got the following exception:
[source, text]
----
[
{
"message": "QueryExecutionError:\norg.apache.commons.httpclient.HttpException: Unable to get tasks states map. Error code 400, Error message: {\"code\":400,\"error\":\"No task is generated for table: events, with task type: SegmentGenerationAndPushTask\"}\n\tat org.apache.pinot.common.minion.MinionClient.executeTask(MinionClient.java:123)\n\tat org.apache.pinot.core.query.executor.sql.SqlQueryExecutor.executeDMLStatement(SqlQueryExecutor.java:102)\n\tat org.apache.pinot.controller.api.resources.PinotQueryResource.executeSqlQuery(PinotQueryResource.java:145)\n\tat org.apache.pinot.controller.api.resources.PinotQueryResource.handlePostSql(PinotQueryResource.java:103)",
"errorCode": 200
}
]
----
My mistake here was that I **didn't have a minion** in the cluster.
The ingestion job is run by the minion component, so without one of those this feature doesn't work!
== An update (30th June 2023)
Today I learned that you can get this error even if you do have a minion configured.
The scenario that results in this error is if no files are found for ingestion.
This might happen if you have an invalid glob expression in the `includeFileNamePattern`:
For example, the following throws the exception:
[source, sql]
----
SET taskName = 'events-task7';
SET input.fs.className = 'org.apache.pinot.spi.filesystem.LocalPinotFS';
SET includeFileNamePattern='glob:customers.csv';
INSERT INTO customers
FROM FILE 'file:///input/';
----
We can fix the query by adding `**/` at the beginning:
[source, sql]
----
SET taskName = 'events-task7';
SET input.fs.className = 'org.apache.pinot.spi.filesystem.LocalPinotFS';
SET includeFileNamePattern='glob:**/customers.csv';
INSERT INTO customers
FROM FILE 'file:///input/';
---- | In this post we'll learn how to fix the Unable to get tasks states map - No task is generated for table error when using Apache Pinot's inserts from SQL feature. | uploads/2022/11/no-task-banner.png | [
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] | apache-pinot-inserts-sql-unable-get-tasks-states-map-no-task-generated-for-table | https://markhneedham.com/blog/2022/11/18/apache-pinot-inserts-sql-unable-get-tasks-states-map-no-task-generated-for-table |
false | 2022-11-11 02:44:37 | Java: FileSystems.getDefault().getPathMatcher: IllegalArgumentException | [
"java"
] | [
"java"
] | I was debugging something in the https://github.com/apache/pinot/[Apache Pinot code^] earlier this week and came across the `FileSystems.getDefault().getPathMatcher` function, which didn't work quite how I expected.
The function creates a `PathMatcher` that you can use to match against Paths.
I was passing through a value of `*.json`, which was then resulting in code similar to this:
[source, java]
----
import java.nio.file.FileSystems;
import java.nio.file.Path;
import java.nio.file.PathMatcher;
class Main {
public static void main(String args[]) {
PathMatcher matcher = FileSystems.getDefault().getPathMatcher("*.json");
System.out.println(matcher.matches(Path.of("/tmp", "mark.json")));
}
}
----
I've copied that code into https://replit.com/@MarkNeedham/Mark-Needham#Main.java[Replit^] and if we run it on there, we'll see the following exception:
[source, text]
----
Exception in thread "main" java.lang.IllegalArgumentException
at java.base/sun.nio.fs.UnixFileSystem.getPathMatcher(UnixFileSystem.java:286)
at Main.main(Main.java:5)
exit status 1
----
After reading the https://docs.oracle.com/javase/7/docs/api/java/nio/file/FileSystem.html#getPathMatcher(java.lang.String)[documentation^] I learnt that the pattern needs to be prefixed with `regex:` or `glob:`, otherwise it won't work.
Let's update the example to do that:
[source, java]
----
import java.nio.file.FileSystems;
import java.nio.file.Path;
import java.nio.file.PathMatcher;
class Main {
public static void main(String args[]) {
PathMatcher matcher = FileSystems.getDefault().getPathMatcher("glob:*.json");
System.out.println(matcher.matches(Path.of("/tmp", "mark.json")));
}
}
----
If we run that again, this time we get a result telling us that the file doesn't exist:
[source, text]
----
false
---- | In this post we'll learn how to use Java's FileSystems.getDefault().getPathMatcher | uploads/2022/11/path-matcher.png | [
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] | java-file-systems-path-matcher | https://markhneedham.com/blog/2022/11/11/java-file-systems-path-matcher |
false | 2022-11-17 02:44:37 | Diffing Apache Parquet schemas with DuckDB | [
"duckdb",
"parquet"
] | [
"parquet"
] | I've been playing around with DuckDB, the https://techcrunch.com/2022/11/15/motherduck-secures-investment-from-andreessen-horowitz-to-commercialize-duckdb/[new hotness^] in the analytics space, over the last month, and my friend https://twitter.com/mesirii[Michael Hunger^] asked whether you could use it to compute a diff of Apache Parquet schemas.
Challenge accepted!
image::{{<siteurl>}}/uploads/2022/11/challenge-accepted.gif[Challenge Accepted!]
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/vfIzz2-ZFB0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
++++
====
## Querying the Parquet Schema
Let's say we have three Parquet files that represent ATP tennis players - `players1.parquet`, `players2.parquet`, and `players3.parquet`.
We can get the list of column names along with their data type and file name by running the following query in the https://duckdb.org/docs/api/cli.html[DuckDB CLI^]:
[source, sql]
----
SELECT file_name, name, type
FROM parquet_schema('players*.parquet')
ORDER BY file_name;
----
If we run this query, we'll see the following results:
.Results
[opts="header"]
|===
| file_name | name | type
| players1.parquet | duckdb_schema | BOOLEAN
| players1.parquet | player_id | INT32
| players1.parquet | name_first | BYTE_ARRAY
| players1.parquet | name_last | BYTE_ARRAY
| players1.parquet | hand | BYTE_ARRAY
| players1.parquet | dob | INT32
| players1.parquet | ioc | BYTE_ARRAY
| players1.parquet | height | INT32
| players1.parquet | wikidata_id | BYTE_ARRAY
| players2.parquet | duckdb_schema | BOOLEAN
| players2.parquet | player_id | INT32
| players2.parquet | first_name | BYTE_ARRAY
| players2.parquet | last_name | BYTE_ARRAY
| players2.parquet | hand | BYTE_ARRAY
| players2.parquet | dob | INT32
| players2.parquet | ioc | BYTE_ARRAY
| players2.parquet | height | INT32
| players2.parquet | wikidata_id | BYTE_ARRAY
| players3.parquet | duckdb_schema | BOOLEAN
| players3.parquet | player_id | BYTE_ARRAY
| players3.parquet | first_name | BYTE_ARRAY
| players3.parquet | last_name | BYTE_ARRAY
| players3.parquet | hand | BYTE_ARRAY
| players3.parquet | dob | BYTE_ARRAY
| players3.parquet | ioc | BYTE_ARRAY
| players3.parquet | height | BYTE_ARRAY
|===
From scanning this list, we can see that there are some differences in the schemas, but it's not entirely obvious what those differences are.
Let's see if we can write some queries to figure it out.
## Temporary Tables
But first, we're going to create a temporary table per schema file to simplify the queries that we have to write.
Temporary tables are stored in memory for the current session and will be gone should we exit the DuckDB CLI.
The following code creates the temporary tables:
[source, sql]
----
CREATE OR REPLACE TEMP TABLE p1Schema AS
SELECT * FROM parquet_schema('players1.parquet');
CREATE OR REPLACE TEMP TABLE p2Schema AS
SELECT * FROM parquet_schema('players2.parquet');
CREATE OR REPLACE TEMP TABLE p3Schema AS
SELECT * FROM parquet_schema('players3.parquet');
----
## Missing fields
Now we're going to write a query that finds the fields that are in p1Schema, but not in p2Schema.
We'll use SQL's `EXCEPT` clause to do this:
[source, sql]
----
SELECT name, type FROM p1Schema
EXCEPT
SELECT name, type FROM p2Schema;
----
If we run this query, we'll see the following results:
.Results
[opts="header"]
|===
| name | type
| name_first | BYTE_ARRAY
| name_last | BYTE_ARRAY
|===
There are a couple of fields missing in `p2Schema` that are in `p1Schema`.
Let's now run the inverse query to discover fields that are in `p2Schema`, but not in `p1Schema`:
[source, sql]
----
SELECT name, type FROM p2Schema
EXCEPT
SELECT name, type FROM p1Schema;
----
The results are as follows:
.Results
[opts="header"]
|===
| name | type
| first_name | BYTE_ARRAY
| last_name | BYTE_ARRAY
|===
It looks like we did some basic field renaming between these two schemas!
Let's put both those queries together, along with a description that explains what's going on for missing column:
[source, sql]
----
WITH schema1 AS (
SELECT name, type FROM p1Schema
), schema2 AS (
SELECT name, type FROM p2Schema
), notInSchema1 AS (
SELECT * FROM schema1 EXCEPT SELECT * FROM schema2
), notInSchema2 AS (
SELECT * FROM schema2 EXCEPT SELECT * FROM schema1
)
SELECT *, 'Missing in players2.parquet' AS description
FROM notInSchema1
UNION ALL
SELECT *, 'Missing in players1.parquet' AS description
FROM notInSchema2;
----
If we run this query, we'll see this outpu
.Results
[opts="header"]
|===
| name | type | description
| name_first | BYTE_ARRAY | Missing in players2.parquet
| name_last | BYTE_ARRAY | Missing in players2.parquet
| first_name | BYTE_ARRAY | Missing in players1.parquet
| last_name | BYTE_ARRAY | Missing in players1.parquet
|===
Now we can clearly see that the first two fields are missing in `players2.parquet` and the latter ones are missing in `players1.parquet`.
How about if we do the same thing for `p2Schema` and `p3Schema`?
[source, sql]
----
WITH schema1 AS (
SELECT name, type FROM p2Schema
), schema2 AS (
SELECT name, type FROM p3Schema
), notInSchema1 AS (
SELECT * FROM schema1 EXCEPT SELECT * FROM schema2
), notInSchema2 AS (
SELECT * FROM schema2 EXCEPT SELECT * FROM schema1
)
SELECT *, 'Missing in players3.parquet' AS description
FROM notInSchema1
UNION ALL
SELECT *, 'Missing in players2.parquet' AS description
FROM notInSchema2;
----
And let's run that:
.Results
[opts="header"]
|===
| name | type | description
| player_id | INT32 | Missing in players3.parquet
| dob | INT32 | Missing in players3.parquet
| height | INT32 | Missing in players3.parquet
| wikidata_id | BYTE_ARRAY | Missing in players3.parquet
| player_id | BYTE_ARRAY | Missing in players2.parquet
| dob | BYTE_ARRAY | Missing in players2.parquet
| height | BYTE_ARRAY | Missing in players2.parquet
|===
`wikidata_id` is missing in players3.parquet, but the other 3 fields are clearly in both schemas, so why are they showing up?!
It turns out they have a different data type in each schema.
## Same columns, different type
To return an easier to read representation of schemas that have the same fields/columns, with different types, we can write the following query:
[source, sql]
----
SELECT schema1.name AS s1Name, schema1.type AS s1Type, schema2.type AS s2Type,
schema1.file_name AS s1File, schema2.file_name AS s2File
FROM p2Schema AS schema1
INNER JOIN p3Schema AS schema2 ON schema2.name = schema1.name
WHERE s1Type <> s2Type;
----
And let's see those results...
.Results
[opts="header"]
|===
| s1Name | s1Type | s2Type | s1File | s2File
| player_id | INT32 | BYTE_ARRAY | players2.parquet | players3.parquet
| dob | INT32 | BYTE_ARRAY | players2.parquet | players3.parquet
| height | INT32 | BYTE_ARRAY | players2.parquet | players3.parquet
|===
## Conclusion
It turns out that computing Parquet schema diffs is another thing that DuckDB is really good at!
So give it a try the next time you have some mismatched schemas, but you don't know exactly what's not matching! | In this post we'll learn how to diff Apache Parquet schemas using DuckDB. | uploads/2022/11/parquet-diff-banner.png | [
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] | duckdb-diff-parquet-schema | https://markhneedham.com/blog/2022/11/17/duckdb-diff-parquet-schema |
false | 2022-11-14 02:44:37 | Apache Pinot: Unable to render templates on ingestion job spec template file | [
"pinot"
] | [
"pinot"
] | I was recently trying to ingest some JSON files into Apache Pinot from an S3 bucket and came across an exception when trying to pass a variable to the LaunchDataIngestionJob command
I was using the following ingestion job specification:
.config/job-spec.yml
[source, yaml]
----
executionFrameworkSpec:
name: 'standalone'
segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner'
segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner'
segmentUriPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentUriPushJobRunner'
jobType: SegmentCreationAndTarPush
inputDirURI: 's3://marks-st-cloud-bucket/events/'
includeFileNamePattern: 'glob:**/*.json'
outputDirURI: '/data'
overwriteOutput: true
pinotFSSpecs:
- scheme: s3
className: org.apache.pinot.plugin.filesystem.S3PinotFS
configs:
region: 'eu-west-2'
- scheme: file
className: org.apache.pinot.spi.filesystem.LocalPinotFS
recordReaderSpec:
dataFormat: 'json'
className: 'org.apache.pinot.plugin.inputformat.json.JSONRecordReader'
tableSpec:
tableName: 'events'
pinotClusterSpecs:
- controllerURI: 'http://${PINOT_CONTROLLER}:9000'
----
This job spec takes in the `PINOT_CONTROLLER` variable.
I initially ran the job like this:
[source, bash]
----
docker run \
--network ingest-json-files-s3 \
-v $PWD/config:/config \
-e AWS_ACCESS_KEY_ID=abc \
-e AWS_SECRET_ACCESS_KEY=def \
apachepinot/pinot:0.11.0 LaunchDataIngestionJob \
-jobSpecFile /config/job-spec.yml \
-values PINOT_CONTROLLER pinot-controller
----
Which resulted in the following error:
[source, text]
----
2022/11/10 11:49:43.030 ERROR [LaunchDataIngestionJobCommand] [main] Got exception to generate IngestionJobSpec for data ingestion job -
java.lang.RuntimeException: Unable to render templates on ingestion job spec template file - [/config/job-spec.yml] with propertiesMap - [[]].
at org.apache.pinot.spi.ingestion.batch.IngestionJobLauncher.getSegmentGenerationJobSpec(IngestionJobLauncher.java:85) ~[pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at org.apache.pinot.tools.admin.command.LaunchDataIngestionJobCommand.execute(LaunchDataIngestionJobCommand.java:111) [pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at org.apache.pinot.tools.Command.call(Command.java:33) [pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at org.apache.pinot.tools.Command.call(Command.java:29) [pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at picocli.CommandLine.executeUserObject(CommandLine.java:1953) [pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at picocli.CommandLine.access$1300(CommandLine.java:145) [pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at picocli.CommandLine$RunLast.executeUserObjectOfLastSubcommandWithSameParent(CommandLine.java:2352) [pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at picocli.CommandLine$RunLast.handle(CommandLine.java:2346) [pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at picocli.CommandLine$RunLast.handle(CommandLine.java:2311) [pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at picocli.CommandLine$AbstractParseResultHandler.execute(CommandLine.java:2179) [pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at picocli.CommandLine.execute(CommandLine.java:2078) [pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at org.apache.pinot.tools.admin.PinotAdministrator.execute(PinotAdministrator.java:165) [pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at org.apache.pinot.tools.admin.PinotAdministrator.main(PinotAdministrator.java:196) [pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
Caused by: groovy.lang.MissingPropertyException: No such property: PINOT_CONTROLLER for class: SimpleTemplateScript1
at org.codehaus.groovy.runtime.ScriptBytecodeAdapter.unwrap(ScriptBytecodeAdapter.java:66) ~[pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at org.codehaus.groovy.runtime.callsite.PogoGetPropertySite.getProperty(PogoGetPropertySite.java:51) ~[pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at org.codehaus.groovy.runtime.callsite.AbstractCallSite.callGroovyObjectGetProperty(AbstractCallSite.java:310) ~[pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at SimpleTemplateScript1.run(SimpleTemplateScript1.groovy:1) ~[?:?]
at groovy.text.SimpleTemplateEngine$SimpleTemplate$1.writeTo(SimpleTemplateEngine.java:181) ~[pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at groovy.text.SimpleTemplateEngine$SimpleTemplate$1.toString(SimpleTemplateEngine.java:193) ~[pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at org.apache.pinot.spi.utils.GroovyTemplateUtils.renderTemplate(GroovyTemplateUtils.java:45) ~[pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
at org.apache.pinot.spi.ingestion.batch.IngestionJobLauncher.getSegmentGenerationJobSpec(IngestionJobLauncher.java:82) ~[pinot-all-0.11.0-jar-with-dependencies.jar:0.11.0-1b4d6b6b0a27422c1552ea1a936ad145056f7033]
... 12 more
java.lang.RuntimeException: Unable to render templates on ingestion job spec template file - [/config/job-spec.yml] with propertiesMap - [[]].
at org.apache.pinot.spi.ingestion.batch.IngestionJobLauncher.getSegmentGenerationJobSpec(IngestionJobLauncher.java:85)
at org.apache.pinot.tools.admin.command.LaunchDataIngestionJobCommand.execute(LaunchDataIngestionJobCommand.java:111)
at org.apache.pinot.tools.Command.call(Command.java:33)
at org.apache.pinot.tools.Command.call(Command.java:29)
at picocli.CommandLine.executeUserObject(CommandLine.java:1953)
at picocli.CommandLine.access$1300(CommandLine.java:145)
at picocli.CommandLine$RunLast.executeUserObjectOfLastSubcommandWithSameParent(CommandLine.java:2352)
at picocli.CommandLine$RunLast.handle(CommandLine.java:2346)
at picocli.CommandLine$RunLast.handle(CommandLine.java:2311)
at picocli.CommandLine$AbstractParseResultHandler.execute(CommandLine.java:2179)
at picocli.CommandLine.execute(CommandLine.java:2078)
at org.apache.pinot.tools.admin.PinotAdministrator.execute(PinotAdministrator.java:165)
at org.apache.pinot.tools.admin.PinotAdministrator.main(PinotAdministrator.java:196)
Caused by: groovy.lang.MissingPropertyException: No such property: PINOT_CONTROLLER for class: SimpleTemplateScript1
at org.codehaus.groovy.runtime.ScriptBytecodeAdapter.unwrap(ScriptBytecodeAdapter.java:66)
at org.codehaus.groovy.runtime.callsite.PogoGetPropertySite.getProperty(PogoGetPropertySite.java:51)
at org.codehaus.groovy.runtime.callsite.AbstractCallSite.callGroovyObjectGetProperty(AbstractCallSite.java:310)
at SimpleTemplateScript1.run(SimpleTemplateScript1.groovy:1)
at groovy.text.SimpleTemplateEngine$SimpleTemplate$1.writeTo(SimpleTemplateEngine.java:181)
at groovy.text.SimpleTemplateEngine$SimpleTemplate$1.toString(SimpleTemplateEngine.java:193)
at org.apache.pinot.spi.utils.GroovyTemplateUtils.renderTemplate(GroovyTemplateUtils.java:45)
at org.apache.pinot.spi.ingestion.batch.IngestionJobLauncher.getSegmentGenerationJobSpec(IngestionJobLauncher.java:82)
... 12 more
----
I was kinda confused about this since I thought I had passed in this parameter.
It turns out that the library used to parse command line arguments expects variables to be specified in the format `-values variable=value` rather than `-values variable value`
So I fixed my command to read like the following:
[source, bash]
----
docker run \
--network ingest-json-files-s3 \
-v $PWD/config:/config \
-e AWS_ACCESS_KEY_ID=abc \
-e AWS_SECRET_ACCESS_KEY=def \
apachepinot/pinot:0.11.0 LaunchDataIngestionJob \
-jobSpecFile /config/job-spec.yml \
-values PINOT_CONTROLLER=pinot-controller
----
And now Pinot is happy with me again :) | In this post we'll learn about the render templates error when running an Apache Pinot ingestion job spec. | uploads/2022/11/render-templates-banner.png | [
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] | apache-pinot-unable-to-render-templates | https://markhneedham.com/blog/2022/11/14/apache-pinot-unable-to-render-templates |
false | 2022-11-22 02:44:37 | Blogging for Google: Why I write about error messages | [
"writing",
"blogging",
"devrel"
] | [
"Writing"
] | Writing blog posts that aim to go viral on social media is a well-known content strategy, but in this post, I want to persuade you that you should blog for Google as well.
== Blog for Google?
What does blogging for Google even mean?
The easiest demonstration is to look at a screenshot from the Google Console Insights report I was sent last week.
This section of the report shows the most used search terms that result in someone ending up on my blog.
.Google Console Insights
image::{{<siteurl>}}/uploads/2022/11/google-search-insights.png[Google Console Insights]
As you can see, 4 of the top 5 search terms are for error messages, and very specific ones at that.
I don't think I ever mentioned these blog posts on social media because they're just not that interesting to a general audience.
They are useful to a very specific audience though: people who have come across those error messages and don't know what to do next!
== When should I wrote about errors?
When I get an error message that I don't understand, the first thing that I do is paste it into Google to see if someone else has come across it.
If there's already a blog post or a StackOverflow answer that covers the exact same problem I'll probably move on, safe in the knowledge that this error message is already covered.
I should say that I sometimes find the answer to my problem under a completely different search term, perhaps buried in the comments of a GitHub issue.
In that case I might still write a post and link to the place where I found the answer.
== What goes in these posts?
Hopefully you're now slightly curious about writing about exceptions or error messages and in this section we're going to learn what these posts should contain.
They do tend to be quite short and I follow rougly this template:
. Describe what you were doing.
. Paste the entire error message that you received.
. Explain how you worked around or fixed the error message.
. Optionally, if you found the answer to your problem from somewhere, make sure to link to that place or credit that person.
The title of the post should contain the error message and/or the search term that you used/would use if you encountered this problem in future.
[NOTE]
====
Even if the error message indicates a bug with the product/software, it's still worth writing about because there'll be a period in time where people are still using the version of the software that has the bug!
I understand that you might not want to write about error messages in a product made by the company that employees you.
My response is that all products have bugs or throw exceptions - at least this way your users will know how to work around them.
====
Below are some recent examples of posts that I've written about error messages:
* https://www.markhneedham.com/blog/2022/11/14/apache-pinot-unable-to-render-templates/[Apache Pinot: Unable to render templates on ingestion job spec template file^]
* https://www.markhneedham.com/blog/2022/06/23/apache-pinot-skipping-periodic-task-pinot-task-manager/[Apache Pinot: Skipping periodic task: Task: PinotTaskManager^]
* https://www.markhneedham.com/blog/2022/04/05/apache-pinot-lookup-join-internal-error-unsupported-function/[Apache Pinot: Lookup Join - 500 Error - Unsupported function: lookup with 4 parameters^]
* https://www.markhneedham.com/blog/2020/12/31/materialize-unable-automatically-determine-timestamp-query/[Materialize: Unable to automatically determine a timestamp for your query; this can happen if your query depends on non-materialized sources^]
* https://www.markhneedham.com/blog/2020/03/31/streamlit-multiselect-numpy-no-attribute-index/[Streamlit: multiselect - AttributeError: 'numpy.ndarray' object has no attribute 'index'^]
Notice that none of these posts are all that long - by my calculations the maximum number of written words in any of them is ~300.
== The scratchpad
So far we've covered why you should write about error messages and what should go into a post about error messages, but how do we do it?
There's certainly a temptation when you come across error messages to try to solve them as quickly as possible and then move on.
I would encourage you (and me!) to be a bit more deliberate about it.
I try to keep some sort of scratchpad document open on my computer whenever I'm coding.
It could be a Google Doc or an empty text file, it doesn't matter.
It just needs to be easily accessible so that when you come across an error message, you can paste it into the file.
I'd also suggest adding some context around the error message, such as what you were doing when you got it and any search terms that you used when trying to figure it out.
When you (hopefully!) solve the problem, you can then copy/paste the context and the error message to your blogging platform, add in the solution, and you're done!
== In Summary
Write about error messages!
These are some of the easiest blog posts that you'll ever write and your users will thank you for it 😊
One of those users might even be your future self - I frequently come across my old blog posts when searching for error messages to the point where Google is almost acting like my external brain. | In this post we'll talk about the benefits of writing about error messages. | uploads/2022/11/blogging-for-google-banner.png | [
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] | blogging-for-google-error-messages | https://markhneedham.com/blog/2022/11/22/blogging-for-google-error-messages |
false | 2022-07-27 02:44:37 | Vercel: Redirect wildcard (nested) paths | [
"vercel"
] | [
"vercel"
] | We're deploying the StarTree developer site, https://dev.startree.ai/[dev.startree.ai^], to Vercel, and recently needed to do some redirects of a few pages.
I initially added https://nextjs.org/docs/api-reference/next.config.js/redirects[individual redirects^] for each page, but there were eventually too many pages and I wanted to automate it.
In this post we'll learn how to do that.
.Vercel: Redirect wildcard (nested) paths
image::{{<siteurl>}}/uploads/2022/07/vercel-nested-paths-banner.png[]
We wanted to redirect everything under https://dev.startree.ai/docs/thirdeye to https://dev.startree.ai/docs/startree-enterprise-edition/startree-thirdeye/ and started off by using wild card path matching, as seen in the `vercel.json` file below:
.vercel.json
[source, json]
----
{
"redirects": [
{
"source": "/docs/thirdeye/:path*",
"destination": "/docs/startree-enterprise-edition/startree-thirdeye/:path*",
"permanent": true
}
]
}
----
This worked for the top level paths and even ones nested to several levels, like https://dev.startree.ai/docs/thirdeye/concepts/root-cause-analysis:
[source, bash]
----
wget -S https://dev.startree.ai/docs/thirdeye/concepts/root-cause-analysis
----
.Output
[source, text]
----
--2022-07-27 10:26:17-- https://dev.startree.ai/docs/thirdeye/concepts/root-cause-analysis
Resolving dev.startree.ai (dev.startree.ai)... 76.76.21.164, 76.76.21.9
Connecting to dev.startree.ai (dev.startree.ai)|76.76.21.164|:443... connected.
HTTP request sent, awaiting response...
HTTP/1.1 308 Permanent Redirect
Cache-Control: public, max-age=0, must-revalidate
Content-Type: text/plain
Date: Wed, 27 Jul 2022 09:26:18 GMT
Location: /docs/startree-enterprise-edition/startree-thirdeye/concepts/root-cause-analysis
Refresh: 0;url=/docs/startree-enterprise-edition/startree-thirdeye/concepts/root-cause-analysis
Server: Vercel
Strict-Transport-Security: max-age=63072000
X-Vercel-Cache: MISS
X-Vercel-Id: lhr1::m8wct-1658913978046-3b7c6c4a96be
Transfer-Encoding: chunked
Location: /docs/startree-enterprise-edition/startree-thirdeye/concepts/root-cause-analysis [following]
--2022-07-27 10:26:18-- https://dev.startree.ai/docs/startree-enterprise-edition/startree-thirdeye/concepts/root-cause-analysis
Reusing existing connection to dev.startree.ai:443.
HTTP request sent, awaiting response...
HTTP/1.1 200 OK
----
I thought this was gonna be the solution, but then I checked a URL that had a trailing slash and the redirection didn't work anymore:
[source, bash]
----
wget -S https://dev.startree.ai/docs/thirdeye/concepts/root-cause-analysis/
----
.Output
[source, text]
----
--2022-07-27 10:25:23-- https://dev.startree.ai/docs/thirdeye/concepts/root-cause-analysis/
Resolving dev.startree.ai (dev.startree.ai)... 76.76.21.61, 76.76.21.241
Connecting to dev.startree.ai (dev.startree.ai)|76.76.21.61|:443... connected.
HTTP request sent, awaiting response...
HTTP/1.1 404 Not Found
----
A bit of searching led me to this https://github.com/vercel/next.js/issues/24288[GitHub issue^], which suggested that I needed to use the https://nextjs.org/docs/api-reference/next.config.js/rewrites#regex-path-matching[regex path matching^] syntax instead of wildcard path matching.
I updated my vercel.json file to look like this:
.vercel.json
[source, json]
----
{
"redirects": [
{
"source": "/docs/thirdeye/:path(.*)",
"destination": "/docs/startree-enterprise-edition/startree-thirdeye/:path",
"permanent": true
}
]
}
----
And now if we try to access the page with the trailing slash again:
[source, bash]
----
wget -S https://dev.startree.ai/docs/thirdeye/concepts/root-cause-analysis/
----
.Output
[source, text]
----
--2022-07-27 10:28:34-- https://dev.startree.ai/docs/thirdeye/concepts/root-cause-analysis/
Resolving dev.startree.ai (dev.startree.ai)... 76.76.21.93, 76.76.21.98
Connecting to dev.startree.ai (dev.startree.ai)|76.76.21.93|:443... connected.
HTTP request sent, awaiting response...
HTTP/1.1 308 Permanent Redirect
Cache-Control: public, max-age=0, must-revalidate
Content-Type: text/plain
Date: Wed, 27 Jul 2022 09:28:34 GMT
Location: /docs/startree-enterprise-edition/startree-thirdeye/concepts/root-cause-analysis/
Refresh: 0;url=/docs/startree-enterprise-edition/startree-thirdeye/concepts/root-cause-analysis/
Server: Vercel
Strict-Transport-Security: max-age=63072000
X-Vercel-Cache: MISS
X-Vercel-Id: lhr1::rz74m-1658914114559-78a83552b924
Transfer-Encoding: chunked
Location: /docs/startree-enterprise-edition/startree-thirdeye/concepts/root-cause-analysis/ [following]
--2022-07-27 10:28:34-- https://dev.startree.ai/docs/startree-enterprise-edition/startree-thirdeye/concepts/root-cause-analysis/
Reusing existing connection to dev.startree.ai:443.
HTTP request sent, awaiting response...
HTTP/1.1 200 OK
----
Success! | In this post we'll learn how to do wildcard redirects for nested paths in Vercel. | uploads/2022/07/vercel-nested-paths-banner.png | [
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] | vercel-redirect-wildcards-nested-paths | https://markhneedham.com/blog/2022/07/27/vercel-redirect-wildcards-nested-paths |
false | 2022-07-11 02:44:37 | Docusaurus: Side menu on custom page | [
"docusaurus"
] | [
"docusaurus"
] | I've been working with Docusaurus to build the https://dev.startree.ai/[dev.startree.ai] website over the last few months and I wanted to add https://docusaurus.io/docs/creating-pages[a custom page^] with a sidebar similar to the one that gets automatically generated on documentation pages.
All the examples I could find showed you to create a splash page, so it took me a while to figure out how to do what I wanted, but in this post we'll learn how to do it.
.Docusaurus: Side menu on custom page
image::{{<siteurl>}}/uploads/2022/07/docusaurus-banner.png[]
First, let's create a Docusaurus site called `my-website` by running the following command:
[source, bash]
----
npx create-docusaurus@latest my-website classic
----
We can run the site locally with the following command:
[source, bash]
----
npm run start
----
This will run a local webserver at http://localhost:3000, which will show the following page:
.Docusaurus
image::{{<siteurl>}}/uploads/2022/07/docusaurus.png[]
Custom pages go under `src/pages` and whatever we name our file will be the name of the page.
Let's create `myCustomPage.js` with the following contents:
.src/pages/myCustomPage.js
[source, js,indent=2]
----
include::content/2022/07/11/myCustomPage.js[]
----
If we want to adjust the items show in the side bar we'd need to change the `DocSidebar` component's `sidebar` property.
Most of the code shown here describes CSS styles that do change location between minor versions.
I'll try to keep the example up to date with the latest version.
[NOTE]
====
The version of the docusaurus at the time of writing this blog post is 2.0.0-beta.22.
====
The custom page is created at http://localhost:3000/myCustomPage and looks like this:
.Docusaurus Custom Page
image::{{<siteurl>}}/uploads/2022/07/docusaurus-myCustomPage.png[] | In this post we'll learn how to add a side menu to a custom page with the Docusaurus static website generator. | uploads/2022/07/docusaurus-banner.png | [
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] | docusaurus-side-menu-custom-page | https://markhneedham.com/blog/2022/07/11/docusaurus-side-menu-custom-page |
false | 2022-07-21 02:44:37 | Apache Pinot: Import JSON data from a CSV file - Illegal Json Path: $['id'] does not match document | [
"pinot"
] | [
"pinot"
] | I've been working on an Apache Pinot dataset where I ingested a JSON document stored in a CSV file.
I made a mistake with the representation of the JSON and it took me a while to figure out what I'd done wrong.
We'll go through it in this blog post.
.Apache Pinot: Import JSON data from a CSV file - Illegal Json Path: $['id'] does not match document
image::{{<siteurl>}}/uploads/2022/07/json-banner.png[]
== Setup
We're going to spin up a local instance of Pinot and Kafka using the following Docker compose config:
.docker-compose.yml
[source, json]
----
include::content/2022/07/21/docker-compose.yml[]
----
We can launch all the components by running the following command:
[source, bash]
----
docker-compose up
----
== Schema and Table
We're going to be using the following schema:
.config/schema.json
[source, json]
----
{
"schemaName": "users",
"dimensionFieldSpecs": [
{
"name": "json_field",
"dataType": "JSON"
}
],
"dateTimeFieldSpecs": [
{
"name": "timestamp_field",
"dataType": "TIMESTAMP",
"format": "1:MILLISECONDS:EPOCH",
"granularity": "1:MILLISECONDS"
}
]
}
----
And this table config:
.config/table.json
[source, json]
----
{
"tableName": "users",
"tableType": "OFFLINE",
"segmentsConfig": {
"replication": 1,
"schemaName": "users",
"timeColumnName": "timestamp_field"
},
"tenants": {},
"tableIndexConfig": {},
"ingestionConfig": {},
"metadata": {}
}
----
We can create them both by running the following command:
[source, bash]
----
docker exec -it pinot-controller bin/pinot-admin.sh AddTable \
-tableConfigFile /config/table.json \
-schemaFile /config/schema.json \
-exec
----
== Importing CSV file
Next we're going to import the following CSV file:
.data/output.csv
[format="csv", options="header"]
|===
include::content/2022/07/21/data/output-broken.csv[]
|===
We'll do this using this job spec:
.config/job-spec.yml
[source, yml]
----
executionFrameworkSpec:
name: 'standalone'
segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner'
segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner'
jobType: SegmentCreationAndTarPush
inputDirURI: '/data'
includeFileNamePattern: 'glob:**/output.csv'
outputDirURI: '/opt/pinot/data/crimes/'
overwriteOutput: true
pinotFSSpecs:
- scheme: file
className: org.apache.pinot.spi.filesystem.LocalPinotFS
recordReaderSpec:
dataFormat: 'csv'
className: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReader'
configClassName: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReaderConfig'
tableSpec:
tableName: 'users'
pinotClusterSpecs:
- controllerURI: "http://localhost:9000"
----
The following command will ingest the CSV file:
[source, bash]
----
docker exec -it pinot-controller bin/pinot-admin.sh LaunchDataIngestionJob \
-jobSpecFile /config/job-spec.yml
----
== Querying the JSON Field
Now it's time to write a query that pulls out the `id` field from `json_field`, which we can do with this query:
[source, sql]
----
select json_extract_scalar(json_field, '$.id', 'STRING') AS id,
json_field
from users
----
.Output
[source, text]
----
[
{
"message": "QueryExecutionError:\njava.lang.IllegalArgumentException: Illegal Json Path: $['id'] does not match document\n\tat org.apache.pinot.core.common.evaluators.DefaultJsonPathEvaluator.throwPathNotFoundException(DefaultJsonPathEvaluator.java:613)\n\tat org.apache.pinot.core.common.evaluators.DefaultJsonPathEvaluator.processValue(DefaultJsonPathEvaluator.java:540)\n\tat org.apache.pinot.core.common.evaluators.DefaultJsonPathEvaluator.evaluateBlock(DefaultJsonPathEvaluator.java:250)\n\tat org.apache.pinot.core.common.DataFetcher$ColumnValueReader.readStringValues(DataFetcher.java:594)",
"errorCode": 200
}
]
----
Hmm, that didn't quite work.
Let's have a look at the contents of `json_field`:
[source, sql]
----
select json_field
from users
----
.Results
[options="header"]
|===
|json_field
|"{\"id\": 7886, \"details\": {\"collaborator\": \"Brett Gill\", \"score\": 6056, \"address\": \"2882 Sheila Lakes Apt. 264\\nRhondaville, KS 09803\"}}"
|===
We can see from the output that we've actually got a string in this field rather than a JSON document, which is why the JSON path query doesn't work.
We'll need to reimport the data after fixing the JSON field, as shown in the CSV file below:
.data/output.csv
[format="csv", options="header"]
|===
include::content/2022/07/21/data/output-working.csv[]
|===
Once we've done that we can run the following query again:
[source, sql]
----
select json_extract_scalar(json_field, '$.id', 'STRING') AS id,
json_field
from users
----
.Results
[options="header"]
|===
|id | json_field
|id json_field
|8360| {"id":8360,"details":{"collaborator":"Mckenzie Brown","score":1384,"address":"68131 Robinson Vista\nChristianport, HI 60353"}}
|===
Success! | In this post we'll learn how to solve a problem when ingesting JSON data from a CSV file into Apache Pinot. | uploads/2022/07/json-banner.png | [
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] | apache-pinot-import-json-data-csv-file-illegal-json-path-does-not-match-document | https://markhneedham.com/blog/2022/07/21/apache-pinot-import-json-data-csv-file-illegal-json-path-does-not-match-document |
false | 2022-01-20 02:44:37 | Apache Pinot: Sorted indexes on real-time tables | [
"pinot"
] | [
"Pinot"
] | I've recently been learning all about Apache Pinot's sorted forward indexes, and in my first blog post I explained https://www.markhneedham.com/blog/2022/01/19/apache-pinot-sorted-indexes-offline-tables/[how they work for offline tables].
In this blog post we'll learn how sorted indexes work with real-time tables.
.Apache Pinot: Sorted indexes on real-time tables
image::{{<siteurl>}}/uploads/2022/01/sorted-indexes-realtime-banner.png[]
== Launch Components
We're going to spin up a local instance of Pinot and Kafka using the following Docker compose config:
.docker-compose.yml
[source, yaml]
----
version: '3.7'
services:
zookeeper:
image: zookeeper:3.5.6
hostname: zookeeper
container_name: zookeeper-strava-realtime
ports:
- "2181:2181"
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ZOOKEEPER_TICK_TIME: 2000
kafka:
image: wurstmeister/kafka:latest
restart: unless-stopped
container_name: "kafka-strava"
ports:
- "9092:9092"
expose:
- "9093"
depends_on:
- zookeeper
environment:
KAFKA_ZOOKEEPER_CONNECT: zookeeper-strava-realtime:2181/kafka
KAFKA_BROKER_ID: 0
KAFKA_ADVERTISED_HOST_NAME: kafka-strava
KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka-strava:9093,OUTSIDE://localhost:9092
KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9093,OUTSIDE://0.0.0.0:9092
KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,OUTSIDE:PLAINTEXT
pinot-controller:
image: apachepinot/pinot:0.9.3
command: "StartController -zkAddress zookeeper-strava-realtime:2181 -dataDir /data"
container_name: "pinot-controller-strava-realtime"
volumes:
- ./config:/config
- ./data-realtime:/data
restart: unless-stopped
ports:
- "9000:9000"
depends_on:
- zookeeper
pinot-broker:
image: apachepinot/pinot:0.9.3
command: "StartBroker -zkAddress zookeeper-strava-realtime:2181"
restart: unless-stopped
container_name: "pinot-broker-strava-realtime"
ports:
- "8099:8099"
depends_on:
- pinot-controller
pinot-server:
image: apachepinot/pinot:0.9.3
command: "StartServer -zkAddress zookeeper-strava-realtime:2181"
restart: unless-stopped
container_name: "pinot-server-strava-realtime"
depends_on:
- pinot-broker
----
We can launch all the components by running the following command:
[source, bash]
----
docker-compose up
----
=== Create Schema
We're going to explore sorted indexes using a dataset of my Strava activities, the same one that we used in the first blog post.
The schema is described below:
./config/schema.json
[source, json]
----
include::content/2022/01/19/config/schema.json[]
----
We can create the schema by running the following command:
[source, bash]
----
docker exec -it pinot-controller-strava-realtime bin/pinot-admin.sh AddSchema \
-schemaFile /config/schema.json -exec
----
== What is a sorted index?
Before we ingest any data, let's remind ourselves about the definition of a sorted index.
[quote, Sorted forward index with run-length encoding, 'https://docs.pinot.apache.org/basics/indexing/forward-index#sorted-forward-index-with-run-length-encoding[docs.pinot.apache.org/basics/indexing/forward-index#sorted-forward-index-with-run-length-encoding]']
_____
When a column is physically sorted, Pinot uses a sorted forward index with run-length encoding on top of the dictionary-encoding.
Instead of saving dictionary ids for each document id, Pinot will store a pair of start and end document ids for each value.
_____
A diagram showing how a sorted index works conceptually is shown below:
.Sorted Forward Index
image::{{<siteurl>}}/uploads/2022/01/sorted-forward.png[width="500px"]
The advantage of having a sorted index is that queries that filter by that column will be more performant since the query engine doesn't need to scan through every document to check if they match the filtering criteria.
For real-time tables we can specify a sorted index in the `tableIndexConfig`:
[source, json]
----
{
"tableIndexConfig": {
"sortedColumn": [
"column_name"
],
}
}
----
When Pinot commits the segment it will ensure that the data is sorted based on this sorted column.
It will then do a single pass over every other column to check whether the data in those columns is sorted.
Columns that contain sorted data will also use a sorted forward index.
This means that it's possible that multiple columns will use a sorted index even though one column is guaranteed to use a sorted index.
[NOTE]
====
Sorted indexes are determined for each segment.
This means that a column could be sorted in one segment, but not in another one.
====
== Data Ingestion into Kafka
Now let's ingest some data into a Kafka topic.
First let's install the Confluent Kafka client:
[source, bash]
----
pip install confluent-kafka
----
And now we'll import some documents into the `activities-realtime` topic.
Import the following libraries:
[source, python]
----
import json
from confluent_kafka import Producer
----
Define an acknowledge function and configure our Kafka producer:
[source, python]
----
def acked(err, msg):
if err is not None:
print("Failed to deliver message: {0}: {1}".format(msg.value(), err.str()))
producer = Producer({'bootstrap.servers': 'localhost:9092'})
----
And finally write some messages to the topic:
[source, python]
----
points = [
{'lat': '56.265595', 'lon': '12.859432', 'id': '2776420839', 'distance': '1.5', 'altitude': '11.2', 'hr': '88',
'cadence': '0', 'time': '2019-10-09 21:25:25+00:00', 'rawTime': '0'},
{'lat': '56.265566', 'lon': '12.859438', 'id': '2776420839', 'distance': '4.6', 'altitude': '11.3', 'hr': '89',
'cadence': '79', 'time': '2019-10-09 21:25:27+00:00', 'rawTime': '2'},
{'lat': '56.265503', 'lon': '12.859488', 'id': '2776420839', 'distance': '12.2', 'altitude': '11.4', 'hr': '92',
'cadence': '79', 'time': '2019-10-09 21:25:30+00:00', 'rawTime': '5'},
{'lat': '56.265451', 'lon': '12.85952', 'id': '2776420839', 'distance': '18.4', 'altitude': '11.4', 'hr': '97',
'cadence': '83', 'time': '2019-10-09 21:25:32+00:00', 'rawTime': '7'},
{'lat': '56.26558', 'lon': '12.85943', 'id': '2776420839', 'distance': '3.1', 'altitude': '11.2', 'hr': '89',
'cadence': '79', 'time': '2019-10-09 21:25:26+00:00', 'rawTime': '1'}
]
for point in points:
payload = json.dumps(point, ensure_ascii=False).encode('utf-8')
producer.produce(topic='activities-realtime', key=str(point['id']), value=payload, callback=acked)
producer.flush()
----
We can check that those messages have reached the topic by running the following command:
[source,bash]
----
docker exec -it kafka-strava kafka-console-consumer.sh \
--bootstrap-server localhost:9092 \
--topic activities-realtime \
--from-beginning
----
.Output
[source, text]
----
{"lat": "56.265595", "lon": "12.859432", "id": "2776420839", "distance": "1.5", "altitude": "11.2", "hr": "88", "cadence": "0", "time": "2019-10-09 21:25:25+00:00", "rawTime": "0"}
{"lat": "56.265566", "lon": "12.859438", "id": "2776420839", "distance": "4.6", "altitude": "11.3", "hr": "89", "cadence": "79", "time": "2019-10-09 21:25:27+00:00", "rawTime": "2"}
{"lat": "56.265503", "lon": "12.859488", "id": "2776420839", "distance": "12.2", "altitude": "11.4", "hr": "92", "cadence": "79", "time": "2019-10-09 21:25:30+00:00", "rawTime": "5"}
{"lat": "56.265451", "lon": "12.85952", "id": "2776420839", "distance": "18.4", "altitude": "11.4", "hr": "97", "cadence": "83", "time": "2019-10-09 21:25:32+00:00", "rawTime": "7"}
{"lat": "56.26558", "lon": "12.85943", "id": "2776420839", "distance": "3.1", "altitude": "11.2", "hr": "89", "cadence": "79", "time": "2019-10-09 21:25:26+00:00", "rawTime": "1"}
Processed a total of 5 messages
----
It's looking good so far.
== Unsorted Table
Now let's create a real-time table called `activities_realtime` to ingest the data from that Kafka topic into Pinot.
This table doesn't specify a sorted column.
./config/table-realtime.json
[source, json]
----
include::content/2022/01/20/config/table-realtime.json[]
----
[WARNING]
====
The `realtime.segment.flush.threshold.rows` config is intentionally set to an extremely small value so that the segment will be committed after 5 records have been ingested.
In a production system this value should be set much higher, as described in the https://docs.pinot.apache.org/operators/operating-pinot/tuning/realtime#fine-tuning-the-segment-commit-protocol[real time tuning guide^].
====
We can create a table by running the following command:
[source, bash]
----
docker exec -it pinot-controller-strava-realtime bin/pinot-admin.sh AddTable \
-tableConfigFile /config/table-realtime.json \
-exec
----
Once we create this table, Pinot will start ingesting data from the Kafka topic.
We can check which segments have been created by navidating to http://localhost:9000/#/tenants/table/activities_realtime_REALTIME.
You should see something like the following:
.Segments
image::{{<siteurl>}}/uploads/2022/01/realtime-segments.png[]
If we click on the first segment, we'll see the following:
.Segment 0
image::{{<siteurl>}}/uploads/2022/01/segment0.png[]
We can see that this segment has been committed by looking at the `segment.realtime.status` property on the right hand side.
The `segment.total.docs` property tells us that this segment contains 5 documents.
Now let's go back and then click the other segment.
If we do that we'll see the following:
.Segment 1
image::{{<siteurl>}}/uploads/2022/01/segment1.png[]
This one is `IN_PROGRESS` and if add some more messages to the Kafka topic they will go into this segment.
Now we're going to check on the sorted status of the columns for all committed segments i.e segment `activities_realtime__0__1__20220119T1541Z` in this case.
First let's collect all the columns in the `activities` schema:
[source, bash]
----
export queryString=`curl -X GET "http://localhost:9000/schemas/activities" -H "accept: application/json" 2>/dev/null |
jq -r '[.dimensionFieldSpecs,.dateTimeFieldSpecs | .[] | .name ] | join("&columns=")'`
----
And now let's call the _getServerMetaData_ endpoint to return the segments for the `activities_realtime` table and filter the response to get the segment name, input file, and column names with sorted status:
[source, bash]
----
curl -X GET "http://localhost:9000/segments/activities_realtime/metadata?columns=${queryString}" \
-H "accept: application/json" 2>/dev/null |
jq -c '.[] | select(.columns != null) | {
segment: .segmentName,
columns: .columns | map({(.columnName): .sorted})
}'
----
[NOTE]
====
To refresh our minds, the data in the table was imported in the order shown in the table below:
.activities_realtime
[format="csv", options="header", cols="10,10,10,10,10,10,8,22,10"]
|===
include::content/2022/01/20/data/points.csv[]
|===
====
If we run this command, we'll see the following output:
.Output
[source, json]
----
{
"segment": "activities_realtime__0__0__20220119T1541Z",
"importedFrom": null,
"columns": [
{"altitude": false},
{"distance": false},
{"hr": false},
{"lon": false},
{"cadence": false},
{"rawTime": false},
{"location": false},
{"id": true},
{"lat": false},
{"timestamp": false}
]
}
----
From the output we can see that almost all of the columns aren't sorted.
The only sorted column is `id` and that's because we only have one unique value in that column.
== Sorted Table
Now let's create a real-time table called `activities_realtime_sorted` that specifies `timestamp` as a sorted column.
./config/table-realtime.json
[source, json]
----
include::content/2022/01/20/config/table-realtime-sorted.json[]
----
We can create a table by running the following command:
[source, bash]
----
docker exec -it pinot-controller-strava-realtime bin/pinot-admin.sh AddTable \
-tableConfigFile /config/table-realtime-.json \
-exec
----
We can call the _getSegments_ endpoint to check which segments have been created for this table:
[source, bash]
----
curl -X GET "http://localhost:9000/segments/activities_realtime_sorted" -H "accept: application/json" 2>/dev/null
----
.Output
[source, json]
----
[
{
"REALTIME": [
"activities_realtime_sorted__0__0__20220120T0952Z",
"activities_realtime_sorted__0__1__20220120T0952Z"
]
}
]
----
We can then call the `getSegmentMetadata` endpoint to return the metadata for each of these segments:
[source, bash]
----
segments=`curl -X GET "http://localhost:9000/segments/activities_realtime_sorted" \
-H "accept: application/json" 2>/dev/null |
jq -r '.[] | .REALTIME[]'`
for segment in ${segments}; do
curl -X GET "http://localhost:9000/segments/activities_realtime_sorted/${segment}/metadata" \
-H "accept: application/json" 2>/dev/null | jq '.'
done
----
.Output
[source, json]
----
{
"segment.crc": "3864673434",
"segment.creation.time": "1642672360783",
"segment.end.time": "1570656332000",
"segment.flush.threshold.size": "5",
"segment.index.version": "v3",
"segment.name": "activities_realtime_sorted__0__0__20220120T0952Z",
"segment.realtime.download.url": "http://172.21.0.3:9000/segments/activities_realtime_sorted/activities_realtime_sorted__0__0__20220120T0952Z",
"segment.realtime.endOffset": "5",
"segment.realtime.numReplicas": "1",
"segment.realtime.startOffset": "0",
"segment.realtime.status": "DONE",
"segment.start.time": "1570656325000",
"segment.table.name": "activities_realtime_sorted",
"segment.time.unit": "MILLISECONDS",
"segment.total.docs": "5",
"segment.type": "REALTIME"
}
{
"segment.creation.time": "1642672361525",
"segment.flush.threshold.size": "5",
"segment.name": "activities_realtime_sorted__0__1__20220120T0952Z",
"segment.realtime.numReplicas": "1",
"segment.realtime.startOffset": "5",
"segment.realtime.status": "IN_PROGRESS",
"segment.table.name": "activities_realtime_sorted",
"segment.type": "REALTIME"
}
----
From this output we learn that segment `activities_realtime_sorted__0__0__20220120T0952Z` has already been committed and segment `activities_realtime_sorted__0__1__20220120T0952Z` is still in progress.
Any new records will be added to segment `activities_realtime_sorted__0__1__20220120T0952Z`.
Now let's check on the sorted status of columns in these segments using the _getServerMetaData_ endpoint:
[source, bash]
----
curl -X GET "http://localhost:9000/segments/activities_realtime_sorted/metadata?columns=${queryString}" \
-H "accept: application/json" 2>/dev/null |
jq -c '.[] | select(.columns != null) | {
segment: .segmentName,
columns: .columns | map({(.columnName): .sorted})
}'
----
[NOTE]
====
The table below shows the effective order of the data in the segment when it was committed:
.activities_realtime_sorted
[format="csv", options="header", cols="10,10,10,10,10,10,8,22,10"]
|===
include::content/2022/01/20/data/points-sorted.csv[]
|===
====
If we run this command, we'll see the following output:
.Output
[source, json]
----
{
"segment": "activities_realtime_sorted__0__0__20220120T0952Z",
"columns": [
{"altitude": true},
{"distance": true},
{"hr": true},
{"lon": false},
{"cadence": true},
{"rawTime": true},
{"location": false},
{"id": true},
{"lat": false},
{"timestamp": true}
]
}
----
Now the _timestamp_ field is sorted, which is what we would expect, along with a bunch of other fields as well.
It's unlikely that any of the other fields would remain sorted if we we imported any records.
_distance_ and _rawTime_ are correlated with _timestamp_ within a single activity, but if a segment contained multiple contained multiple activities that correlation would be lost.
== Conclusion
That's the end of this mini blog series about Pinot's sorted indexes.
Hopefully it all made sense, but if not feel free to ask any questions on the https://communityinviter.com/apps/apache-pinot/apache-pinot[Pinot Community Slack^]. | In this post we'll learn all about sorted indexes on offline tables in Apache Pinot. | uploads/2022/01/sorted-indexes-realtime-banner.png | [
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] | apache-pinot-sorted-indexes-realtime-tables | https://markhneedham.com/blog/2022/01/20/apache-pinot-sorted-indexes-realtime-tables |
false | 2022-01-18 00:44:37 | Strava: Export and interpolate lat/long points for an activity | [
"python",
"strava"
] | [
"python"
] | :icons: font
I've been working with https://www.markhneedham.com/blog/tag/strava/[Strava^] data again recently and wanted to extract all the lat/long coordinates recorded for my runs.
Having done this, I realised that my running watch hadn't recorded as many points as I expected, so I needed to interpolate the missing points.
In this blog post we'll learn how to do that.
.Strava: Export and interpolate lat/long points for an activity
image::{{<siteurl>}}/uploads/2022/01/strava-banner.png[]
== Setup
Let's first install a few libraries that we'll be using:
[source, bash]
----
pip install stravalib pandas pyproj
----
Once we've installed those library, let's import them into our Python script:
[source, python]
----
import pickle
import time
import pandas as pd
from datetime import timedelta
----
== Creating the Strava client
First we'll need to create a Strava client using the stravalib library.
This involves generating an access token, which I showed how to do in my blog post titled https://www.markhneedham.com/blog/2020/12/20/strava-export-all-activities-json/[Strava: Export all activities to JSON file].
We'll assume that's already been done and the client has been serialised to `auth/client.pkl`.
We can load the client by running the following code:
[source, python]
----
def load_object(filename):
with open(filename, 'rb') as input:
loaded_object = pickle.load(input)
return loaded_object
client = load_object('auth/client.pkl')
----
== Exporting lat/long points
Now that we've got that setup, it's time to export those lat/long points
We'll use https://www.strava.com/activities/3092741860[one of my runs^] from my trip to Sweden for the Neo4j Engineering Offsite in February 2020, back in the good old pre-Covid days.
We can get the lat/long points for an activity from the https://pythonhosted.org/stravalib/api.html#stravalib.client.Client.get_activity_streams[get_activity_streams^] function, as shown in the following code sample:
[source, python]
----
id = 3092741860
response = client.get_activity(id)
start = response.start_date
response = client.get_activity_streams(id, ["distance", "latlng", "time", "heartrate", "cadence", "altitude"])
latlong = response["latlng"].data
time_list = response["time"].data
altitude = response["altitude"].data
hr = response["heartrate"].data if response.get("heartrate") else 0
cadence = response["cadence"].data if response.get("cadence") else 0
data = pd.DataFrame([*latlong], columns=['lat','lon'])
data["id"] = id
data["distance"] = response["distance"].data
data['altitude'] = altitude
data['hr'] = hr
data['cadence'] = cadence
data['time'] = [(start+timedelta(seconds=t)) for t in time_list]
data['rawTime'] = time_list
data.to_csv("points.csv", index=False)
----
Below are the first few lines of this file:
.points.csv
[format="csv", options="header", cols="10,10,10,10,10,10,10,20,10"]
|===
include::content/2022/01/18/data/points.csv[lines=1..6]
|===
There are 914 points over 86 minutes, or just over 10 points per minute.
I want to have one point per second, or around 5,160 points for this activity.
== Interpolating points
To achieve this we're going to generate/interpolate the missing points.
We can do this by computing equally spaced lat/long coordinates in between the points that we do have using the https://pyproj4.github.io/pyproj/stable/[Pyproj library^].
[NOTE]
====
I learnt about the Pyproj library from https://gis.stackexchange.com/questions/311362/interpolating-orthodrome-between-two-lon-lat-points-in-python[Felix's StackOverflow answer^].
====
Let's learn how to use the library on a couple of the lat/longs from the dataset.
We'll have the https://pyproj4.github.io/pyproj/stable/api/geod.html?highlight=npts#pyproj.Geod.npts[`npts`^] function compute two points in between the provided points:
[source, python]
----
from pyproj import Geod
geoid = Geod(ellps="WGS84")
point1 = (56.434604, 12.838070)
point2 = (56.434625, 12.838106)
points_to_generate = 2
extra_points = geoid.npts(point1[0], point1[1], point2[0], point2[1], points_to_generate)
for point in extra_points:
print(point)
----
.Output
[source, text]
----
(56.434610999999336, 12.838082000000197)
(56.43461799999933, 12.838094000000197)
----
So far, so good.
We can now use this approach to compute missing points for the whole run.
We'll first extract the lat/long points and the rawTime value into a list of tuples:
[source, python]
----
import csv
from pyproj import Geod
points = []
with open("points.csv", "r") as event_file:
reader = csv.reader(event_file, delimiter=",")
next(reader, None) # skip the headers
for row in reader:
lat = float(row[0])
lon = float(row[1])
points.append((lat, lon, int(row[8])))
for point in points[0:5]:
print(point)
----
.Output
[source, text]
----
(56.434599, 12.838058, 0)
(56.434604, 12.83807, 1)
(56.434625, 12.838106, 4)
(56.434717, 12.838408, 11)
(56.434718, 12.838463, 12)
----
And now let's fill in the missing points:
[source, python]
----
geoid = Geod(ellps="WGS84")
all_points = []
for point1, point2 in zip(points, points[1:]):
missing_points = (point2[2] - point1[2]) <1>
if missing_points > 0:
extra_points = geoid.npts(point1[0], point1[1], point2[0], point2[1], missing_points)
all_points.append(point1)
all_points.extend([extra_points[idx] + (point1[2] + idx,) for idx in range(1,len(extra_points))]) <2>
all_points.append(points[-1])
for point in all_points[0:13]:
print(point)
----
<1> How many points to generate
<2> Add the new points along with the number of seconds into the activity
Below are the combined actual points and interpolate points up to the 12th second:
.Output
[source, text]
----
(56.434599, 12.838058, 0)
(56.434604, 12.83807, 1)
(56.43461449999925, 12.838088000000225, 2)
(56.43461974999944, 12.838097000000166, 3)
(56.434625, 12.838106, 4)
(56.434647999979404, 12.838181500003664, 5)
(56.43465949997426, 12.838219250004581, 6)
(56.434670999972546, 12.838257000004889, 7)
(56.43468249997426, 12.838294750004582, 8)
(56.43469399997941, 12.838332500003668, 9)
(56.43470549998799, 12.83837025000214, 10)
(56.434717, 12.838408, 11)
(56.434718, 12.838463, 12)
---- | In this post we'll learn how to export and interpolate the lat/long points for a Strava activity. | uploads/2022/01/strava-banner.png | [
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] | strava-export-interpolate-lat-long-points-activity | https://markhneedham.com/blog/2022/01/18/strava-export-interpolate-lat-long-points-activity |
false | 2022-01-19 00:44:37 | Apache Pinot: Sorted indexes on offline tables | [
"pinot"
] | [
"Pinot"
] | I've recently been learning all about Apache Pinot's sorted forward indexes.
I was initially going to explain how they work for offline and real-time tables, but the post got a bit long, so instead we'll have two blog posts.
In this one we'll learn how sorted indexes are applied for offline tables.
.Apache Pinot: Sorted indexes on offline tables
image::{{<siteurl>}}/uploads/2022/01/sorted-indexes-offline-banner.png[]
== Launch Components
We're going to spin up a local instance of Pinot using the following Docker compose config:
.docker-compose.yml
[source, yaml]
----
version: '3.7'
services:
zookeeper:
image: zookeeper:3.5.6
hostname: zookeeper
container_name: zookeeper-strava
ports:
- "2181:2181"
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ZOOKEEPER_TICK_TIME: 2000
pinot-controller:
image: apachepinot/pinot:0.9.3
command: "StartController -zkAddress zookeeper-strava:2181 -dataDir /data"
container_name: "pinot-controller-strava"
volumes:
- ./config:/config
- ./data:/data
- ./input:/input
restart: unless-stopped
ports:
- "9000:9000"
depends_on:
- zookeeper
pinot-broker:
image: apachepinot/pinot:0.9.3
command: "StartBroker -zkAddress zookeeper-strava:2181"
restart: unless-stopped
container_name: "pinot-broker-strava"
ports:
- "8099:8099"
depends_on:
- pinot-controller
pinot-server:
image: apachepinot/pinot:0.9.3
command: "StartServer -zkAddress zookeeper-strava:2181"
restart: unless-stopped
container_name: "pinot-server-strava"
depends_on:
- pinot-broker
----
We can launch all the components by running the following command:
[source, bash]
----
docker-compose up
----
== Create Schema
We're going to explore sorted indexes using a dataset of my Strava activities.
We'll be using the following schema:
./config/schema.json
[source, json]
----
include::content/2022/01/19/config/schema.json[]
----
We can create the schema by running the following command:
[source, bash]
----
docker exec -it pinot-controller-strava bin/pinot-admin.sh AddSchema \
-schemaFile /config/schema.json -exec
----
== Create Table
Now let's create an offline table based on that schema.
./config/table-offline.json
[source, json]
----
include::content/2022/01/19/config/table-offline.json[]
----
We can create a table by running the following command:
[source, bash]
----
docker exec -it pinot-controller-strava bin/pinot-admin.sh AddTable \
-tableConfigFile /config/table-offline.json \
-exec
----
== What is a sorted index?
Before we ingest any data, let's remind ourselves about the definition of a sorted index.
[quote, Sorted forward index with run-length encoding, 'https://docs.pinot.apache.org/basics/indexing/forward-index#sorted-forward-index-with-run-length-encoding[docs.pinot.apache.org/basics/indexing/forward-index#sorted-forward-index-with-run-length-encoding]']
_____
When a column is physically sorted, Pinot uses a sorted forward index with run-length encoding on top of the dictionary-encoding.
Instead of saving dictionary ids for each document id, Pinot will store a pair of start and end document ids for each value.
_____
A diagram showing how a sorted index works conceptually is shown below:
.Sorted Forward Index
image::{{<siteurl>}}/uploads/2022/01/sorted-forward.png[width="500px"]
The advantage of having a sorted index is that queries that filter by that column will be more performant since the query engine doesn't need to scan through every document to check if they match the filtering criteria.
When creating a segment Pinot does a single pass over every column to check whether the data is sorted.
Columns that contain sorted data will use a sorted forward index.
[NOTE]
====
Sorted indexes are determined for each segment.
This means that a column could be sorted in one segment, but not in another one.
====
== Ingesting Data
Now let's import some data into our table.
We'll be importing data from CSV files that contain some of the lat/longs from a few of my Strava runs.
`activity1.csv` contains the first 5 recorded points from one run:
.activity1.csv
[format="csv", options="header", cols="10,10,10,10,10,10,10,20,10"]
|===
include::content/2022/01/19/input/activity1.csv[]
|===
And `activity2.csv` contains 5 recorded points from two different runs:
.activity2.csv
[format="csv", options="header", cols="10,10,10,10,10,10,10,20,10"]
|===
include::content/2022/01/19/input/activity2.csv[]
|===
[source, bash]
----
docker exec \
-it pinot-controller-strava bin/pinot-admin.sh LaunchDataIngestionJob \
-jobSpecFile /config/job-spec.yml
----
Once the job has run we'll have two segments in our table, which we can see by navigating to http://localhost:9000/#/tenants/table/activities_offline_OFFLINE.
You should see something like the following:
.Segments
image::{{<siteurl>}}/uploads/2022/01/segments-table.png[]
If the segments are showing up we can be reasonably sure that the import has worked, but let's also navigate to http://localhost:9000/#/query to make sure.
If we run a query against the table, we should see something like the following:
.Querying the activities_offline table
image::{{<siteurl>}}/uploads/2022/01/sorted-query.png[]
== Checking sorted status
Next, we're going to check on the sorted status of the columns in each segment.
First let's collect all the columns in the `activities` schema:
[source, bash]
----
export queryString=`curl -X GET "http://localhost:9000/schemas/activities" -H "accept: application/json" 2>/dev/null |
jq -r '[.dimensionFieldSpecs,.dateTimeFieldSpecs | .[] | .name ] | join("&columns=")'`
----
And now let's call the _getServerMetaData_ endpoint and filter the response to get the segment name, input file, and column names with sorted status:
[source, bash]
----
curl -X GET "http://localhost:9000/segments/activities_offline/metadata?columns=${queryString}" \
-H "accept: application/json" 2>/dev/null |
jq -c '.[] | select(.columns != null) | {
segment: .segmentName,
importedFrom: .custom ["input.data.file.uri"],
columns: .columns | map({(.columnName): .sorted})
}'
----
If we run this command, we'll see the following output:
.Output
[source, json]
----
{
"segment": "activities_offline_OFFLINE_1581488399000_1582343333000_1",
"importedFrom": "file:/input-blog/activity2.csv",
"columns": [
{"altitude": false},
{"distance": false},
{"hr": false},
{"lon": false},
{"cadence": false},
{"rawTime": false},
{"location": false},
{"id": false},
{"lat": true},
{"timestamp": false}
]
}
{
"segment": "activities_offline_OFFLINE_1570656325000_1570656332000_0",
"importedFrom": "file:/input-blog/activity1.csv",
"columns": [
{"altitude": true},
{"distance": true},
{"hr": true},
{"lon": false},
{"cadence": true},
{"rawTime": true},
{"location": false},
{"id": true},
{"lat": false},
{"timestamp": true}
]
}
----
[NOTE]
====
I have formatted the JSON output using the https://j-brooke.github.io/FracturedJson/[FracturedJson tool^] in the web browser to make it easier to read.
====
From this output we can see that the segment created from `activity1.csv` has many more sorted columns than the one created from `activity2.csv.`
The only column that was explicitly sorted is `timestamp`, but `rawTime` and `distance` are also sorted because they are correlated with `timestamp` within an activity.
For the `activity2.csv` segment the only sorted column is `lat`, which is sorted by chance more than anything else!
None of the other columns are sorted.
== Summary
So that's the end of this first post explaining how sorted indexes work in Apache Pinot.
Hopefully it all made sense, but if not feel free to ask any questions on the https://communityinviter.com/apps/apache-pinot/apache-pinot[Pinot Community Slack^].
In our next post we'll learn https://www.markhneedham.com/blog/2022/01/20/apache-pinot-sorted-indexes-realtime-tables/[how sorted indexes work on real-time tables]. | In this post we'll learn all about sorted indexes on offline tables in Apache Pinot. | uploads/2022/01/sorted-indexes-offline-banner.png | [
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] | apache-pinot-sorted-indexes-offline-tables | https://markhneedham.com/blog/2022/01/19/apache-pinot-sorted-indexes-offline-tables |
false | 2022-01-31 02:44:37 | Apache Pinot: Resetting a segment after an invalid JSON Transformation | [
"pinot"
] | [
"pinot"
] | :icons: font
I recently had a typo in a https://docs.pinot.apache.org/developers/advanced/ingestion-level-transformations[Pinot ingestion transformation function^] and wanted to have Pinot re-process the Kafka stream without having to restart all the things.
In this blog post we'll learn how to do that.
.Apache Pinot: Resetting a segment after an invalid JSON Transformation
image::{{<siteurl>}}/uploads/2022/01/reset-banner.png[]
== Setup
We're going to spin up a local instance of Pinot and Kafka using the following Docker compose config:
.docker-compose.yml
[source, json]
----
include::content/2022/01/31/docker/docker-compose.yml[]
----
We can launch all the components by running the following command:
[source, bash]
----
docker-compose up
----
== Create Schema
We're going to use the following schema:
./config/schema.json
[source, json]
----
include::content/2022/01/31/docker/config/schema.json[]
----
It's only small, but it will be enough for our purposes.
We can create the schema by running the following command:
[source, bash]
----
docker exec -it pinot-controller-json bin/pinot-admin.sh AddSchema \
-schemaFile /config/schema.json -exec
----
== Create Table
Now let's create a real-time table based on that schema:
./config/table.json
[source, json]
----
include::content/2022/01/31/docker/config/table.json[]
----
<1> Typo in the JSON path (`'$.ages'`) means that an exception will be thrown when the function is executed.
[NOTE]
==
For documentation on the JSONPATHLONG function, see the https://docs.pinot.apache.org/configuration-reference/functions/jsonpathlong[JSONPATHLONG function page^].
==
We can create the table by running the following command:
[source, bash]
----
docker exec -it pinot-controller-json bin/pinot-admin.sh AddTable \
-tableConfigFile /config/table.json \
-exec
----
== Ingest Data into Kafka
Now let's ingest a few messages into the Kafka `events` topic:
[source, bash]
----
printf '{"timestamp": "2019-10-09 22:25:25", "payload": {"age": 18}}
{"timestamp": "2019-10-09 23:25:25", "payload": {"age": 14}}
{"timestamp": "2019-10-09 23:40:25", "payload": {"age": 16}}\n' |
docker exec -i kafka-json /opt/kafka/bin/kafka-console-producer.sh \
--bootstrap-server localhost:9092 \
--topic events
----
We can check that the messages have been ingested by running the following command:
[source, bash]
----
docker exec -i kafka-json /opt/kafka/bin/kafka-console-consumer.sh \
--bootstrap-server localhost:9092 \
--topic events \
--from-beginning \
--max-messages 3
----
.Output
[source, text]
----
{"timestamp": "2019-10-09 22:25:25", "payload": {"age": 18}}
{"timestamp": "2019-10-09 23:25:25", "payload": {"age": 14}}
{"timestamp": "2019-10-09 23:40:25", "payload": {"age": 16}}
Processed a total of 3 messages
----
All good so far.
Let's navigate to http://localhost:9000/#/query and query the `events` table:
.No documents
image::{{<siteurl>}}/uploads/2022/01/events-empty.png[]
Hmmm, no documents.
== The Debug API
We can find out what's going on by querying the debug API for this table:
[source, bash]
----
curl -X GET "http://localhost:9000/debug/tables/events?verbosity=0" -H "accept: application/json"
----
.Output
[source, json]
----
[
{
"tableName": "events_REALTIME",
"numSegments": 1,
"numServers": 1,
"numBrokers": 1,
"segmentDebugInfos": [
{
"segmentName": "events__0__0__20220131T1057Z",
"serverState": {
"Server_172.24.0.6_8098": {
"idealState": "CONSUMING",
"externalView": "CONSUMING",
"segmentSize": "0 bytes",
"consumerInfo": {
"segmentName": "events__0__0__20220131T1057Z",
"consumerState": "CONSUMING",
"lastConsumedTimestamp": 1643626843673,
"partitionToOffsetMap": {
"0": "3"
}
},
"errorInfo": {
"timestamp": "2022-01-31 10:57:50 UTC",
"errorMessage": "Caught exception while transforming the record: {\n \"nullValueFields\" : [ ],\n \"fieldToValueMap\" : {\n \"payload\" : {\n \"age\" : 16\n },\n \"age\" : null,\n \"timestamp\" : \"2019-10-09 23:40:25\"\n }\n}",
"stackTrace": "java.lang.RuntimeException: Caught exception while executing function: jsonPathLong(payload,'$.ages')\n\tat org.apache.pinot.segment.local.function.InbuiltFunctionEvaluator$FunctionExecutionNode.execute(InbuiltFunctionEvaluator.java:124)\n\tat org.apache.pinot.segment.local.function.InbuiltFunctionEvaluator.evaluate(InbuiltFunctionEvaluator.java:88)\n\tat org.apache.pinot.segment.local.recordtransformer.ExpressionTransformer.transform(ExpressionTransformer.java:96)\n\tat org.apache.pinot.segment.local.recordtransformer.CompositeTransformer.transform(CompositeTransformer.java:83)\n\tat org.apache.pinot.core.data.manager.realtime.LLRealtimeSegmentDataManager.processStreamEvents(LLRealtimeSegmentDataManager.java:518)\n\tat org.apache.pinot.core.data.manager.realtime.LLRealtimeSegmentDataManager.consumeLoop(LLRealtimeSegmentDataManager.java:420)\n\tat org.apache.pinot.core.data.manager.realtime.LLRealtimeSegmentDataManager$PartitionConsumer.run(LLRealtimeSegmentDataManager.java:568)\n\tat java.base/java.lang.Thread.run(Thread.java:829)\nCaused by: java.lang.IllegalStateException: Caught exception while invoking method: public static long org.apache.pinot.common.function.scalar.JsonFunctions.jsonPathLong(java.lang.Object,java.lang.String) with arguments: [{age=16}, $.ages]\n\tat org.apache.pinot.common.function.FunctionInvoker.invoke(FunctionInvoker.java:131)\n\tat org.apache.pinot.segment.local.function.InbuiltFunctionEvaluator$FunctionExecutionNode.execute(InbuiltFunctionEvaluator.java:122)\n\t... 7 more\nCaused by: java.lang.reflect.InvocationTargetException\n\tat java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.base/java.lang.reflect.Method.invoke(Method.java:566)\n\tat org.apache.pinot.common.function.FunctionInvoker.invoke(FunctionInvoker.java:128)\n\t... 8 more\nCaused by: com.jayway.jsonpath.PathNotFoundException: No results for path: $['ages']\n\tat com.jayway.jsonpath.internal.path.EvaluationContextImpl.getValue(EvaluationContextImpl.java:133)\n\tat com.jayway.jsonpath.JsonPath.read(JsonPath.java:187)\n\tat com.jayway.jsonpath.internal.JsonContext.read(JsonContext.java:102)\n\tat com.jayway.jsonpath.internal.JsonContext.read(JsonContext.java:85)\n\tat org.apache.pinot.common.function.scalar.JsonFunctions.jsonPath(JsonFunctions.java:89)\n\tat org.apache.pinot.common.function.scalar.JsonFunctions.jsonPathLong(JsonFunctions.java:152)\n\t... 13 more\n"
}
}
}
}
],
"serverDebugInfos": [],
"brokerDebugInfos": [],
"tableSize": {
"reportedSize": "0 bytes",
"estimatedSize": "0 bytes"
},
"ingestionStatus": {
"ingestionState": "HEALTHY",
"errorMessage": ""
}
}
]
----
As expected, Pinot failed to find the `ages` property because it doesn't exist in those messages.
Let's fix the transformation by applying the following table config:
[source, bash]
----
docker exec -it pinot-controller-json bin/pinot-admin.sh AddTable \
-tableConfigFile /config/table-fixed.json \
-exec
----
./config/table-fixed.json
[source, json]
----
include::content/2022/01/31/docker/config/table-fixed.json[]
----
<1> Typo has now been fixed
This fixes the table config, but doesn't retrospectively ingest the messages where the exception was thrown.
We can return the Kafka offset of the consuming segment by running the following:
[source, bash]
----
curl -X GET "http://localhost:9000/tables/events/consumingSegmentsInfo" -H "accept: application/json"
----
.Output
[source, json]
----
{
"_segmentToConsumingInfoMap": {
"events__0__0__20220131T1057Z": [
{
"serverName": "Server_172.24.0.6_8098",
"consumerState": "CONSUMING",
"lastConsumedTimestamp": 1643627394569,
"partitionToOffsetMap": {
"0": "3"
}
}
]
}
}
----
The offset for partition 0 is `3`, but we want to process offsets 0-2.
== Resetting the consuming segment
To do that we'll need to reset the consuming segment, by running the following command:
[source, bash]
----
curl -X POST "http://localhost:9000/segments/events_REALTIME/events__0__0__20220131T1057Z/reset" -H "accept: application/json"
----
.Output
[source, json]
----
{"status":"Successfully reset segment: events__0__0__20220131T1057Z of table: events_REALTIME"}
----
If we now go back to the query editor we'll see that those documents have now been ingested:
.Documents!
image::{{<siteurl>}}/uploads/2022/01/events-full.png[] | In this post we'll learn how to consume messages from a Kafka topic after fixing a faulty JSON transformation. | uploads/2022/01/reset-banner.png | [
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] | pinot-resetting-segment-invalid-json-transformation | https://markhneedham.com/blog/2022/01/31/pinot-resetting-segment-invalid-json-transformation |
false | 2022-01-13 00:44:37 | Apache Pinot: Checking which indexes are defined | [
"pinot"
] | [
"Pinot"
] | :icons: font
One of the most common questions in the https://communityinviter.com/apps/apache-pinot/apache-pinot[Apache Pinot community Slack^] is how to work out which indexes are defined on columns in https://docs.pinot.apache.org/basics/components/segment[Pinot segments].
This blog post will attempt to answer that question.
.Apache Pinot: Checking which indexes are defined
image::{{<siteurl>}}/uploads/2022/01/indexes-banner.png[]
== Setup
First, we're going to spin up a local instance of Pinot using the following Docker compose config:
.docker-compose.yml
[source, yaml]
----
version: '3.7'
services:
zookeeper:
image: zookeeper:3.5.6
hostname: zookeeper
container_name: zookeeper-indexes
ports:
- "2181:2181"
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ZOOKEEPER_TICK_TIME: 2000
pinot-controller:
image: apachepinot/pinot:0.9.3
command: "StartController -zkAddress zookeeper-indexes:2181 -dataDir /data"
container_name: "pinot-controller-indexes"
volumes:
- ./config:/config
- ./data:/data
- ./input:/input
restart: unless-stopped
ports:
- "9000:9000"
depends_on:
- zookeeper
pinot-broker:
image: apachepinot/pinot:0.9.3
command: "StartBroker -zkAddress zookeeper-indexes:2181"
restart: unless-stopped
container_name: "pinot-broker-indexes"
ports:
- "8099:8099"
depends_on:
- pinot-controller
pinot-server:
image: apachepinot/pinot:0.9.3
command: "StartServer -zkAddress zookeeper-indexes:2181"
restart: unless-stopped
container_name: "pinot-server-indexes"
depends_on:
- pinot-broker
----
[source, bash]
----
docker-compose up
----
== Data
We'll be working with the following CSV files that contain transcripts for various students:
.transcript1.csv
[format="csv", options="header"]
|===
include::content/2022/01/13/data/transcript1.csv[]
|===
.transcript2.csv
[format="csv", options="header"]
|===
include::content/2022/01/13/data/transcript2.csv[]
|===
We've deliberately got two different files so that we can create one segment based on each.
== Create Table
Let's create a Pinot schema and table based on this CSV file.
The schema is defined below:
./config/schema.json
[source.json]
----
include::content/2022/01/13/config/schema.json[]
----
Our table config is defined below:
./config/table.json
[source.json]
----
include::content/2022/01/13/config/table.json[]
----
Now let's create the table and schema:
[source, bash]
----
docker exec -it manual-pinot-controller bin/pinot-admin.sh AddTable \
-tableConfigFile /config/table.json \
-schemaFile /config/schema.json -exec
----
== Import CSV files
After we've done that, it's time to import the CSV file.
We'll import the `transcript1.csv` using the ingestion job spec defined below:
./config/job-spec1.yml
[source,yaml]
----
include::content/2022/01/13/config/job-spec1.yml[]
----
[source, bash]
----
docker exec -it pinot-controller-indexes bin/pinot-admin.sh LaunchDataIngestionJob \
-jobSpecFile /config/job-spec1.yml
----
And `transcript2.csv` using the ingestion job spec defined below:
./config/job-spec2.yml
[source,yaml]
----
include::content/2022/01/13/config/job-spec2.yml[]
----
[source, bash]
----
docker exec -it pinot-controller-indexes bin/pinot-admin.sh LaunchDataIngestionJob \
-jobSpecFile /config/job-spec2.yml
----
We can check which segments have been created by running the following command:
[source, bash]
----
curl -X GET "http://localhost:9000/segments/transcript" -H "accept: application/json"
----
.Output
[source, json]
----
[
{
"OFFLINE": [
"transcript_OFFLINE_1570863600000_1572418800000_0",
"transcript_OFFLINE_1641746000037_1642068001037_0"
]
}
]
----
We've got two segments - one for each file - which is what we expected.
== Check indexes
We can check which indexes are defined on the columns in a segment by calling the _getServerMetaData_ endpoint, available at http://localhost:9000/help#/Segment/getServerMetadata.
A screenshot of the Swagger documentation for this end point is shown below:
.Segment metadata end point
image::{{<siteurl>}}/uploads/2022/01/segment-metadata.png[]
Let's see what data is returned when we query this end point for the `transcript` table:
[source, bash]
----
curl -X GET "http://localhost:9000/segments/transcript/metadata" -H "accept: application/json"
----
.Output
[source, json]
----
{
"transcript_OFFLINE_1570863600000_1572418800000_0": {
"segmentName": "transcript_OFFLINE_1570863600000_1572418800000_0",
"schemaName": null,
"crc": 1700211719,
"creationTimeMillis": 1642069557334,
"creationTimeReadable": "2022-01-13T10:25:57:334 UTC",
"timeGranularitySec": 0,
"startTimeMillis": 1570863600000,
"startTimeReadable": "2019-10-12T07:00:00.000Z",
"endTimeMillis": 1572418800000,
"endTimeReadable": "2019-10-30T07:00:00.000Z",
"segmentVersion": "v3",
"creatorName": null,
"custom": {
"input.data.file.uri": "file:/input/transcript1.csv"
},
"columns": [],
"indexes": {},
"star-tree-index": null
},
"transcript_OFFLINE_1641746000037_1642068001037_0": {
"segmentName": "transcript_OFFLINE_1641746000037_1642068001037_0",
"schemaName": null,
"crc": 1556428049,
"creationTimeMillis": 1642069571796,
"creationTimeReadable": "2022-01-13T10:26:11:796 UTC",
"timeGranularitySec": 0,
"startTimeMillis": 1641746000037,
"startTimeReadable": "2022-01-09T16:33:20.037Z",
"endTimeMillis": 1642068001037,
"endTimeReadable": "2022-01-13T10:00:01.037Z",
"segmentVersion": "v3",
"creatorName": null,
"custom": {
"input.data.file.uri": "file:/input/transcript2.csv"
},
"columns": [],
"indexes": {},
"star-tree-index": null
}
}
----
As expected, we have two segments.
It also tells us when each of the segments was created, along with the minimum and maximum values for the timestamp column.
To get a breakdown of column metadata we'll need to pass in a list of column names.
Let's have a look what gets returned if we pass in the `firstName` column:
[source, bash]
----
curl -X GET "http://localhost:9000/segments/transcript/metadata?columns=firstName&columns=" -H "accept: application/json"
----
.Output
[source, json]
----
{
"transcript_OFFLINE_1570863600000_1572418800000_0": {
"segmentName": "transcript_OFFLINE_1570863600000_1572418800000_0",
"schemaName": null,
"crc": 1700211719,
"creationTimeMillis": 1642069557334,
"creationTimeReadable": "2022-01-13T10:25:57:334 UTC",
"timeGranularitySec": 0,
"startTimeMillis": 1570863600000,
"startTimeReadable": "2019-10-12T07:00:00.000Z",
"endTimeMillis": 1572418800000,
"endTimeReadable": "2019-10-30T07:00:00.000Z",
"segmentVersion": "v3",
"creatorName": null,
"custom": {
"input.data.file.uri": "file:/input/transcript1.csv"
},
"columns": [
{
"totalDocs": 4,
"partitionFunction": null,
"partitions": null,
"hasDictionary": true,
"columnMaxLength": 4,
"autoGenerated": false,
"paddingCharacter": "\u0000",
"maxNumberOfMultiValues": 0,
"fieldSpec": {
"name": "firstName",
"singleValueField": true,
"defaultNullValueString": "null",
"virtualColumnProvider": null,
"maxLength": 512,
"dataType": "STRING",
"transformFunction": null,
"defaultNullValue": "null"
},
"bitsPerElement": 2,
"totalNumberOfEntries": 4,
"sorted": false,
"minValue": "Bob",
"maxValue": "Nick",
"cardinality": 3,
"fieldType": "DIMENSION",
"singleValue": true,
"columnName": "firstName",
"dataType": "STRING"
}
],
"indexes": {
"firstName": {
"bloom-filter": "NO",
"dictionary": "YES",
"forward-index": "YES",
"inverted-index": "NO",
"null-value-vector-reader": "NO",
"range-index": "NO",
"json-index": "NO"
}
},
"star-tree-index": null
},
"transcript_OFFLINE_1641746000037_1642068001037_0": {
"segmentName": "transcript_OFFLINE_1641746000037_1642068001037_0",
"schemaName": null,
"crc": 1556428049,
"creationTimeMillis": 1642069571796,
"creationTimeReadable": "2022-01-13T10:26:11:796 UTC",
"timeGranularitySec": 0,
"startTimeMillis": 1641746000037,
"startTimeReadable": "2022-01-09T16:33:20.037Z",
"endTimeMillis": 1642068001037,
"endTimeReadable": "2022-01-13T10:00:01.037Z",
"segmentVersion": "v3",
"creatorName": null,
"custom": {
"input.data.file.uri": "file:/input/transcript2.csv"
},
"columns": [
{
"totalDocs": 4,
"partitionFunction": null,
"partitions": null,
"hasDictionary": true,
"columnMaxLength": 6,
"autoGenerated": false,
"paddingCharacter": "\u0000",
"maxNumberOfMultiValues": 0,
"fieldSpec": {
"name": "firstName",
"singleValueField": true,
"defaultNullValueString": "null",
"virtualColumnProvider": null,
"maxLength": 512,
"dataType": "STRING",
"transformFunction": null,
"defaultNullValue": "null"
},
"bitsPerElement": 2,
"totalNumberOfEntries": 4,
"sorted": false, <1>
"minValue": "Jason",
"maxValue": "Olivia",
"cardinality": 3,
"fieldType": "DIMENSION",
"singleValue": true,
"columnName": "firstName",
"dataType": "STRING"
}
],
"indexes": {
"firstName": {
"bloom-filter": "NO",
"dictionary": "YES", <2>
"forward-index": "YES",
"inverted-index": "NO",
"null-value-vector-reader": "NO",
"range-index": "NO",
"json-index": "NO"
}
},
"star-tree-index": null
}
}
----
<1> The column isn't sorted.
<2> The column uses a dictionary based forward index.
Along with the segment metadata we've now also got a bunch of information about the _firstName_ column and its indexes.
We're mostly interested in the information under `indexes.firstName`, but we can also see whether a https://docs.pinot.apache.org/basics/indexing/forward-index#sorted-forward-index-with-run-length-encoding[sorted forward index column^] has been applied to this column by checking the `columns.sorted` property.
Next we're going to extract information about the indexes defined for each column.
We'll need to combine the data from `columns` and `indexes`, which will be much easier to do in Python.
The following script writes a CSV file describing column names and indexes for each segment:
[source, python]
----
import requests
import csv
pinot_url = "http://localhost:9000"
response = requests.get(f"{pinot_url}/schemas/transcript")
r = response.json()
fields = r["dimensionFieldSpecs"] + r["dateTimeFieldSpecs"]
columns = [field["name"] for field in fields]
data = { "columns": columns}
response = requests.get(f"{pinot_url}/segments/transcript/metadata", params=data)
r = response.json()
for segment, values in r.items():
with open(f"output/schema_{segment}.csv", "w") as schema_file:
writer = csv.writer(schema_file, delimiter=",")
print(segment)
writer.writerow(["column", "sorted"] + list(list(values["indexes"].values())[0].keys()))
for column in values["columns"]:
column_name = column["fieldSpec"]["name"]
filtered_map = {k:v for k,v in values["indexes"].items() if k == column_name}
writer.writerow([column_name, column["sorted"]] + list(filtered_map[column_name].values()))
----
The output files are shown below:
.schema_transcript_OFFLINE_1570863600000_1572418800000_0.csv
[format="csv", options="header"]
|===
include::content/2022/01/13/no-indexes/schema_transcript_OFFLINE_1570863600000_1572418800000_0.csv[]
|===
.schema_transcript_OFFLINE_1641746000037_1642068001037_0.csv
[format="csv", options="header"]
|===
include::content/2022/01/13/no-indexes/schema_transcript_OFFLINE_1641746000037_1642068001037_0.csv[]
|===
== Adding new indexes
./config/table-inverted-index.json
[source.json]
----
include::content/2022/01/13/config/table-inverted-index.json[]
----
abc
[source, bash]
----
curl -X PUT "http://localhost:9000/tables/transcript" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
--data @config/table-inverted-index.json
----
.Output
[source, json]
----
{"status":"Table config updated for transcript"}
----
The table is now updated, but the inverted index hasn't been applied to any of the segments yet.
To apply this new index, we'll need to call the Reload API.
We can reload an individual segment or all of the segments.
.Reload API
image::{{<siteurl>}}/uploads/2022/01/reload-api.png[]
Let's reload just segment `transcript_OFFLINE_1570863600000_1572418800000_0`, using the following command:
[source, bash]
----
curl -X POST \
"http://localhost:9000/segments/transcript/transcript_OFFLINE_1570863600000_1572418800000_0/reload?forceDownload=false" \
-H "accept: application/json"
----
++++
<style>
table.inverted-index tbody tr:nth-child(2) {
background-color: yellow;
}
table.inverted-index tbody tr:nth-child(2) td:nth-child(6) {
font-weight: 700;
}
</style>
++++
Now we can re-run our Python script to get the latest state of indexes, which is shown below:
.schema_transcript_OFFLINE_1570863600000_1572418800000_0.csv
[format="csv", options="header", role="inverted-index"]
|===
include::content/2022/01/13/inverted-index/schema_transcript_OFFLINE_1570863600000_1572418800000_0.csv[]
|===
.schema_transcript_OFFLINE_1641746000037_1642068001037_0.csv
[format="csv", options="header", role="inverted-index"]
|===
include::content/2022/01/13/inverted-index/schema_transcript_OFFLINE_1641746000037_1642068001037_0.csv[]
|===
We can see that an inverted index has been added to the _firstName_ column for segment `transcript_OFFLINE_1570863600000_1572418800000_0`, but not segment `transcript_OFFLINE_1641746000037_1642068001037_0`.
We can apply the inverted index to all segments by running the following command:
[source, bash]
----
curl -X POST "http://localhost:9000/segments/transcript/reload?forceDownload=false" -H "accept: application/json"
----
And if we run our Python script one more time, we'll get the following output:
.schema_transcript_OFFLINE_1570863600000_1572418800000_0.csv
[format="csv", options="header", role="inverted-index"]
|===
include::content/2022/01/13/inverted-index-all/schema_transcript_OFFLINE_1570863600000_1572418800000_0.csv[]
|===
.schema_transcript_OFFLINE_1641746000037_1642068001037_0.csv
[format="csv", options="header", role="inverted-index"]
|===
include::content/2022/01/13/inverted-index-all/schema_transcript_OFFLINE_1641746000037_1642068001037_0.csv[]
|===
The inverted index on the _firstName_ column is now available on all segments! | In this post we'll learn how to check which indexes are defined on Pinot segments. | uploads/2022/01/indexes-banner.png | [
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] | apache-pinot-which-indexes-are-defined | https://markhneedham.com/blog/2022/01/13/apache-pinot-which-indexes-are-defined |
false | 2022-01-14 00:44:37 | Python: Generate WKT from Lat Long Coordinates | [
"python",
"wkt"
] | [
"python"
] | Recently I've been playing around with geometry objects in https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry[WKT format^] while documenting https://docs.pinot.apache.org/basics/indexing/geospatial-support[Apache Pinot's Geospatial functions^].
I then wanted to figure out how to generate a WKT string from a list of lat long coordinates, which we'll learn how to do in this blog post.
.Python: Generate WKT from Lat Long Coordinates
image::{{<siteurl>}}/uploads/2022/01/wkt-banner.png[]
We're going to do all this using Python's https://shapely.readthedocs.io/en/stable/manual.html[Shapely^] library, so let's first install that library:
[source, bash]
----
pip install shapely
----
[NOTE]
====
I learnt about the Shapely library from https://gis.stackexchange.com/questions/294206/create-a-polygon-from-coordinates-in-geopandas-with-python[Davma's StackOverflow question^].
====
Let's say we have a bunch of lat/long coordinates defined like this:
[source, python]
----
lat_longs = [
(50.854457, 4.377184),
(52.518172, 13.407759),
(50.072651, 14.435935),
(48.853033, 2.349553),
(50.854457, 4.377184)
]
----
We're going to create a polygon based on these coordinates using the following code:
[source, python]
----
from shapely.geometry import Polygon
polygon = Polygon(lat_longs)
----
And getting a WKT string is as easy as calling the `wkt` property:
[source, python]
----
print(polygon.wkt)
----
.Output
[source, text]
----
POLYGON ((50.854457 4.377184, 52.518172 13.407759, 50.072651 14.435935, 48.853033 2.349553, 50.854457 4.377184))
----
And finally, we can plot that polygon on a map using the https://clydedacruz.github.io/openstreetmap-wkt-playground/[OpenStreetMap WKT Playground^].
.Polygon visualisation
image::{{<siteurl>}}/uploads/2022/01/wkt-diagram.png[width="500px"] | In this post we'll learn how to convert lat long coordinates to the WKT format. | uploads/2022/01/wkt-banner.png | [
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] | python-generate-wkt-lat-long-coordinates | https://markhneedham.com/blog/2022/01/14/python-generate-wkt-lat-long-coordinates |
false | 2022-01-22 02:44:37 | Kafka: Writing data to a topic from the command line | [
"kafka"
] | [
"kafka"
] | I've been doing more Apache Pinot documentation - this time covering the https://docs.pinot.apache.org/users/user-guide-query/supported-transformations#json-functions[JSON functions^] - and I needed to quickly write some data into Kafka to test things out.
I'd normally do that using the https://www.markhneedham.com/blog/2022/01/20/apache-pinot-sorted-indexes-realtime-tables/[Python Kafka client^], but this time I wanted to do it using only command line tools.
So that's what we'll be doing in this blog post and it's more for future me than anyone else!
.Kafka: Writing data to a topic from the command line
image::{{<siteurl>}}/uploads/2022/01/kafka-banner.png[]
== Setup
Let's start by spinning up Kafka using Docker compose:
.docker-compose.yml
[source, yaml]
----
version: '3.7'
services:
zookeeper:
image: zookeeper:3.5.6
hostname: zookeeper
container_name: zookeeper-blog
ports:
- "2181:2181"
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ZOOKEEPER_TICK_TIME: 2000
kafka:
image: wurstmeister/kafka:latest
restart: unless-stopped
container_name: "kafka-blog"
ports:
- "9092:9092"
expose:
- "9093"
depends_on:
- zookeeper
environment:
KAFKA_ZOOKEEPER_CONNECT: zookeeper-blog:2181/kafka
KAFKA_BROKER_ID: 0
KAFKA_ADVERTISED_HOST_NAME: kafka-blog
KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka-blog:9093,OUTSIDE://localhost:9092
KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9093,OUTSIDE://0.0.0.0:9092
KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,OUTSIDE:PLAINTEXT
----
We can spin up that container by running the following command:
[source, bash]
----
docker-compose up
----
== Write messages
Now it's time to import some data using the `kafka-console-producer.sh` script.
If we want to ingest a single message to the `docs` topic, we can run the following:
[source, bash]
----
echo '{"timestamp": "2019-10-09 21:25:25", "meta": {"age": 12}}' |
docker exec -i kafka-blog /opt/kafka/bin/kafka-console-producer.sh \
--bootstrap-server localhost:9092 \
--topic docs
----
But what about if we have a file full of documents, as shown in the file below:
.documents.json
[source, json]
----
include::content/2022/01/22/data/documents.json[]
----
We can pipe those into `kafka-console-producer.sh` like this:
[source, bash]
----
docker exec -i kafka-blog /opt/kafka/bin/kafka-console-producer.sh \
--bootstrap-server localhost:9092 \
--topic docs \
< documents.json
----
== Read messages
So far, so good.
We haven't go any errors, but let's double check that our messages have made it into Kafka.
We can check the offsets (which in this case indicates the number of documents ingested) for the `docs` topic by running the following command:
[source, bash]
----
docker exec -it kafka-blog kafka-run-class.sh kafka.tools.GetOffsetShell \
--broker-list localhost:9092 \
--topic docs
----
.Output
[source, text]
----
docs:0:4
----
The output indicates that partition `0` of the `docs` topic has an offset of 4, which means this partition of the topic contains 4 documents.
We imported one message on its own and three from `documents.json`, so that's what we'd expect.
We can return a maximum of 4 messages (i.e. all of them) from the `docs` topic, like so:
[source, bash]
----
docker exec -i kafka-blog /opt/kafka/bin/kafka-console-consumer.sh \
--bootstrap-server localhost:9092 \
--topic docs \
--from-beginning \
--max-messages 4
----
.Output
[source, text]
----
{"timestamp": "2019-10-09 21:25:25", "meta": {"age": 12}}
{"timestamp": "2019-10-09 22:25:25", "meta": {"age": 18}}
{"timestamp": "2019-10-09 23:25:25", "meta": {"age": 14}}
{"timestamp": "2019-10-09 23:40:25", "meta": {"age": 16}}
Processed a total of 4 messages
---- | In this post we'll learn how to write data to a Kafka topic from the command line. | uploads/2022/01/kafka-banner.png | [
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] | kafka-writing-data-topic-command-line | https://markhneedham.com/blog/2022/01/22/kafka-writing-data-topic-command-line |
false | 2022-06-16 02:44:37 | docker exec: Passing in environment variables | [
"docker"
] | [
"docker"
] | I've been working on an https://dev.startree.ai/docs/pinot/recipes/[Apache Pinot recipe^] showing how to ingest data from S3 and I needed to pass in my AWS credentials to the `docker exec` command that I was running.
It wasn't difficult to do, but took me a little while to figure out.
.docker exec: Passing in environment variables
image::{{<siteurl>}}/uploads/2022/06/docker-exec.png[]
The command that I was running looked like this:
[source, bash]
----
docker exec \
-it pinot-controller bin/pinot-admin.sh LaunchDataIngestionJob \
-jobSpecFile /config/job-spec.yml
----
And the https://docs.pinot.apache.org/basics/data-import/pinot-file-system/amazon-s3[Pinot documentation^] says that I need to pass in `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY`.
I initially came across https://stackoverflow.com/questions/27812548/how-to-set-an-environment-variable-in-a-running-docker-container[a StackOverflow question^] that described a bunch of approaches that didn't work for me, but luckily I found https://devconnected.com/docker-exec-command-with-examples/[a tutorial^] that showed how to do it.
Effectively we can set these environment variables via the `-e` parameter, as shown below:
[source, bash]
----
docker exec \
-e AWS_ACCESS_KEY_ID=<accessKeyId> \
-e AWS_SECRET_ACCESS_KEY=<secretAccessKey> \
-it pinot-controller bin/pinot-admin.sh LaunchDataIngestionJob \
-jobSpecFile /config/job-spec.yml
----
It's that simple! | In this post we'll learn how to pass environment variables to docker exec. | uploads/2022/06/docker-exec.png | [
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] | docker-exec-environment-variables | https://markhneedham.com/blog/2022/06/16/docker-exec-environment-variables |
false | 2022-06-23 02:44:37 | Apache Pinot: Skipping periodic task: Task: PinotTaskManager | [
"pinot"
] | [
"pinot"
] | As I mentioned in my last blog post, I've been working on an https://dev.startree.ai/docs/pinot/recipes/[Apache Pinot recipe^] showing how to ingest data from S3 and after I'd got that working I moved onto using the https://docs.pinot.apache.org/basics/components/minion#segmentgenerationandpushtask[SegmentGenerationAndPushTask^] to poll S3 and ingest files automatically.
It took me longer than it should have to get this working and hopefully this blog post will help you avoid the problems that I had.
.Apache Pinot: Skipping periodic task: Task: PinotTaskManager
image::{{<siteurl>}}/uploads/2022/06/pinot-no-periodic-task-banner.png[]
We're gonna start with a Docker Compose file that spins up Zookeeper and a Pinot Controller, as shown below:
.docker-compose.yml
[source, yaml]
----
version: '3.7'
services:
zookeeper:
image: zookeeper:3.5.6
hostname: zookeeper
container_name: zookeeper
ports:
- "2181:2181"
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ZOOKEEPER_TICK_TIME: 2000
pinot-controller:
image: apachepinot/pinot:0.10.0
command: "StartController -zkAddress zookeeper:2181"
container_name: "pinot-controller"
volumes:
- ./config:/config
- ./input:/input
restart: unless-stopped
ports:
- "9000:9000"
depends_on:
- zookeeper
----
We can run this by using the `docker-compose up` command.
Once we've done that we're going to create an `events` table that ingests files from S3 once a minute.
The config for this table is shown below:
./config/table.json
[source, json]
----
{
"tableName": "events",
"tableType": "OFFLINE",
"segmentsConfig": {
"timeColumnName": "ts",
"schemaName": "events",
"replication": "1"
},
"tableIndexConfig": {
"loadMode": "MMAP"
},
"tenants": {},
"metadata": {},
"ingestionConfig": {
"batchIngestionConfig": {
"segmentIngestionType": "APPEND",
"segmentIngestionFrequency": "DAILY",
"batchConfigMaps": [
{
"input.fs.className": "org.apache.pinot.plugin.filesystem.S3PinotFS",
"input.fs.prop.region": "eu-west-2",
"input.fs.prop.accessKey": "<access-key>",
"input.fs.prop.secretKey": "<secret-key>",
"inputDirURI": "s3://<bucket-name>",
"includeFileNamePattern": "glob:**/*.json",
"excludeFileNamePattern": "glob:**/*.tmp",
"inputFormat": "json"
}
]
}
},
"task": {
"taskTypeConfigsMap": {
"SegmentGenerationAndPushTask": {
"schedule": "0 */1 * * * ?",
"tableMaxNumTasks": 10
}
}
}
}
----
I created this table using the following command:
[source, bash]
----
docker exec -it pinot-controller bin/pinot-admin.sh AddTable \
-tableConfigFile /config/table.json \
-schemaFile /config/schema.json -exec
----
I then waited for the JSON documents to be ingested.
After a few minutes of not seeing any data in my Pinot table, I started debugging to figure out what was going on.
The `SegmentGenerationAndPushTask` is configured by the `PinotTaskManager`, so I looked for any mentions of that class in the Pinot controller logs:
[source, bash]
----
docker exec -it pinot-controller grep -rni --color "PinotTaskManager" logs/
----
.Output
[source,text]
----
logs/pinot-all.log:4055:2022/06/20 09:14:38.608 INFO [PeriodicTaskScheduler] [main] Skipping periodic task: Task: PinotTaskManager, Interval: -1s, Initial Delay: 272s
----
Hmmm, the periodic task that would schedule our import job is being ignored, which explains why no data is being ingested.
It took me a while to figure out that the task scheduler is disabled by default, and needs to be enabled in the Controller configuration, as shown below:
./config/controller-conf.conf
[source, text]
----
controller.access.protocols.http.port=9000
controller.zk.str=zookeeper:2181
controller.helix.cluster.name=PinotCluster
controller.host=pinot-controller
controller.port=9000
controller.task.scheduler.enabled=true
controller.task.frequencyPeriod=5m
----
If we're using Docker, we need to update the command for starting the Pinot Controller to refer to that configuration file, as shown below:
[source, yaml]
----
version: '3.7'
services:
zookeeper:
image: zookeeper:3.5.6
hostname: zookeeper
container_name: zookeeper
ports:
- "2181:2181"
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ZOOKEEPER_TICK_TIME: 2000
pinot-controller:
image: apachepinot/pinot:0.10.0
command: "StartController -zkAddress zookeeper:2181 -config /config/controller-conf.conf"
container_name: "pinot-controller"
volumes:
- ./config:/config
- ./input:/input
restart: unless-stopped
ports:
- "9000:9000"
depends_on:
- zookeeper
----
And once we've restarted that container, we can search the logs again:
[source, bash]
----
docker exec -it pinot-controller grep -rni --color "PinotTaskManager" logs/
----
.Output
[source,text]
----
2022/06/23 11:08:34.181 INFO [PeriodicTaskScheduler] [main] Starting periodic task scheduler with tasks: [Task: PinotTaskManager, Interval: 300s, Initial Delay: 280s, Task: RetentionManager, Interval: 21600s, Initial Delay: 183s, Task: OfflineSegmentIntervalChecker, Interval: 86400s, Initial Delay: 215s, Task: RealtimeSegmentValidationManager, Interval: 3600s, Initial Delay: 242s, Task: BrokerResourceValidationManager, Interval: 3600s, Initial Delay: 210s, Task: SegmentStatusChecker, Interval: 300s, Initial Delay: 219s, Task: SegmentRelocator, Interval: 3600s, Initial Delay: 229s, Task: MinionInstancesCleanupTask, Interval: 3600s, Initial Delay: 206s, Task: TaskMetricsEmitter, Interval: 300s, Initial Delay: 263s]
----
Success!
Now the `PinotTaskManager` is running and will schedule `SegmentGenerationAndPushTask` as expected. | In this post we'll learn how to solve a problem when periodically importing data into Apache Pinot. | uploads/2022/06/pinot-no-periodic-task-banner.png | [
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] | apache-pinot-skipping-periodic-task-pinot-task-manager | https://markhneedham.com/blog/2022/06/23/apache-pinot-skipping-periodic-task-pinot-task-manager |
false | 2022-12-02 02:44:37 | Python: Sorting lists of dictionaries with sortedcontainers | [
"python",
"sortedcontainers"
] | [
"python"
] | I was recently working on a Kafka streams data generator, where I only wanted to publish events once the time on those events had been reached.
To solve this problem I needed a sorted list and in this blog post we're going to explore how I went about doing this.
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/RmNNlC7zhZw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
++++
====
== Unsorted lists
We're going to start out by created a list of dictionaries that contain a timestamp and name, as shown below:
[source, python]
----
import datetime as dt
now = dt.datetime.now()
items = [
{"timestamp": now, "name": "Mark"},
{"timestamp": now + dt.timedelta(hours=1), "name": "Dunith"},
{"timestamp": now - dt.timedelta(minutes=27), "name": "Michael"}
]
----
If we want to print out this list, we could do so using a for loop:
[source, python]
----
for item in items:
print(item)
----
We'd see the following output:
.Output
[source, json]
----
{'timestamp': datetime.datetime(2022, 11, 24, 7, 36, 31, 757235), 'name': 'Mark'}
{'timestamp': datetime.datetime(2022, 11, 24, 8, 36, 31, 757235), 'name': 'Dunith'}
{'timestamp': datetime.datetime(2022, 11, 24, 7, 9, 31, 757235), 'name': 'Michael'}
----
As you'd probably expect, this is sorted in insertion order.
If we want to sort the results we could wrap `items` in the `sorted` function, like this:
[source, python]
----
for item in sorted(items, key=lambda x: x["timestamp"]):
print(item)
----
In this code sample we're sorting by the `timestamp` property, and if we run this code, we'll see the following output:
.Output
[source, json]
----
{'timestamp': datetime.datetime(2022, 11, 24, 7, 9, 31, 757235), 'name': 'Michael'}
{'timestamp': datetime.datetime(2022, 11, 24, 7, 36, 31, 757235), 'name': 'Mark'}
{'timestamp': datetime.datetime(2022, 11, 24, 8, 36, 31, 757235), 'name': 'Dunith'}
----
This time Michael comes first, then Mark, and finally Dunith.
When you want to do some adhoc sorting, this is a reasonable approach, but I wanted a list that's always sorted.
== sortedcontainers
Enter the https://grantjenks.com/docs/sortedcontainers/[sortedcontainers^] library, a collections library written in Python that's super fast and supports sorted lists, maps, and dictionaries.
We're only going to use the list, so let's import that:
[source, python]
----
from sortedcontainers import SortedList
----
When we create an instance of `SortedList` we need to pass in a lamba expression that describes our sorting key:
[source, python]
----
sorted_items = SortedList(key=lambda x: x["timestamp"])
----
We can have it sort an existing list using the `update` function:
[source, python]
----
sorted_items.update(items)
----
Let's print that list:
[source, python]
----
for item in sorted_items:
print(item)
----
.Output
[source, json]
----
{'timestamp': datetime.datetime(2022, 11, 24, 7, 9, 31, 757235), 'name': 'Michael'}
{'timestamp': datetime.datetime(2022, 11, 24, 7, 36, 31, 757235), 'name': 'Mark'}
{'timestamp': datetime.datetime(2022, 11, 24, 8, 36, 31, 757235), 'name': 'Dunith'}
----
So far so good.
We can also add items to the list:
[source, python]
----
sorted_items.add(
{"timestamp": now + dt.timedelta(minutes=12), "name": "Jennifer"}
)
for item in sorted_items:
print(item)
----
.Output
[source, json]
----
{'timestamp': datetime.datetime(2022, 11, 24, 7, 9, 31, 757235), 'name': 'Michael'}
{'timestamp': datetime.datetime(2022, 11, 24, 7, 36, 31, 757235), 'name': 'Mark'}
{'timestamp': datetime.datetime(2022, 11, 24, 7, 48, 31, 757235), 'name': 'Jennifer'}
{'timestamp': datetime.datetime(2022, 11, 24, 8, 36, 31, 757235), 'name': 'Dunith'}
----
And as we'd expect, the Jennifer list item gets sorted between Mark and Dunith.
The classic list operations are sorted as well.
.Checking an item exists
[source, python]
----
{"timestamp": now, "name": "Mark"} in sorted_items
----
.Output
[source, json]
----
True
----
.Concatenating lists
[source, python]
----
for item in (sorted_items + sorted_items):
print(item)
----
.Output
[source, json]
----
{'timestamp': datetime.datetime(2022, 11, 24, 7, 9, 31, 757235), 'name': 'Michael'}
{'timestamp': datetime.datetime(2022, 11, 24, 7, 9, 31, 757235), 'name': 'Michael'}
{'timestamp': datetime.datetime(2022, 11, 24, 7, 36, 31, 757235), 'name': 'Mark'}
{'timestamp': datetime.datetime(2022, 11, 24, 7, 36, 31, 757235), 'name': 'Mark'}
{'timestamp': datetime.datetime(2022, 11, 24, 7, 48, 31, 757235), 'name': 'Jennifer'}
{'timestamp': datetime.datetime(2022, 11, 24, 7, 48, 31, 757235), 'name': 'Jennifer'}
{'timestamp': datetime.datetime(2022, 11, 24, 8, 36, 31, 757235), 'name': 'Dunith'}
{'timestamp': datetime.datetime(2022, 11, 24, 8, 36, 31, 757235), 'name': 'Dunith'}
----
.Deleting items
[source, python]
----
sorted_items.remove({"timestamp": now, "name": "Mark"})
for item in sorted_items:
print(item)
----
.Output
[source, json]
----
{'timestamp': datetime.datetime(2022, 11, 24, 7, 9, 31, 757235), 'name': 'Michael'}
{'timestamp': datetime.datetime(2022, 11, 24, 7, 48, 31, 757235), 'name': 'Jennifer'}
{'timestamp': datetime.datetime(2022, 11, 24, 8, 36, 31, 757235), 'name': 'Dunith'}
----
=== In Summary
So far my experience with the sortedcontainers library is great - it's done everything I expected and is really easy to use.
Give it a try! | In this post we'll learn how to sort lists of dictionaries in Python with the sortedcontainers library. | uploads/2022/12/dictionaries-banner.png | [
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] | python-sorting-lists-dictionaries-sortedcontainers | https://markhneedham.com/blog/2022/12/02/python-sorting-lists-dictionaries-sortedcontainers |
false | 2022-12-06 02:44:37 | kcat/jq: Reached end of topic at offset: exiting | [
"kafka",
"kcat",
"jq"
] | [
"Kafka"
] | I've recently been working with Debezium to get the https://dev.startree.ai/docs/pinot/demo-apps/pizza-shop[Pizza Shop product catalogue^] from MySQL into Apache Kafka and ran into an issue when querying the resulting stream using https://docs.confluent.io/platform/current/app-development/kafkacat-usage.html[kcat^] and https://stedolan.github.io/jq/[jq^].
In this blog I'll show how I worked around that problem.
I configured Debezium to write any changes to the products table into the `mysql.pizzashop.products` topic.
I then queriesthis topic to find the changes for just one of the products:
[source, bash]
----
kcat -C -b localhost:29092 -t mysql.pizzashop.products -e | jq '.payload | select(.id == 1)'
----
And got the following output:
.Output
[source, bash]
----
{
"id": 1,
"name": "Moroccan Spice Pasta Pizza - Veg",
"description": "A pizza loaded with a spicy combination of Harissa sauce and delicious pasta.",
"category": "veg pizzas",
"price": 335,
"image": "https://www.dominos.co.in//files/items/MoroccanSpicePPVG_N.jpg",
"created_at": "2022-11-22T10:07:36Z",
"updated_at": 1669111656000,
"__deleted": "false"
}
% Reached end of topic mysql.pizzashop.products [0] at offset 83: exiting
{
"id": 1,
"name": "Moroccan Spice Pasta Pizza",
"description": "A pizza loaded with a spicy combination of Harissa sauce and delicious pasta.",
"category": "veg pizzas",
"price": 335,
"image": "https://www.dominos.co.in//files/items/MoroccanSpicePPVG_N.jpg",
"created_at": "2022-11-22T10:07:36Z",
"updated_at": 1669117483000,
"__deleted": "false"
}
----
If I took away the `jq` command I wasn't getting the 'reached end of topic' error message, it was returning both events properly.
But the messages don't format so nicely without jq so I still needed a solution.
A bit of searching led me to https://ftisiot.net/posts/jq-kcat-consumer/[a blog post^] written by Francesco Tisiot in which he described exactly the same problem that I was having.
The solution is to pass in the `-u` flag to kcat, which stops it from buffering the output.
We end up with the following command that works perfectly:
[source, bash]
----
kcat -C -b localhost:29092 -t mysql.pizzashop.products -e -u | jq '.payload | select(.id == 1)'
----
Thanks Francesco! | In this post we'll learn how to work around a problem when using kcat and jq together. | uploads/2022/12/kcat-jq-exiting-banner.png | [
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] | kcat-jq-reached-end-of-topic-exiting | https://markhneedham.com/blog/2022/12/06/kcat-jq-reached-end-of-topic-exiting |
false | 2024-02-04 00:44:37 | LLaVA 1.5 vs. 1.6 | [
"ollama",
"generative-ai",
"llava",
"til"
] | [
"TIL"
] | :icons: font
https://llava-vl.github.io/[LLaVA^] (or Large Language and Vision Assistant), an open-source large multi-modal model, just released https://llava-vl.github.io/blog/2024-01-30-llava-1-6/[version 1.6^].
It claims to have improvements over version 1.5, which was released a few months ago:
[quote]
____
1. Increasing the input image resolution to 4x more pixels. This allows it to grasp more visual details. It supports three aspect ratios, up to 672x672, 336x1344, 1344x336 resolution.
2. Better visual reasoning and OCR capability with an improved visual instruction tuning data mixture.
3. Better visual conversation for more scenarios, covering different applications. Better world knowledge and logical reasoning.
4. Efficient deployment and inference with SGLang.
____
Both the initial and new versions are available on https://ollama.ai/[Ollama^], so I thought I'd give them a try on some of my images to see how they get on.
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/jXUv8mlQHq0?si=n7NDkvoqV2weOz1e" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
====
If you want to follow along, you'll need to install Ollama, launch Ollama (`ollama serve`), and then pull down both models:
[source, bash]
----
ollama pull llava:7b-v1.5-q4_0
ollama pull llava:v1.6
----
Now let's try out the models.
== A photo of me
We'll start with the following image of me looking through a magnifying glass.
.Me with a magnifying glass
image::{{<siteurl>}}/uploads/2024/02/images/image1.png[width=500]
Let's create a chat session with the 1.5 version of LLaVA:
[source, bash]
----
ollama run llava:7b-v1.5-q4_0
----
We can then ask it to describe the image:
[source, text]
----
"Can you describe this image? ./images/image.png"
----
.Output
[source, text]
----
Added image './images/image1.png'
The image depicts a bald man wearing glasses, holding up a magnifying glass to his eye. He is standing in front of a black background, possibly to focus on the
subject matter being observed through the lens of the magnifying glass. His attentive gaze suggests that he might be examining an object or studying something in detail.
----
One neat recent feature of Ollama is that we can switch to another model from within the session by calling the `load` command.
So to use the v1.6 model, we would call the following:
[source, bash]
----
/load llava:v1.6
----
.Output
[source, text]
----
Loading model 'llava:v1.6'
----
And if we ask this model to describe the image, we'll see the following output:
[source, text]
----
In the image, there is a man who appears to be in his middle years. He has a bald head and is wearing glasses with black frames and a thin black strap. The man is holding up what looks like a magnifying glass over his eye, as if examining something closely. He has a slight smile on his face and is looking directly at the camera, creating an engaging perspective.
The man is dressed in a casual manner, with a white t-shirt adorned with a small pattern and a pair of dark pants. His stance is relaxed, suggesting he might be in a lighthearted or informal setting.
Behind him, there's a plain black background that contrasts with the man and draws attention to him. The image does not contain any text or additional objects. The focus is solely on the man and his interaction with the magnifying glass.
----
1.6 gives a more detailed answer, but I think it's more or less the same description as 1.5 gave.
== Ollama Python
For the rest of the examples, we're going to use the Ollama Python library, which we can install by running the following:
[source, bash]
----
pip install ollama
----
Next, we'll run the following in a Jupyter notebook or from a Python REPL:
[source, python]
----
import ollama
def generate(model, image, prompt):
stream = ollama.generate(
model=model,
prompt=prompt,
images=[image],
stream=True
)
response = ""
for chunk in stream:
response += chunk['response']
print(chunk['response'], end='', flush=True)
print("")
return response
----
== Captioning an image
Next, we're going to ask models to come up with a caption for the following image:
.An arrow on bricks
image::{{<siteurl>}}/uploads/2024/02/images/image2.png[width=500]
We can do this by running the following code:
[source, python]
----
image = "images/image2.png"
prompt = "Create a caption for this image"
for model in ['llava:7b-v1.5-q4_0', 'llava:v1.6']:
print(f"Model: {model}")
generate(model, image, prompt)
----
.Output
[source, text]
----
Model: llava:7b-v1.5-q4_0
A blue brick wall with a white arrow pointing to the left.
Model: llava:v1.6
"A playful twist on direction, with a blue wall providing the backdrop."
----
If we update the prompt to include the word 'creative', like this:
[source, python]
----
image = "images/image2.png"
prompt = "Create a creative caption for this image"
for model in ['llava:7b-v1.5-q4_0', 'llava:v1.6']:
print(f"Model: {model}")
generate(model, image, prompt)
----
We get a slightly better output:
.Output
[source, text]
----
Model: llava:7b-v1.5-q4_0
"A blue arrow pointing the way, guiding us on our journey."
Model: llava:v1.6
"Guiding the way to endless possibilities: a sign pointing towards an unknown adventure."
----
There's not much in it, but it seems to me that version 1.6 has 'understood' the prompt a bit better.
== Reading a banner image
Next up, let's see if the models can read the text from a banner image of one of my blog posts.
.A blog image
image::{{<siteurl>}}/uploads/2024/02/images/image3.png[width=500]
We'll use the following prompt:
[source, python]
----
image = "images/image3.png"
prompt = "What text is written on this image?"
for model in ['llava:7b-v1.5-q4_0', 'llava:v1.6']:
print(f"Model: {model}")
generate(model, image, prompt)
----
I ran this a few times because it was giving different answers each time.
Here's the output for version 1.5:
.Output
[source, text]
----
Model: llava:7b-v1.5-q4_0
5 Ways to Use AI in Your Business
Running a large language model locally on my laptop.
"Hugging Face"
----
And 1.6:
.Output
[source, text]
----
Model: llava:7b-v1.5-q4_0
The text on the image reads: "HUGGING FACE - RUNNING A LARGE LANGUAGE MODEL LOCALLY ON MY LAPTOP"
The text on the image reads: "Hugging Face" followed by a description of how to use Hugging Face for running a large language model locally on one's laptop.
The text on the image reads: "Hugging Face Running a Large Language Model Locally On My Laptop"
----
It's by no means perfect, but I think version 1.6 wins this one.
== Extracting code from an image
But can it extract code from an image?
These days lots of people create LinkedIn posts with code on images and I'd quite like to get that code and run it locally.
Perhaps LLaVA can help out here.
We're going to use the following image:
.Python code for a window function
image::{{<siteurl>}}/uploads/2024/02/images/image4.png[width=500]
And we'll use the following prompt:
[source, python]
----
image = "images/image4.png"
prompt = "Extract the code from this image"
for model in ['llava:7b-v1.5-q4_0', 'llava:v1.6']:
print(f"Model: {model}")
generate(model, image, prompt)
----
And here's how the models fared, starting with 1.5:
.Output
[source, text]
----
Model: llava:7b-v1.5-q4_0
```sql
a = A + 1
b = B + 1
c = C + 1
d = D + 1
if (a > b and a > c) then begin
RP_RPT("A is greater than B but less than C")
end if
if (b > a and b > c) then begin
RP_RPT("B is greater than A but less than C")
end if
if (c > a and c > b) then begin
RP_RPT("C is greater than A and B")
end if
if (a = b and b = c) then begin
RP_RPT("A,B,C are equal")
end if
if (a < b and a < c) then begin
RP_RPT("A is less than B but greater than C")
end if
if (b < a and b < c) then begin
RP_RPT("B is less than A but greater than C")
end if
if (c < a and c < b) then begin
RP_RPT("C is less than A but greater than B")
end if
if (a > b or not(b = c)) then begin
RP_RPT("A,B are not equal")
end if
if (a < b and a <= C) then begin
RP_RPT("A is less than B but greater than or equal to C")
end if
if (b >= A and b <= C) then begin
RP_RPT("B is within the range of A & C")
end if
if (c > A and c <= B) then begin
RP_RPT("C is within the range of A & B")
end if
end if
if (A = -1024 or B = -1024 or C = -1024) then begin
RP_RPT("A,B,C are all equal to the value of -1024")
end if
if (D >= A + B + C and D <= 2**31 - 1) then begin
RP_RPT("D is within the range of A+B+C")
----
And now 1.6
.Output
[source, text]
----
Model: llava:v1.6
The image contains a screenshot of a computer screen with a code snippet displayed. Here is the extracted code:
```python
import statistics as stats
data = [10, 20, 30, 40, 50]
mean = stats.mode(data)
median = stats.median(data)
stdev = stats.stdev(data)
print('Mean:', mean)
print('Median:', median)
print('Standard Deviation (STDEV):', stdev)
```
This code is written in Python and uses the `statistics` module to calculate the mean, median, and standard deviation of a list of numbers. The list `data` contains five elements, which are then processed using the `mode`, `median`, and `stdev` functions from the `statistics` library.
----
Neither model does a good job when it comes to extracting code.
== Understanding a diagram
And we'll finish off by seeing whether the models can explain this diagram, which shows a relational vs graph approach to modelling a problem.
.Relational vs graph modelling
image::{{<siteurl>}}/uploads/2024/02/images/image5.png[width=500]
We'll use this prompt:
[source, python]
----
image = "images/image5.png"
prompt = "Can you describe this diagram?"
for model in ['llava:7b-v1.5-q4_0', 'llava:v1.6']:
print(f"Model: {model}")
generate(model, image, prompt)
----
Now, let's see how they get on.
First up, 1.5:
.Output
[source, text]
----
Model: llava:7b-v1.5-q4_0
This diagram displays a network of interconnected files, with multiple layers and different shapes. There is a mix of both square and rectangular shapes, which can be seen in various parts of the image. The relationships between these elements are represented by arrows connecting them to one another, indicating that some files are dependent or related to others.
This network diagram appears to represent an intricate system where different files have specific connections with other files within the structure. It provides a visual representation of how these files interact and engage with each other in this complex environment.
----
This doesn't make a whole lot of sense.
Let's see if 1.6 does any better:
.Output
[source, text]
----
Model: llava:v1.6
The image you've shared is a flowchart that describes a process or workflow, likely from an algorithmic or computational context. Here's a description of the elements in the flowchart:
1. **Start Point**: There are two arrows pointing to a starting point labeled "from this..." suggesting the beginning of a process or algorithm.
2. **Sequence of Operations**: The sequence of operations is depicted by a series of boxes, each representing an action or function within the context of the flowchart. These actions seem to be related to data processing or transformation:
- "Node1" appears to represent some sort of input or initial stage.
- "Node2" and "Node3" seem to be intermediate steps or stages in the process.
- "Node4" represents another significant step or decision point.
- "Node5" is the final node, indicating the completion of the workflow or the output of the process.
3. **Arrows**: The arrows show the flow or direction of the operations. They indicate that the starting point leads to Node1 and then moves through the subsequent nodes until it reaches Node5.
4. **Labels on Nodes**: Each node has labels that are likely to represent actions, functions, or data elements involved in the process. However, without more context, these labels are not clearly defined.
5. **Final Arrow**: There's an arrow from Node5 pointing back to "from this..." which may indicate that the output of Node5 is being fed back into another part of the system.
6. **Connections Between Nodes**: The lines connecting the nodes represent the flow or relationship between them, showing that one node leads to the next in a logical sequence.
7. **Annotations**: There are annotations within the flowchart, but they're not fully legible due to the quality of the image. One can make out phrases like "Node1" and "Node5", indicating the names or identities of these nodes in the workflow.
This type of diagram is commonly used in computer science and software engineering to visualize the steps involved in a process, making it easier to understand and communicate the logic behind a particular algorithm or system.
----
It's slightly better, but still doesn't do a good job of explaining the difference between the approaches. | In this post, we'll learn about LLaVA, an open source large multi-modal model. | uploads/2024/02/llava-banner.png | [
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] | llava-large-multi-modal-model-v1.5-v1.6 | https://markhneedham.com/blog/2024/02/04/llava-large-multi-modal-model-v1.5-v1.6 |
false | 2024-01-03 00:44:37 | nvim: Unable to create directory for swap file - recovery impossible: permission denied | [
"nvim",
"til"
] | [
"TIL"
] | :icons: font
I was playing around with https://neovim.io/[neovim^] last week and despite installing it via Homebrew, ran into a weird permissions error.
In this blog post, I'll describe the problem I had and how to solve it.
I installed it like this:
[source, bash]
----
brew install nvim
----
And then tried to create a new file:
[source, bash]
----
nvim foo.py
----
Which resulted in the following error:
.Output
[source, text]
----
E303: Unable to create directory "/Users/markhneedham/.local/state/nvim" for swap file, recovery impossible: permission denied
E303: Unable to open swap file for "foo.py", recovery impossible
----
Hmm, let's take a look at what's in my `~/.local` directory.
[source, bash]
----
ls -alh ~/.local
----
.Output
[source, text]
----
total 0
drwxr-xr-x 6 markhneedham staff 192B 29 Nov 21:40 .
drwxr-x---+ 149 markhneedham staff 4.7K 2 Jan 23:37 ..
drwxr-xr-x@ 5 markhneedham staff 160B 19 Dec 06:22 bin
drwxr-xr-x@ 6 markhneedham staff 192B 29 Nov 21:40 pipx
drwxr-xr-x 5 markhneedham staff 160B 2 Jan 23:31 share
drwxr-xr-x 3 root staff 96B 14 Feb 2023 state
----
Weird, it's made `root` the owner of the `state` directory, whereas I need it to be `markhneedham`.
But we can fix that using `chown`:
[source, bash]
----
sudo chown ~/.local/state markhneedham:staff
----
And now if we open a file with nvim it's happy! | In this post, we'll learn how work about a permissions issue when using nvim. | uploads/2024/01/nvim-banner.png | [
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] | nvim-swap-file-permission-denied | https://markhneedham.com/blog/2024/01/03/nvim-swap-file-permission-denied |
false | 2024-01-04 00:44:37 | ClickHouse: Float equality | [
"clickhouse",
"til"
] | [
"TIL"
] | I've been playing around with https://numpy.org/doc/stable/index.html[NumPy^] data in ClickHouse this week and wanted to share what I learnt when checking for equality of float values.
Let's get going!
== Creating arrays
We're going to use Python's NumPy library to create 5 arrays containing 10 values each:
[source, python]
----
import numpy as np
rng = np.random.default_rng(seed=42)
rng.random(size=(5, 5))
----
.Output
[source, text]
----
array([[0.28138389, 0.29359376, 0.66191651, 0.55703215, 0.78389821],
[0.66431354, 0.40638686, 0.81402038, 0.16697292, 0.02271207],
[0.09004786, 0.72235935, 0.46187723, 0.16127178, 0.50104478],
[0.1523121 , 0.69632038, 0.44615628, 0.38102123, 0.30151209],
[0.63028259, 0.36181261, 0.08764992, 0.1180059 , 0.96189766]])
----
By default, the type of the values is `float64`:
[source, python]
----
rng.random(size=(5, 10)).dtype
----
.Output
[source, text]
----
dtype('float64')
----
But we're going to cast that to `float32` since that's the format of the data in the file that I've been working with.
We'll then save the arrays to a file:
[source, python]
----
values = rng.random(size=(5, 10))
np.save('arrays.npy', values.astype(np.float32))
----
== Querying in ClickHouse
Next, let's download and launch ClickHouse Local:
[source, bash]
----
curl https://clickhouse.com/ | sh
./clickhouse local -m
----
We can then read the `arrays.npy` file:
[source, sql]
----
FROM file('arrays.npy', Npy)
SELECT array, toTypeName(array);
----
.Output
[source, text]
----
┌─array────────────────────────────────────────────────────┬─toTypeName(array)─┐
│ [0.2813839,0.29359376,0.6619165,0.55703217,0.78389823] │ Array(Float32) │
│ [0.66431355,0.40638685,0.8140204,0.16697292,0.022712072] │ Array(Float32) │
│ [0.09004786,0.72235936,0.46187723,0.16127178,0.50104475] │ Array(Float32) │
│ [0.1523121,0.69632035,0.44615626,0.38102123,0.3015121] │ Array(Float32) │
│ [0.6302826,0.36181262,0.08764992,0.1180059,0.9618977] │ Array(Float32) │
└──────────────────────────────────────────────────────────┴───────────────────┘
----
Next, let's see if we can return only the rows where the first item of the array is `0.99237555`.
My first attempt looked like this:
[source, sql]
----
FROM file('arrays.npy', Npy)
SELECT array, toTypeName(array)
WHERE array[1] = 0.66431355
FORMAT Vertical;
----
.Output
[source, text]
----
0 rows in set. Elapsed: 0.002 sec.
----
Computer says no!
Let's write a query that returns the type of our float literal:
[source, sql]
----
SELECT 0.66431355 AS literal, toTypeName(literal);
----
.Output
[source, text]
----
┌────literal─┬─toTypeName(0.66431355)─┐
│ 0.66431355 │ Float64 │
└────────────┴────────────────────────┘
----
Aha, it's a `Float64`, which explains why it didn't match our array of `Float32` values.
There are at least two ways that I know of to rectify this.
We can use the `toFloat32` function to cast the float literal to the right type:
[source, sql]
----
FROM file('arrays.npy', Npy)
SELECT array, toTypeName(array)
WHERE array[1] = toFloat32(0.66431355);
----
Or, we can cast the value to `Float32`
[source, sql]
----
FROM file('arrays.npy', Npy)
SELECT array, toTypeName(array)
WHERE array[1] = CAST(0.66431355 AS Float32);
----
Either way, we get the following result.
.Output
[source, text]
----
Row 1:
──────
┌─array────────────────────────────────────────────────────┬─toTypeName(array)─┐
│ [0.66431355,0.40638685,0.8140204,0.16697292,0.022712072] │ Array(Float32) │
└──────────────────────────────────────────────────────────┴───────────────────┘
----
My takeaway when working with floats (or even decimals) is to make sure that you know the underlying data types that you're working with. | In this post, we'll learn how to check for equality of float values in ClickHouse. | uploads/2024/01/clickhouse-floats-banner.png | [
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] | clickhouse-float-equality | https://markhneedham.com/blog/2024/01/04/clickhouse-float-equality |
false | 2024-01-05 00:44:37 | Pandas: Exclude columns using regex | [
"pandas",
"til"
] | [
"TIL"
] | After a few months of using ClickHouse, I've got quite used to using the https://clickhouse.com/docs/en/sql-reference/statements/select#except[`SELECT <expr> EXCEPT`^] modifier, which lets you remove columns based on a regular expression.
I wanted to do something similar when working with some data in Pandas and in this blog we'll explore how to do that.
We're gonna be working with a CSV file of UK energy and gas tariffs for one of the energy providers.
The CSV file looks like this:
.tariffs.csv
[format="csv", options="header"]
|===
include::content/2024/01/05/tariffs.csv[]
|===
We can read it with Pandas like this:
[source, python]
----
import pandas as pd
tariffs = pd.read_csv("tariffs.csv")
print(tariffs.to_string(index=False))
----
.Output
[source, text]
----
energyType standingCharge unitRate startDate endDate
electricity 47.560 29.380 2024-01-01 2024-03-30
gas 29.600 7.450 2024-01-01 2024-03-30
electricity 44.961 26.758 2023-10-01 2023-12-31
gas 27.726 6.594 2023-10-01 2023-12-31
electricity 44.961 29.428 2023-07-01 2023-09-30
gas 27.726 7.179 2023-07-01 2023-09-30
electricity 44.961 49.117 2023-04-01 2023-06-30
gas 27.726 11.990 2023-04-01 2023-06-30
electricity 40.646 65.555 2023-01-01 2023-03-30
gas 27.128 16.229 2023-01-01 2023-03-30
electricity 40.646 50.630 2022-10-01 2022-12-31
gas 27.128 14.021 2022-10-01 2022-12-31
----
Now, let's say we only want to return fields that contain the term `Date`.
We can use the `loc` function where we only include columns that contain this term as the column argument:
[source, python]
----
print(
(tariffs
.loc[:, tariffs.columns.str.contains("Date")]
.to_string(index=False)
)
)
----
.Output
[source, text]
----
startDate endDate
2024-01-01 2024-03-30
2024-01-01 2024-03-30
2023-10-01 2023-12-31
2023-10-01 2023-12-31
2023-07-01 2023-09-30
2023-07-01 2023-09-30
2023-04-01 2023-06-30
2023-04-01 2023-06-30
2023-01-01 2023-03-30
2023-01-01 2023-03-30
2022-10-01 2022-12-31
2022-10-01 2022-12-31
----
And what about if we want to get rid of the `Date` fields?
We can add the `~` prefix:
[source, python]
----
print(
(tariffs
.loc[:, ~tariffs.columns.str.contains("Date")]
.to_string(index=False)
)
)
----
.Output
[source, text]
----
energyType standingCharge unitRate
electricity 47.560 29.380
gas 29.600 7.450
electricity 44.961 26.758
gas 27.726 6.594
electricity 44.961 29.428
gas 27.726 7.179
electricity 44.961 49.117
gas 27.726 11.990
electricity 40.646 65.555
gas 27.128 16.229
electricity 40.646 50.630
gas 27.128 14.021
---- | In this post, we'll learn how to exclude columns in Pandas using a regular expression. | uploads/2024/01/pandas-banner.png | [
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] | pandas-exclude-columns-regex | https://markhneedham.com/blog/2024/01/05/pandas-exclude-columns-regex |
false | 2024-01-02 00:44:37 | ClickHouse: How does a number have a set number of decimal places? | [
"clickhouse",
"til"
] | [
"TIL"
] | :icons: font
I've been working with a dataset in ClickHouse where I compute currency values and I really struggled to figure out how to get numbers whose decimal part is divisible by 10 to have a fixed number of decimal places.
If you want to do that too, hopefully, this blog post will help.
Let's start by seeing what happens if we output the number `12.40`
[source, sql]
----
SELECT 12.40 AS number;
----
.Output
[source, sql]
----
┌─number─┐
│ 12.4 │
└────────┘
----
The trailing `0` has been stripped.
But maybe we can fix that by casting it to a Decimal type with 2 decimal places?
[source, sql]
----
SELECT CAST(12.40 AS Decimal(10,2)) AS number;
----
.Output
[source, sql]
----
┌─number─┐
│ 12.4 │
└────────┘
----
Hmm, nope.
What about if we coerce the value with the `toDecimal32` function instead?
[source, sql]
----
SELECT toDecimal32(12.40, 2) AS number;
----
.Output
[source, sql]
----
┌─number─┐
│ 12.4 │
└────────┘
----
Still nope.
At this point I was pretty stuck - Google didn't have any suggestions and even ChatGPT didn't know what to do.
Lucky for me, my colleague https://github.com/Avogar[Pavel^] came to the rescue by pointing me to the https://clickhouse.com/docs/en/operations/settings/formats#output_format_decimal_trailing_zeros[`output_format_decimal_trailing_zeros`^] setting.
If we enable that, we'll see our trailing 0 as long as we have a Decimal type.
[source, sql]
----
SELECT 12.40 AS number,
toDecimal32(number, 2) AS number2,
CAST(number AS Decimal(10,2)) AS number3
SETTINGS output_format_decimal_trailing_zeros=1;
----
.Output
[source, sql]
----
┌─number─┬─number2─┬─number3─┐
│ 12.4 │ 12.40 │ 12.40 │
└────────┴─────────┴─────────┘
----
Thanks Pavel! | In this post, we'll learn how to format a Decimal number to have a set/fixed number of decimal places. | uploads/2024/01/clickhouse-decimal-banner.png | [
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] | clickhouse-set-number-decimal-places | https://markhneedham.com/blog/2024/01/02/clickhouse-set-number-decimal-places |
false | 2024-01-28 00:44:37 | Ollama is on PyPi | [
"ollama",
"generative-ai",
"til"
] | [
"TIL"
] | :icons: font
This week https://ollama.ai/[Ollama^] released a Python/PyPi library to go with their awesome tool for running LLMs on your own machine.
You still need to download and run Ollama, but after that you can do almost everything from the library.
In this blog post, we're going to take it for a spin.
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/JwYwPiOh72w?si=oOsZFPmSAhF2kPDK" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
====
== Setup
You can install the library by running the following command:
[source, bash]
----
pip install ollama
----
And then import the library from your Python REPL or Jupyter notebook:
[source, python]
----
import ollama
----
The most obvious first task is installing one of the models.
At the moment there isn't a way to list all of the available models, but you can see what's available on the https://ollama.ai/library[Ollama models page^].
== Installing a model
If we want to install the Mistral 7B model from Mistral AI, we could run the following:
[source, python]
----
ollama.pull("mistral")
----
There isn't any feedback when this is running though, so I'd suggest that you run this type of command using the CLI so that you can see how much is left to download.
Perhaps that will change in future versions of the library though, we'll have to see.
Once the library has installed, you can view it like this:
[source, python]
----
ollama.show("mistral")
----
.Output
[source, python]
----
{
'license': '...',
'modelfile': '# Modelfile generated by "ollama show"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM mistral:latest\n\nFROM /Users/markhneedham/.ollama/models/blobs/sha256:e8a35b5937a5e6d5c35d1f2a15f161e07eefe5e5bb0a3cdd42998ee79b057730\nTEMPLATE """[INST] {{ .System }} {{ .Prompt }} [/INST]"""\nPARAMETER stop "[INST]"\nPARAMETER stop "[/INST]"',
'parameters': 'stop "[INST]"\nstop "[/INST]"',
'template': '[INST] {{ .System }} {{ .Prompt }} [/INST]',
'details': {
'parent_model': '',
'format': 'gguf',
'family': 'llama',
'families': ['llama'],
'parameter_size': '7B',
'quantization_level': 'Q4_0'
}
}
----
== The `chat` function
There are two functions that you can use to interact with the models - `chat` and `generate`.
Let's start with the `chat` function, which we can use like this:
[source, python]
----
stream = ollama.chat(
model="mistral",
messages = [{
"role": "user",
'content': 'What is a Large Language Model in 3 bullet points?'
}],
stream=True
)
for chunk in stream:
print(chunk['message']['content'], end='', flush=True)
----
The output varies each time you run this, but this is what it showed when I ran it, taking around 3 seconds:
.Output
[source, text]
----
1. A large language model is a type of artificial intelligence (AI) system designed to generate human-like text based on given prompts or context. It uses deep learning techniques, specifically recurrent neural networks and transformers, to analyze vast amounts of text data and learn patterns in language.
2. Large language models can perform various natural language processing tasks such as translation, summarization, question answering, and text generation. They can also be fine-tuned on specific datasets to improve performance in certain domains or applications.
3. The size of a large language model refers to the number of parameters it has, which determines its capacity to learn complex patterns and relationships in language. For example, BERT, a popular large language model, has over 110 billion parameters, making it one of the largest models to date. These models require significant computational resources and advanced hardware such as GPUs to train and run efficiently.
----
If we were building a chat app, we could keep an array of prompts and responses so that the LLM could use the full context when replying to the latest prompt.
== The `generate` function
Alternatively, if you just want to do a one-off prompt and get a response, you can use the `generate` function.
Multi-modal modals are all the rage these days, so let's see if we can get the LLaVA model to describe the following image:
.A colourful llama
image::{{<siteurl>}}/uploads/2024/01/02024_2475991309.png[width=500]
[source, python]
----
file_path = "/Users/markhneedham/Downloads/02024_2475991309.png"
stream = ollama.generate(
model="llava",
prompt="Please describe what's in this image:",
images=[file_path],
stream=True
)
for chunk in stream:
print(chunk['response'], end='', flush=True)
----
This one takes between 3-7 seconds to run and the output is as follows:
.Output
[source, text]
----
The image features a colorful stuffed animal, possibly a llama or an alpaca, wearing a pair of sunglasses and a feathery rainbow-colored scarf. It is standing in front of a pink wall with green dots on it. This fun and playful scene appears to be the focus of the picture.
----
== Configuring your own model
You can also create your own model based on any of the other models that you've downloaded or using any of the https://www.markhneedham.com/blog/2023/10/18/ollama-hugging-face-gguf-models/[GGUF files from Hugging Face^].
I'm gonna make a more creative version of the Mistral model by setting the temperature to 0.99, as shown below:
[source, python]
----
modelfile='''
FROM mistral
PARAMETER temperature 0.99
'''
ollama.create(model='creative-mistral', modelfile=modelfile)
----
The new model will be created almost instantly and is called `creative-mistral`.
We can then call that one like this:
[source, python]
----
stream = ollama.chat(
model="creative-mistral",
messages = [{
"role": "user",
'content': 'What is a Large Language Model in 3 bullet points?'
}],
stream=True
)
for chunk in stream:
print(chunk['message']['content'], end='', flush=True)
----
.Output
[source, text]
----
* A large language model is a type of artificial intelligence (AI) system designed to understand and generate human-like text based on input data.
* It is trained on vast amounts of text data using deep learning techniques, allowing it to learn patterns, context, and relationships within language.
* Capable of generating coherent and contextually relevant responses or completions to textual prompts, they are used in various applications such as chatbots, content generation, translation, summarization, and more.
----
== Conclusion
I'm super excited about this library being released and I think it makes Ollama even more useful than it already was.
I'm definitely looking forward to playing around with this more over the coming weeks. | In this post, we'll learn how to use the Ollama library that was recently released on PyPi. | uploads/2024/01/ollama-pypi-banner.png | [
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] | ollama-now-on-pypi | https://markhneedham.com/blog/2024/01/28/ollama-now-on-pypi |
false | 2024-01-19 00:44:37 | ClickHouse: Configure default output format | [
"clickhouse",
"til"
] | [
"TIL"
] | :icons: font
When running queries with https://clickhouse.com/docs/en/operations/utilities/clickhouse-local[ClickHouse Local^], the results are rendered back to the screen in a table format in blocks.
This default format is called https://clickhouse.com/docs/en/interfaces/formats#prettycompact[`PrettyCompact`] and most of the time this works fine, but sometimes you can end up with multiple mini-tables.
In this blog post, we're going to learn how to change the default format so that all the results show in one table.
But first, let's see how the problem manifests.
I've been playing around with the Kaggle https://www.kaggle.com/datasets/imperialwarrior/singapore-crypto?select=metadata.csv[Singaporean Cryptocurrency / Crypto Dataset^] and I wrote the following query after downloading the ZIP file that contains all the data.
[source, sql]
----
from file('data/Singapore Crypto.zip :: metadata.csv', CSVWithNames)
select *
LIMIT 10;
----
.Output
[source, text]
----
┌─Coin Pair Name──────┬─Coin Pair Symbol─┬─Filename──────┐
│ Bitcoin SGD │ BTC-SGD │ BTC-SGD.csv │
│ Ethereum SGD │ ETH-SGD │ ETH-SGD.csv │
│ Tether USDt SGD │ USDT-SGD │ USDT-SGD.csv │
│ BNB SGD │ BNB-SGD │ BNB-SGD.csv │
│ USD Coin SGD │ USDC-SGD │ USDC-SGD.csv │
│ XRP SGD │ XRP-SGD │ XRP-SGD.csv │
│ Lido Staked ETH SGD │ STETH-SGD │ STETH-SGD.csv │
│ Cardano SGD │ ADA-SGD │ ADA-SGD.csv │
│ Dogecoin SGD │ DOGE-SGD │ DOGE-SGD.csv │
└─────────────────────┴──────────────────┴───────────────┘
┌─Coin Pair Name───┬─Coin Pair Symbol─┬─Filename─────┐
│ Wrapped TRON SGD │ WTRX-SGD │ WTRX-SGD.csv │
└──────────────────┴──────────────────┴──────────────┘
----
You can see that the results are rendered in two different tables.
We can fix that by specifying `FORMAT PrettyMonoBlock` at the end of the query, like this:
[source, sql]
----
from file('data/Singapore Crypto.zip :: metadata.csv', CSVWithNames)
select *
LIMIT 10
FORMAT PrettyMonoBlock;
----
We then get a single table result:
.Output
[source, text]
----
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Coin Pair Name ┃ Coin Pair Symbol ┃ Filename ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ Bitcoin SGD │ BTC-SGD │ BTC-SGD.csv │
├─────────────────────┼──────────────────┼───────────────┤
│ Ethereum SGD │ ETH-SGD │ ETH-SGD.csv │
├─────────────────────┼──────────────────┼───────────────┤
│ Tether USDt SGD │ USDT-SGD │ USDT-SGD.csv │
├─────────────────────┼──────────────────┼───────────────┤
│ BNB SGD │ BNB-SGD │ BNB-SGD.csv │
├─────────────────────┼──────────────────┼───────────────┤
│ USD Coin SGD │ USDC-SGD │ USDC-SGD.csv │
├─────────────────────┼──────────────────┼───────────────┤
│ XRP SGD │ XRP-SGD │ XRP-SGD.csv │
├─────────────────────┼──────────────────┼───────────────┤
│ Lido Staked ETH SGD │ STETH-SGD │ STETH-SGD.csv │
├─────────────────────┼──────────────────┼───────────────┤
│ Cardano SGD │ ADA-SGD │ ADA-SGD.csv │
├─────────────────────┼──────────────────┼───────────────┤
│ Dogecoin SGD │ DOGE-SGD │ DOGE-SGD.csv │
├─────────────────────┼──────────────────┼───────────────┤
│ Wrapped TRON SGD │ WTRX-SGD │ WTRX-SGD.csv │
└─────────────────────┴──────────────────┴───────────────┘
----
So far, so good.
But how can we make `PrettyMonoBlock` the default format?
When using ClickHouse Local or the ClickHouse Client, we can provide a config file in XML or YAML format.
The setting we need to define is `format` and the value should be `PrettyMonoBlock`:
.config-local.xml
[source, xml]
----
<?xml version="1.0" ?>
<clickhouse>
<format>PrettyMonoBlock</format>
</clickhouse>
----
or
.config-local.yaml
[source, xml]
----
format: PrettyMonoBlock
----
We can then have ClickHouse Local/Client use the config by passing the file using the `-C` parameter.
[source, bash]
----
./clickhouse local -m -C config-local.xml
----
or
[source, bash]
----
./clickhouse client -m -C config-local.yaml
----
In addition, if you're using ClickHouse Local, it will automatically use a config file named `config.xml`.
Once we've got that config setup, we can re-run the query and it will return us a single table without needing to specify the format in the query. | In this post, we'll learn how to configure the default output format used by ClickHouse. | uploads/2024/01/clickhouse-default-format-banner.png | [
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] | clickhouse-configure-output-format | https://markhneedham.com/blog/2024/01/19/clickhouse-configure-output-format |
false | 2024-01-12 00:44:37 | An introduction to Retrieval Augmented Generation | [
"retrieval-augmented-generation",
"rag",
"generative-ai",
"chromadb",
"chroma",
"langchain",
"til"
] | [
"TIL"
] | Retrieval Augmented Generation (RAG) is a technique used with Large Language Models (LLM) where you *augment* the prompt with data **retrieved** from a data store so that the LLM can *generate* a better answer to the question that is being asked.
In this blog post, we're going to learn the basics of RAG by creating a Question and Answer system on top of the https://en.wikipedia.org/wiki/2023_Wimbledon_Championships[2023 Wimbledon Championships^] Wikipedia page.
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/Cim1lNXvCzY?si=FCBtGd7w_TZ_k4W0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
====
== Setup
I'm using the Poetry package manager, but feel free to use whichever tooling you like to follow along.
Below is my Poetry config file:
.pyproject.toml
[source, toml]
----
[tool.poetry]
name = "intro-to-rag"
version = "0.1.0"
description = ""
authors = ["Mark Needham"]
readme = "README.md"
[tool.poetry.dependencies]
python = ">=3.11,<3.12"
fastembed = "^0.1.3"
chromadb = "^0.4.22"
langchain = "^0.1.0"
wikipedia = "^1.4.0"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
----
== Initialising an LLM
We're going to start by initialising the Mixtral Large Language Model, which we have running locally using https://ollama.ai/[Ollama^].
We'll use the popular https://www.langchain.com/[LangChain^] library to glue everything together.
[source, python]
----
from langchain_community.llms import Ollama
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = Ollama(
model="mixtral",
callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
)
----
We can check that's all wired together by running the following:
[source, python]
----
llm.invoke("Who won the men's singles at Wimbledon 2023?")
----
.Output
[source, text]
----
I'm unable to provide that information as there has been no Wimbledon tournament held in 2023 yet. The last Men's Singles champion at Wimbledon was Novak Djokovic in 2022. It's recommended to check the official Wimbledon website or reliable news sources for updated and accurate information on the latest championship results.
----
Often an LLM will hallucinate an answer to a question if it doesn't know the answer i.e. it will come up with something plausible but completely made up.
In this case, it's actually done a good job by telling us that it doesn't know.
== Augmented Generation
One way that we could help the LLM out here is by creating a new prompt where we manually paste in the contents of the whole page (which we called the `context`) and ask the LLM to use that context to answer the question.
This would work, but it's not all that efficient - although we don't have the issue of having to pay for tokens because we're using a local model, it tends to be the case that an LLM will take longer to answer a longer prompt.
So instead, we want to identify the part of the page that likely has the answer to the question and only paste that bit into the prompt.
Let's see how the LLM responds to the question if we augment the prompt manually i.e. Augmented Generation!
In LangChain we can construct a prompt using the following code:
[source, python]
----
from langchain.prompts import PromptTemplate
template = """You are a bot that answers user questions about Wimbledon using only the context provided.
If you don't know the answer, simply state that you don't know.
{context}
Question: {input}"""
prompt = PromptTemplate(
template=template, input_variables=["context", "input"]
)
----
We can then populate the prompt like this:
[source, python]
----
prompt.format(
context="Gentlemen's singles Carlos Alcaraz def. Serbia Novak Djokovic 1–6, 7–6(8–6), 6–1, 3–6, 6–4",
input="Who won the men's singles at Wimbledon 2023?"
)
----
.Output
[source, text]
----
You are a bot that answers user questions about Wimbledon using only the context provided.
If you don't know the answer, simply state that you don't know.
Gentlemen's singles Carlos Alcaraz def. Serbia Novak Djokovic 1–6, 7–6(8–6), 6–1, 3–6, 6–4
Question: Who won the men's singles at Wimbledon 2023?
----
We can then feed that prompt into the LLM like this:
[source, python]
----
llm.invoke(
prompt.format(
context="Gentlemen's singles Carlos Alcaraz def. Serbia Novak Djokovic 1–6, 7–6(8–6), 6–1, 3–6, 6–4",
input="Who won the men's singles at Wimbledon 2023?"
)
)
----
.Output
[source, text]
----
Based on the provided context, Carlos Alcaraz of Spain won the Gentlemen's singles at Wimbledon in 2023. He defeated Novak Djokovic of Serbia with a score of 1–6, 7–6(8–6), 6–1, 3–6, 6–4.
----
And there we go, now we have the correct answer.
But our solution so far requires us to find the appropriate information and feed it to the LLM - it would be better if we could automate that part of the process.
== An introduction to vector search
The most popular way to do this is to use vector search.
At a high level, this involves the following steps:
* Create embeddings (arrays of numbers) for each of the chunks of text on our page using an embedding algorithm. Those are our *chunk embeddings*.
* Store those embeddings somewhere
* Use that same embedding algorithm to create an embedding for our search query. This is our *search query embedding*.
* Search the embedding storage system to find the `k` most similar of the *chunk embeddings* to the *search query embedding*.
* Retrieve the text for the matching *chunk embedding* and put that text into the prompt as the context
=== Generate embeddings
Let's start by creating embeddings of the Wimbledon 2023 page.
We can retrieve the page using the `WikipediaLoader`:
[source, python]
----
from langchain.document_loaders import WikipediaLoader
search_term = "2023 Wimbledon Championships"
docs = WikipediaLoader(query=search_term, load_max_docs=1).load()
----
This gives us back a LangChain `Document` that contains the whole page.
As we discussed above, we're going to break the page up into chunks.
One way to do this is using the `RecursiveCharacterTextSplitter` which goes through the text and extracts chunks of a certain length with optional overlap between each chunk:
[source, python]
----
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size = 150,
chunk_overlap = 50,
length_function = len,
is_separator_regex = False,
)
chunks = text_splitter.split_documents(docs)
chunks[0]
----
.Output
[source, text]
----
Document(
page_content='The 2023 Wimbledon Championships was a Grand Slam tennis tournament that took place at the All England Lawn Tennis and Croquet Club in Wimbledon, London, United Kingdom.',
metadata={
'title': '2023 Wimbledon Championships',
'summary': 'The 2023 Wimbledon Championships was a Grand Slam tennis tournament that took place at the All England Lawn Tennis and Croquet Club in Wimbledon, London, United Kingdom.',
'source': 'https://en.wikipedia.org/wiki/2023_Wimbledon_Championships'
}
)
----
=== Generate and Store embeddings
Next, we're going to generate embeddings for each chunk of text using OpenAI and then store the embedding and the text in ChromaDB.
[source, python]
----
from langchain.vectorstores import Chroma
from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
embeddings = FastEmbedEmbeddings(model_name="BAAI/bge-base-en-v1.5")
store = Chroma.from_documents(
chunks,
embeddings,
ids = [f"{item.metadata['source']}-{index}" for index, item in enumerate(chunks)],
collection_name="Wimbledon-Embeddings",
persist_directory='db',
)
store.persist()
----
=== Querying the embeddings
Now we're going to query the embeddings.
For some reason that I haven't quite figured out, the similarity search doesn't return the right chunk unless I increase the value of `k`.
I've tried some different embedding algorithms, but it doesn't seem to change the result, so we'll work around this for now.
[source, python]
----
result = store.similarity_search(
query="Who won the men's singles at Wimbledon 2023?",
k=10
)
[doc.page_content for doc in result]
----
.Output
[source, text]
----
[
'The 2023 Wimbledon Championships was a Grand Slam tennis tournament that took place at the All England Lawn Tennis and Croquet Club in Wimbledon,',
'two decades since he won the tournament for the first time in 2003.',
'The tournament was played on grass courts, with all main draw matches played at the All England Lawn Tennis and Croquet Club, Wimbledon, from 3 to 16',
'Tennis and Croquet Club, Wimbledon, from 3 to 16 July 2023. Qualifying matches were played from 26 to 29 June 2023 at the Bank of England Sports',
"singles & doubles events for men's and women's wheelchair tennis players. This edition features gentlemen's and ladies' invitational doubles",
"=== Ladies' singles ===\n\n Markéta Vondroušová def. Ons Jabeur, 6–4, 6–4\n\n\n=== Gentlemen's doubles ===",
'26 to 29 June 2023 at the Bank of England Sports Ground in Roehampton.',
'Tour calendars under the Grand Slam category, as well as the 2023 ITF tours for junior and wheelchair competitions respectively.',
"=== Gentlemen's doubles ===\n\n Wesley Koolhof / Neal Skupski def. Marcel Granollers / Horacio Zeballos, 6–4, 6–4\n\n\n=== Ladies' doubles ===",
"== Events ==\n\n\n=== Gentlemen's singles ===\n\n Carlos Alcaraz def. Novak Djokovic 1–6, 7–6(8–6), 6–1, 3–6, 6–4\n\n\n=== Ladies' singles ==="
]
----
We can see that the chunk that has the answer is in last place.
As I said, it's not clear to me why those other chunks are ranking higher, but that's what's happening!
== Retrieval Augmented Generation
We've now got all the pieces in place to do Retrieval Augmented Generation, so the final step is to glue them all together.
In LangChain's terminology, we need to create a _chain_ that combines the vector store with the LLM.
We can do this with the following code:
[source, python]
----
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.chains import create_retrieval_chain
retriever = store.as_retriever(search_kwargs={
'k': 10
})
combine_docs_chain = create_stuff_documents_chain(llm, prompt)
chain = create_retrieval_chain(retriever, combine_docs_chain)
----
Which we can then call like this:
[source, python]
----
result = chain.invoke({
"input": "Who won the men's singles at Wimbledon 2023?"
})
----
.Output
[source, text]
----
Carlos Alcaraz of Spain won the men's singles at Wimbledon 2023. He defeated Novak Djokovic in the final with a score of 1–6, 7–6(8–6), 6–1, 3–6, 6–4.
---- | In this post, we'll learn how to do Retrieval Augmented Generation with ChromaDB, Ollama, LangChain, and Mixtral from Mistral AI to answer questions about the Wimbledon 2023 tennis tournament. | uploads/2024/01/rag-banner.png | [
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] | intro-to-retrieval-augmented-generation | https://markhneedham.com/blog/2024/01/12/intro-to-retrieval-augmented-generation |
false | 2023-03-20 02:44:37 | DuckDB/Python: Cannot combine LEFT and RIGHT relations of different connections! | [
"duckdb"
] | [
"duckdb"
] | I've been playing around with DuckDB over the weekend and ran into an interesting problem while using the Relational API in the Python package.
We're going to explore that in this blog post.
== Set up
To get started, let's install DuckDB:
[source, bash]
----
pip install duckdb
----
And now let's open a Python shell and import the package:
[source, python]
----
import duckdb
----
Next, let's create a DuckDB connection and import the `httpfs` module, which we'll use in just a minute:
[source, python]
----
con = duckdb.connect('file.db')
con.sql("INSTALL httpfs")
con.sql("LOAD httpfs")
----
== Importing ATP matches
We're going to import some matches from the ATP tennis tour using the following code:
[source, python]
----
csv_files = [
f"https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_{year}.csv"
for year in range(1968,2024)
]
con.execute("""
CREATE OR REPLACE TABLE matches AS
SELECT * FROM read_csv_auto($1, types={'winner_seed': 'VARCHAR', 'loser_seed': 'VARCHAR'})
""", [csv_files])
----
We can check that the table has been populated by running the following command:
[source, python]
----
con.table("matches").project("tourney_date, winner_name, loser_name, score").limit(5)
----
.Output
[source, python]
----
┌──────────────┬─────────────────┬─────────────────┬─────────┐
│ tourney_date │ winner_name │ loser_name │ score │
│ int64 │ varchar │ varchar │ varchar │
├──────────────┼─────────────────┼─────────────────┼─────────┤
│ 19680708 │ Douglas Smith │ Peter Ledbetter │ 6-1 7-5 │
│ 19680708 │ Louis Pretorius │ Maurice Pollock │ 6-1 6-1 │
│ 19680708 │ Cecil Pedlow │ John Mulvey │ 6-2 6-2 │
│ 19680708 │ Tom Okker │ Unknown Fearmon │ 6-1 6-1 │
│ 19680708 │ Armistead Neely │ Harry Sheridan │ 6-2 6-4 │
└──────────────┴─────────────────┴─────────────────┴─────────┘
----
== Nadal vs Djokovic
I wanted to find the matches between Rafael Nadal and Novak Djokovic and in order to keep using the Relational API, I needed to use the `sql` function to do this, which meant I couldn't pass in any parameters.
I therefore ended up with the following code:
[source, python]
----
players_df = pd.DataFrame({"player1": ["Rafael Nadal"], "player2": ["Novak Djokovic"]})
players = duckdb.from_df(players_df).set_alias("players")
----
I then wrote the following code to find the matches won by Nadal:
[source, python]
----
matches = con.table("matches")
matches.join(players,
condition="matches.winner_name = players.player1 AND matches.loser_name = players.player2",
how="inner"
)
----
Unfortunately, this threw an error:
[source, text]
----
duckdb.Error: Cannot combine LEFT and RIGHT relations of different connections!
----
The mistake I made here is that the `players` and `matches` relations were created on different DuckDB connections, as the error suggests.
If we want to get them on the same connection, we need to update the following line:
[source, python]
----
players = duckdb.from_df(players_df)
----
To instead read:
[source, python]
----
players = con.from_df(players_df).set_alias("players")
----
This will make sure that the `players` relation is part of the same connection as `matches`.
I hadn't realised that calling `duckdb.from_df` actually adds the relation to the default DuckDB connection.
If we then run the join query again:
[source, python]
----
(matches
.join(players,
condition="matches.winner_name = players.player1 AND matches.loser_name = players.player2",
how="inner")
.project("tourney_name, tourney_date, round")
.limit(5)
)
----
We'll get the following output:
[source, text]
----
┌──────────────────────┬──────────────┬─────────┐
│ tourney_name │ tourney_date │ round │
│ varchar │ int64 │ varchar │
├──────────────────────┼──────────────┼─────────┤
│ Roland Garros │ 20060529 │ QF │
│ Indian Wells Masters │ 20070305 │ F │
│ Rome Masters │ 20070507 │ QF │
│ Roland Garros │ 20070528 │ SF │
│ Wimbledon │ 20070625 │ SF │
└──────────────────────┴──────────────┴─────────┘
----
Success! | In this post we'll learn how to solve an error that you might run into when using DuckDB's Relational API. | uploads/2023/03/duckdb-join-banner.png | [
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] | duckdb-cannot-combine-left-right-relations | https://markhneedham.com/blog/2023/03/20/duckdb-cannot-combine-left-right-relations |
false | 2023-03-10 02:44:37 | Apache Pinot: Geospatial - java.nio.BufferUnderflowException: null | [
"pinot"
] | [
"pinot"
] | I've been working on a blog post showing how to use https://dev.startree.ai/docs/pinot/recipes/geospatial-indexing?utm_source=medium&utm_medium=direct&utm_campaign=dr_markhneedham_emea_gb[Geospatial indexes^] in Apache Pinot and ran into an interesting exception that I'll explain in this blog post.
== Set up
But first, let's take a look at the structure of the data that I'm ingesting from Apache Kafka.
Below is an example of one of those events:
[source, json]
----
{
"trainCompany": "London Overground",
"atocCode": "LO",
"lat": 51.541615,
"lon": -0.122528896,
"ts": "2023-03-10 11:35:20",
"trainId": "202303107145241"
}
----
As you've probably guessed, I'm importing the locations of trains in the UK.
I created the following schema:
.Pinot Schema
[source, json]
----
{
"schemaName": "trains",
"dimensionFieldSpecs": [
{"name": "trainCompany", "dataType": "STRING"},
{"name": "trainId", "dataType": "STRING"},
{"name": "atocCode", "dataType": "STRING"},
{"name": "point", "dataType": "BYTES"}
],
"dateTimeFieldSpecs": [
{
"name": "ts",
"dataType": "TIMESTAMP",
"format": "1:MILLISECONDS:EPOCH",
"granularity": "1:MILLISECONDS"
}
]
}
----
The first three columns are automatically mapped from the data source.
The `point` column is going to store a `Point` object based on the lat/lon values in the event.
We'll create that object in the table config, which you can see below:
.Pinot Table Config
[source, json]
----
{
"tableName": "trains",
"tableType": "REALTIME",
"segmentsConfig": {
"timeColumnName": "ts",
"schemaName": "trains",
"replication": "1",
"replicasPerPartition": "1"
},
"fieldConfigList": [
{
"name": "point",
"encodingType":"RAW",
"indexType":"H3",
"properties": {
"resolutions": "5"
}
}
],
"tableIndexConfig": {
"loadMode": "MMAP",
"noDictionaryColumns": ["point"],
"streamConfigs": {
"streamType": "kafka",
"stream.kafka.topic.name": "trains",
"stream.kafka.broker.list": "kafka-geospatial:9093",
"stream.kafka.consumer.type": "lowlevel",
"stream.kafka.consumer.prop.auto.offset.reset": "smallest",
"stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
"stream.kafka.decoder.class.name": "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder",
"realtime.segment.flush.threshold.rows":"1000",
"realtime.segment.flush.threshold.time":"1h"
}
},
"ingestionConfig": {
"transformConfigs": [
{
"columnName": "point",
"transformFunction": "STPoint(lon, lat, 1)"
}
]
},
"tenants": {},
"metadata": {}
}
----
The function under `ingestionConfig.transformConfigs` creates a Point Geomtry object, which is stored in the `point` column.
We also create a Geospatial index on the `point` column, which is defined under `fieldConfigList`.
== The BufferUnderflowException
Once I'd created this table the data started ingesting, but I was sometimes ending up with the following error on the Pinot server:
[source, text]
----
pinot-server-geospatial | 2023/03/09 12:49:05.889 ERROR [BaseCombineOperator] [pqw-5] Caught exception while processing query: QueryContext{_tableName='trains_REALTIME', _subquery=null, _selectExpressions=[ts, trainId, atocCode, trainCompany, stastext(point)], _aliasList=[null, null, null, null, null], _filter=stwithin(point,'84000000010000000600000000bfc097f3a00000004049c107e8691db8bfbbd01b7fffffff4049c10bc3b2dbd8bfbac631c00000014049bf9dcb81ef38bfc0ae8a200000014049be7fdbcf81e0bfc3b13b000000004049bf9dee86bbf8bfc097f3a00000004049c107e8691db8') = '1', _groupByExpressions=null, _havingFilter=null, _orderByExpressions=[ts DESC], _limit=10, _offset=0, _queryOptions={responseFormat=sql, groupByMode=sql, timeoutMs=10000}, _expressionOverrideHints={}, _explain=false}
pinot-server-geospatial | java.nio.BufferUnderflowException: null
pinot-server-geospatial | at java.nio.Buffer.nextGetIndex(Buffer.java:643) ~[?:?]
pinot-server-geospatial | at java.nio.HeapByteBuffer.get(HeapByteBuffer.java:165) ~[?:?]
pinot-server-geospatial | at org.apache.pinot.segment.local.utils.GeometrySerializer.readGeometry(GeometrySerializer.java:83) ~[pinot-all-0.12.0-jar-with-dependencies.jar:0.12.0-118f5e065cb258c171d97a586183759fbc61e2bf]
pinot-server-geospatial | at org.apache.pinot.segment.local.utils.GeometrySerializer.readGeometry(GeometrySerializer.java:79) ~[pinot-all-0.12.0-jar-with-dependencies.jar:0.12.0-118f5e065cb258c171d97a586183759fbc61e2bf]
pinot-server-geospatial | at org.apache.pinot.segment.local.utils.GeometrySerializer.deserialize(GeometrySerializer.java:68) ~[pinot-all-0.12.0-jar-with-dependencies.jar:0.12.0-118f5e065cb258c171d97a586183759fbc61e2bf]
pinot-server-geospatial | at org.apache.pinot.core.geospatial.transform.function.BaseBinaryGeoTransformFunction.transformGeometryToIntValuesSV(BaseBinaryGeoTransformFunction.java:99) ~[pinot-all-0.12.0-jar-with-dependencies.jar:0.12.0-118f5e065cb258c171d97a586183759fbc61e2bf]
pinot-server-geospatial | at org.apache.pinot.core.geospatial.transform.function.StWithinFunction.transformToIntValuesSV(StWithinFunction.java:46) ~[pinot-all-0.12.0-jar-with-dependencies.jar:0.12.0-118f5e065cb258c171d97a586183759fbc61e2bf]
pinot-server-geospatial | at org.apache.pinot.core.operator.dociditerators.ExpressionScanDocIdIterator.processProjectionBlock(ExpressionScanDocIdIterator.java:140) ~[pinot-all-0.12.0-jar-with-dependencies.jar:0.12.0-118f5e065cb258c171d97a586183759fbc61e2bf]
pinot-server-geospatial | at org.apache.pinot.core.operator.dociditerators.ExpressionScanDocIdIterator.applyAnd(ExpressionScanDocIdIterator.java:120) ~[pinot-all-0.12.0-jar-with-dependencies.jar:0.12.0-118f5e065cb258c171d97a586183759fbc61e2bf]
pinot-server-geospatial | at org.apache.pinot.core.operator.filter.H3InclusionIndexFilterOperator.getFilterBlock(H3InclusionIndexFilterOperator.java:131) ~[pinot-all-0.12.0-jar-with-dependencies.jar:0.12.0-118f5e065cb258c171d97a586183759fbc61e2bf]
pinot-server-geospatial | at org.apache.pinot.core.operator.filter.H3InclusionIndexFilterOperator.getNextBlock(H3InclusionIndexFilterOperator.java:113) ~[pinot-all-0.12.0-jar-with-dependencies.jar:0.12.0-118f5e065cb258c171d97a586183759fbc61e2bf]
pinot-server-geospatial | at org.apache.pinot.core.operator.filter.H3InclusionIndexFilterOperator.getNextBlock(H3InclusionIndexFilterOperator.java:49) ~[pinot-all-0.12.0-jar-with-dependencies.jar:0.12.0-118f5e065cb258c171d97a586183759fbc61e2bf]
pinot-server-geospatial | at org.apache.pinot.core.operator.BaseOperator.nextBlock(BaseOperator.java:43) ~[pinot-all-0.12.0-jar-with-dependencies.jar:0.12.0-118f5e065cb258c171d97a586183759fbc61e2bf]
----
I did a bit of debugging, which revealed that the value in the `point` column was sometimes an empty byte array.
It turns out that an empty byte array is actually the default value for the `BYTES` column type and that default value was being inserted when my transformation function failed.
And the transformation function failed if either of the lat or lon values were null!
I figured this out by running the following command against the Kafka stream:
[source, bash]
----
kcat -C -b localhost:9092 -t trains -u | jq 'select(.lat == null or .lon == null)'
----
And eventually saw the following output:
[source, text]
----
{
"trainCompany": "London Overground",
"atocCode": "LO",
"lat": null,
"lon": null,
"ts": "2023-03-10 12:56:13",
"trainId": "202303107145033"
}
% Reached end of topic trains [0] at offset 4475923
----
This error was stopping any segments being committed, so I needed to fix it.
The fix that I've come up with is to create a default value that represents a point near the arctic as I'm fairly sure no UK trains will be going that far North!
== Default values for BYTES column
The default value goes in the schema config and it should be a hex encoded value.
I ran the following query to get a Hex encoded representation of a location in the Arctic:
[source, sql]
----
SELECT ST_GeomFromText('POINT (0.6861134172138761 83.5002942140996)')
FROM trains
----
This query returns the value `003fe5f4a42008f90c4054e004d205fbe4`, which I added to my schema, as shown below:
[source, json]
----
{
"schemaName": "trains",
"dimensionFieldSpecs": [
{"name": "trainCompany", "dataType": "STRING"},
{"name": "trainId", "dataType": "STRING"},
{"name": "atocCode", "dataType": "STRING"},
{"name": "point", "dataType": "BYTES", "defaultNullValue": "003fe5f4a42008f90c4054e004d205fbe4"}
],
"dateTimeFieldSpecs": [
{
"name": "ts",
"dataType": "TIMESTAMP",
"format": "1:MILLISECONDS:EPOCH",
"granularity": "1:MILLISECONDS"
}
]
}
----
When we're querying the table we can filter those values out like this:
[source, sql]
----
select $docId, $hostName, $segmentName, *
from trains
where point <> ST_GeomFromText('POINT (0.6861134172138761 83.5002942140996)')
limit 10
----
== The AddTableCommand
When I was trying to create the schema and table from scratch with a default value, I found that I couldn't use the `AddTable` command as it was getting into a mess by trying to decode the default value twice - once in the command itself and once on the Pinot Controller when it received the table config.
The error message looked like this:
[source, text]
----
2023/03/10 13:15:04.401 INFO [AddTableCommand] [main] {"code":400,"error":"Invalid TableConfigs. Cannot convert value: 'AD/l9KQgCPkMQFTgBNIF++Q=' to type: BYTES\n at [Source: (String)\"{\"tableName\":\"trains\",\"schema\":{\"schemaName\":\"trains\",\"primaryKeyColumns\":null,\"dimensionFieldSpecs\":[{\"name\":\"trainCompany\",\"maxLength\":512,\"dataType\":\"STRING\",\"transformFunction\":null,\"defaultNullValue\":\"null\",\"singleValueField\":true,\"virtualColumnProvider\":null,\"defaultNullValueString\":\"null\"},{\"name\":\"trainId\",\"maxLength\":512,\"dataType\":\"STRING\",\"transformFunction\":null,\"defaultNullValue\":\"null\",\"singleValueField\":true,\"virtualColumnProvider\":null,\"defaultNullValueString\":\"null\"},{\"name\":\"at\"[truncated 2231 chars]; line: 1, column: 777] (through reference chain: org.apache.pinot.spi.config.TableConfigs[\"schema\"]->org.apache.pinot.spi.data.Schema[\"dimensionFieldSpecs\"]->java.util.ArrayList[3]->org.apache.pinot.spi.data.DimensionFieldSpec[\"defaultNullValue\"])"}
----
I'm not really sure how to get this to work, but luckily there is a workaround.
First, create the schema:
[source, bash]
----
docker run \
--network geospatial \
-v $PWD/config:/config \
apachepinot/pinot:0.12.0-arm64 AddSchema \
-schemaFile /config/schema.json \
-controllerHost "pinot-controller-geospatial" \
-exec
----
And then create the table using the HTTP API:
[source, bash]
----
curl -X POST http://localhost:9000/tables --data @config/table.json
----
Not ideal, but it works!
== Conclusion
This ended up being super fiddly, but it does work!
If you're stuck on something similar, do join the https://dev.startree.ai/slack-invite?utm_source=medium&utm_medium=direct&utm_campaign=dr_markhneedham_emea_gb[StarTree Community Slack^] and I'll do my best to help. | In this post we'll learn about the BufferUnderflowException when storing Geospatial points in Apache Pinot. | uploads/2023/03/geospatial-underflow-banner.png | [
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] | apache-pinot-geospatial-buffer-underflow | https://markhneedham.com/blog/2023/03/10/apache-pinot-geospatial-buffer-underflow |
false | 2023-03-31 02:44:37 | Tennis Head to Head with DuckDB and Streamlit | [
"duckdb",
"streamlit"
] | [
"duckdb"
] | In this blog post we're going to learn how to build an application to compare the matches between two ATP tennis players.
DuckDB and Streamlit will be our partners in crime for this mission.
== Set up
To get started, let's create a virtual environment:
[source, bash]
----
python -m venv .venv
source .venv/bin/activate
----
And now install some libraries:
[source, bash]
----
pip install duckdb streamlit streamlit-searchbox
----
And now let's open a file, `app.py` and import the packages:
[source, python]
----
import streamlit as st
import duckdb
from streamlit_searchbox import st_searchbox
----
Next, let's create a DuckDB connection and import the `httpfs` module, which we'll use in just a minute:
[source, python]
----
atp_duck = duckdb.connect('atp.duck.db', read_only=True)
atp_duck.sql("INSTALL httpfs")
atp_duck.sql("LOAD httpfs")
----
== Importing ATP matches
We're going to import some matches from the ATP tennis tour using the following code:
[source, python]
----
csv_files = [
f"https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_{year}.csv"
for year in range(1968,2024)
]
atp_duck.execute("""
CREATE OR REPLACE TABLE matches AS
SELECT * FROM read_csv_auto($1, types={'winner_seed': 'VARCHAR', 'loser_seed': 'VARCHAR'})
""", [csv_files])
----
We can check that the table has been populated by running the following command:
[source, python]
----
atp_duck.table("matches").project("tourney_date, winner_name, loser_name, score").limit(5)
----
.Output
[source, python]
----
┌──────────────┬─────────────────┬─────────────────┬─────────┐
│ tourney_date │ winner_name │ loser_name │ score │
│ int64 │ varchar │ varchar │ varchar │
├──────────────┼─────────────────┼─────────────────┼─────────┤
│ 19680708 │ Douglas Smith │ Peter Ledbetter │ 6-1 7-5 │
│ 19680708 │ Louis Pretorius │ Maurice Pollock │ 6-1 6-1 │
│ 19680708 │ Cecil Pedlow │ John Mulvey │ 6-2 6-2 │
│ 19680708 │ Tom Okker │ Unknown Fearmon │ 6-1 6-1 │
│ 19680708 │ Armistead Neely │ Harry Sheridan │ 6-2 6-4 │
└──────────────┴─────────────────┴─────────────────┴─────────┘
----
== Searching for players
Now we're going to create a Streamlit app on top of this data.
Let's start by adding a title:
[source, python]
----
st.title("ATP Head to Head")
----
And now we'll create a function that will let us search for players:
[source, python]
----
def search_players(search_term):
query = '''
SELECT DISTINCT winner_name AS player
FROM matches
WHERE player ilike '%' || $1 || '%'
UNION
SELECT DISTINCT loser_name AS player
FROM matches
WHERE player ilike '%' || $1 || '%'
'''
values = atp_duck.execute(query, parameters=[search_term]).fetchall()
return [value[0] for value in values]
----
Next, we're going to need some search boxes for the players, which is where the https://github.com/m-wrzr/streamlit-searchbox[streamlit_searchbox^] library comes in handy.
[source, python]
----
left, right = st.columns(2)
with left:
player1 = st_searchbox(search_players, label="Player 1", key="player1_search")
with right:
player2 = st_searchbox(search_players, label="Player 2", key="player2_search")
st.markdown("***")
st.header(f"{player1} vs {player2}")
st.error("No matches found between these players.")
----
We can then launch the Streamlit app by running the following command:
[source, bash]
----
streamlit run app.py
----
If we navigate to http://localhost:8501, we'll see the following screen where we can search for players:
image::{{<siteurl>}}/uploads/2023/03/djok-nadal.png[Djokovic vs Nadal]
== Finding matches
Let's now update the app to find matches between players:
[source, python]
----
matches_for_players = atp_duck.execute("""
SELECT tourney_date,tourney_name, surface, round, winner_name, score
FROM matches
WHERE (loser_name = $1 AND winner_name = $2) OR
(loser_name = $2 AND winner_name = $1)
ORDER BY tourney_date DESC
""", [player1, player2]).fetchdf()
----
Let's also count the number of wins that each player has and we'll update the header too:
[source, python]
----
player1_wins = matches_for_players[matches_for_players.winner_name == player1].shape[0]
player2_wins = matches_for_players[matches_for_players.winner_name == player2].shape[0]
st.header(f"{player1} {player1_wins}-{player2_wins} {player2}")
----
If we go back to our web browser, we'll see this:
image::{{<siteurl>}}/uploads/2023/03/djok-nadal-matches.png[Djokovic vs Nadal Matches]
So far, so good.
== Aggregation by surface and round
Let's now do some aggregations.
One of the cool things about DuckDB is that we can query a Pandas DataFrame as if its a table.
The code below groups wins by round and surface:
[source, python]
----
left, right = st.columns(2)
with left:
st.markdown(f'#### By Surface')
by_surface = atp_duck.sql("""
SELECT winner_name AS player, surface, count(*) AS wins
FROM matches_for_players
GROUP BY ALL
""").fetchdf()
st.dataframe(by_surface.pivot(index="surface", columns="player" ,values="wins"))
with right:
st.markdown(f'#### By Round')
by_surface = atp_duck.sql("""
SELECT winner_name AS player, round, count(*) AS wins
FROM matches_for_players
GROUP BY ALL
""").fetchdf()
st.dataframe(by_surface.pivot(index="round", columns="player" ,values="wins"))
----
And if we go back to the web browser:
image::{{<siteurl>}}/uploads/2023/03/djok-nadal-surface-round.png[Djokovic vs Nadal By Round and Surface]
We can now search for other players and see how they've performed against each other.
== Summary
These two tools make it super easy to quickly create data apps.
The full code used in this blog post is on https://gist.github.com/mneedham/e06cc23bccb1da62071efab5ca924ac0[this Gist^]. | In this post we'll learn how to build an ATP head to head app with DuckDB and Streamlit. | uploads/2023/03/duckdb-streamlit-banner.png | [
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] | tennis-head-to-head-duckdb-streamlit | https://markhneedham.com/blog/2023/03/31/tennis-head-to-head-duckdb-streamlit |
false | 2023-04-05 02:44:37 | DuckDB/SQL: Convert epoch to timestamp with timezone | [
"duckdb"
] | [
"duckdb"
] | I've been playing around with the Citi Bike Stations dataset on Kaggle with DuckDB and ran into trouble when trying to convert a column containing epoch timestamps to a timestamp with timezone support.
In this blog we'll learn how to do that, which will at least be helpful to future me, if noone else!
The dataset contains 4GB worth of CSV files, but I've just downloaded a few of them manually for now.
Once I downloaded them, I unpacked the ZIP file and put the CSV files into a `data` directory.
I installed DuckDB as well and then launched it:
[source, bash]
----
./duckdb bikes.duck.db
----
.Output
[source, bash]
----
v0.7.1 b00b93f0b1
Enter ".help" for usage hints
----
Next, we'll create a table called `bikeStations` that's populated from those CSV files, as shown below:
[source, sql]
----
CREATE OR REPLACE TABLE bikeStations AS
FROM read_csv_auto('data/*.csv', types={"station_id": "VARCHAR"})
WHERE missing_station_information = false;
----
The table contains a column called `station_status_last_reported` that we want to convert to a timestamp type.
Let's query that column to view its contents:
[source, sql]
----
select station_status_last_reported
FROM bikeStations
LIMIT 5;
----
.Output
[opts="header"]
|===
| station_status_last_reported
| 1604692013
| 1572853294
| 1630455776
| 1617131040
| 1569461183
|===
We can convert the epoch to a timestamp using the to_timestamp function, like this:
[source, sql]
----
select to_timestamp(station_status_last_reported) AS ts
FROM bikeStations
LIMIT 5;
----
.Output
[opts="header"]
|===
| ts
| 2020-11-06 19:46:53
| 2019-11-04 07:41:34
| 2021-09-01 00:22:56
| 2021-03-30 19:04:00
| 2019-09-26 01:26:23
|===
At the moment this timestamp is using the UTC timezone, but we can't see that unless we cast the result to `timestamptz`:
[source, sql]
----
select to_timestamp(station_status_last_reported)::timestamptz AS tstz
FROM bikeStations
LIMIT 5;
----
.Output
[opts="header"]
|===
| tstz
| 2020-11-06 19:46:53+00
| 2019-11-04 07:41:34+00
| 2021-09-01 00:22:56+01
| 2021-03-30 19:04:00+01
| 2019-09-26 01:26:23+01
|===
But the dataset is actually from New York, so we need to convert it to be in Eastern time instead.
We can do that like this:
[source, sql]
----
select to_timestamp(station_status_last_reported)::TIMESTAMPTZ AT TIME ZONE 'US/Eastern' AS tstz
FROM bikeStations
LIMIT 5;
----
.Output
[opts="header"]
|===
| tstz
| 2020-11-06 14:46:53
| 2019-11-04 02:41:34
| 2021-08-31 19:22:56
| 2021-03-30 14:04:00
| 2019-09-25 20:26:23
|===
And if we want to persist that to the table in another column, we can write the following code:
[source, sql]
----
ALTER TABLE bikeStations ADD COLUMN ts TIMESTAMP;
UPDATE bikeStations
SET ts = to_timestamp(station_status_last_reported)::TIMESTAMPTZ AT TIME ZONE 'US/Eastern';
----
And let's check it worked:
[source, sql]
----
select station_status_last_reported, ts
from bikeStations
LIMIT 5;
----
.Output
[opts="header"]
|===
| station_status_last_reported | ts
| 1604692013 | 2020-11-06 14:46:53
| 1572853294 | 2019-11-04 02:41:34
| 1630455776 | 2021-08-31 19:22:56
| 1617131040 | 2021-03-30 14:04:00
| 1569461183 | 2019-09-25 20:26:23
|=== | In this post we'll learn how to convert an epoch timestamp into a SQL timestamp with timezone support using DuckDB. | uploads/2023/04/epoch-timezone-banner.png | [
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] | duckdb-sql-convert-epoch-timestamp-timezone | https://markhneedham.com/blog/2023/04/05/duckdb-sql-convert-epoch-timestamp-timezone |
false | 2023-04-20 02:44:37 | DuckDB/SQL: Get decade from date | [
"duckdb"
] | [
"duckdb"
] | Working with dates in SQL can sometimes be a bit tricky, especially when you need to extract specific information, like the decade a date belongs to.
In this blog post, we'll explore how to easily obtain the decade from a date using DuckDB, a lightweight and efficient SQL database engine.
First, https://duckdb.org/docs/installation/[install DuckDB^] and launch it:
[source, bash]
----
./duckdb
----
Next, we're going to create a `movies` table that has columns for `title` and `releaseDate`:
[source, sql]
----
create or replace table movies(title VARCHAR, releaseDate DATE);
----
And now let's add some records of famous movies and their release dates:
[source, sql]
----
INSERT INTO movies
VALUES ('Avatar', '2009-12-10'),
('Star Wars: The Force Awakens', '2015-12-15'),
('Titanic', '1997-11-18'),
('The Avengers', '2012-04-25'),
('Jurassic World', '2015-06-09'),
('Furious 7', '2015-04-01');
----
The `releaseDate` column contains the actual day the movie was released, but I want to know the decade.
We can do this by first extracting the year using the `year` function and then dividing that by 10 and multiplying it by 10.
The following query does this:
[source, sql]
----
select title, releaseDate, year(releaseDate)/10*10 AS decade
From movies;
----
.Output
[options="header"]
|===
| title | releaseDate | decade
| Avatar | 2009-12-10 | 2000
| Star Wars: The Force Awakens | 2015-12-15 | 2010
| Titanic | 1997-11-18 | 1990
| The Avengers | 2012-04-25 | 2010
| Jurassic World | 2015-06-09 | 2010
| Furious 7 | 2015-04-01 | 2010
|===
We could then write a new query that counts the number of movies released per decade:
[source, sql]
----
select year(releaseDate)/10*10 AS decade, count(*)
From movies
GROUP BY ALL
----
.Output
[options="header"]
|===
| decade | count_star()
| 2000 | 1
| 2010 | 4
| 1990 | 1
|===
Tada! | In this post we'll learn how to extract the decade from a date using SQL and DuckDB. | uploads/2023/04/get-decade-from-date-banner.png | [
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] | duckdb-sql-decade-from-date | https://markhneedham.com/blog/2023/04/20/duckdb-sql-decade-from-date |
false | 2023-05-17 02:44:37 | Venkat - An inline code snippet execution extension for VS Code (Made in GPT-4) | [
"venkat",
"vscode"
] | [
"VSCode"
] | (Co-authored with https://data-folks.masto.host/@mesirii@chaos.social[Michael Hunger^])
https://twitter.com/venkat_s[Venkat Subramaniam^] is a legendary speaker on the tech conference circuit whose https://www.youtube.com/results?search_query=venkat+subramaniam[presentations^] are famous for executing arbitrary code snippets and showing the results as a tooltip directly in the editor.
This makes it really great for videos or talks as you don't need a second output terminal to run your code and you can just continue explaining what you're doing.
The results go away afterwards, so you don't need to worry about that.
image::{{<siteurl>}}/uploads/2023/05/venkat-textmate.png[A screenshot from a talk by Venkat Subramaniam showing code snippets]
He even published a https://www.agilelearner.com/presentation/5[dedicated free course^] back in 2013 explaining how he does it using the TextMate editor.
I wanted to use a similar effect in my https://youtube.com/@learndatawithmark[LearnDataWithMark videos^], so my friend Michael Hunger and I were unsuccessfully looking for a VS Code extension that does it.
So what else could we do at 10 pm than *fire up ChatGPT4* to help us build a VS Code extension.
Keep in mind that we've never built a VS Code extension before, nore are we that good at Typescript.
We were following in the footsteps of Simon Willisons, https://simonwillison.net/2023/Mar/27/ai-enhanced-development/[who pointed out^] that with the assistance of GPT4, he's now able to take on and complete ambitious projects in a few hours instead of a few days.
GPT-4 did an excellent job, guiding us through the initial setup of all the `package.json` and `launch.json` files and putting together a minimal example for executing selected Python code.
Initially, just passing the code directly to the interpreter and capturing the result.
[source,typescript]
----
import * as vscode from 'vscode';
import { exec } from 'child_process';
export function activate(context: vscode.ExtensionContext) {
let disposable = vscode.commands.registerCommand('extension.executePythonAndComment', async () => {
let editor = vscode.window.activeTextEditor;
if (editor) {
let document = editor.document;
let selection = editor.selection;
let code = document.getText(selection);
exec(`python -c "${code}"`, (error, stdout, stderr) => {
if (error || stderr) {
console.log(`error: ${error ? error.message : stderr}`);
return;
}
editor.edit(editBuilder => {
let position = new vscode.Position(selection.end.line + 1, 0);
editBuilder.insert(position, `# Result: ${stdout}`);
});
});
}
});
context.subscriptions.push(disposable);
}
----
We evolved it to run the whole file up to the end of the current line, which made it necessary to write out the content to a file and execute that file.
This design choice fortuitously opened up the opportunity to support different languages and not have to fiddle around with escaping symbols to avoid shell expansion.
Within two hours we had a working prototype and were happy.
GPT also provided us with instructions to package and publish the extension.
Somewhat surprisingly, it also did a better job of explaining the registration and publication process than the official documentation.
The most tricky part was selecting an icon, but we think we came up with a good one in the end!
image::{{<siteurl>}}/uploads/2023/05/venkat-marketplace.png[link=https://marketplace.visualstudio.com/items?itemName=MarkNeedham.venkat,alt="Emoji with mustache and glasses, Install Venkat from the VS Code marketplace",target="_blank"]
You can install https://marketplace.visualstudio.com/items?itemName=MarkNeedham.venkat[Venkat from the VS Code Marketplace^] and an animation showing how it works in Python is shown below.
image::{{<siteurl>}}/uploads/2023/05/venkat-demo.gif[]
This was fun, so we continued to add a few more features - GPT-4 helped with all the steps because what do we know!
We think it's now in a state where it can be tried out by other people, so we'd love for you to give it a try.
And if you have any feature requests please let us know, GPT-4 is patiently waiting for some more work to do! | In this post we introduce venkat, a VS Code extension that lets you execute inline code snippets. | uploads/2023/05/venkat-banner.png | [
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] | venkat-inline-code-snippet-execution-vs-code-execution | https://markhneedham.com/blog/2023/05/17/venkat-inline-code-snippet-execution-vs-code-execution |
false | 2023-05-09 02:44:37 | Python 3.10: Pattern matching with match/case | [
"python",
"til"
] | [
"TIL"
] | :icons: font
I've been reading https://www.oreilly.com/library/view/fluent-python-2nd/9781492056348/[Fluent Python^] and learnt about pattern matching with the match/case statement, introduced in Python 3.10.
You can use it instead of places where you'd otherwise use `if`, `elif`, `else` statements.
I created a small example to understand how it works.
The following function takes in a list where the first argument should be `foo`, followed by a variable number of arguments, which we print to the console:
[source, python]
----
def parse_if(x):
if x[0] == "foo":
if len(x[1:]) > 1:
print(f"Multiple values: {'_'.join([str(n) for n in x[1:]])}")
elif len(x[1:]) == 1:
if x[1] == 5:
print(f"Only one value and it's the magical 5")
else:
print(f"Only one value: {x[1]}")
else:
print("No values")
else:
raise SyntaxError(f"Invalid expression: {x}")
----
Let's run that function with some different arguments:
[source, python]
----
parse_if(["foo", *(range(0,10))])
parse_if(["foo", 1])
parse_if(["foo", 5dada])
parse_if(["foo"])
parse_if(["bar"])
----
.Output
[source, text]
----
Multiple values: 0_1_2_3_4_5_6_7_8_9
Only one value: 1
Only one value and it's the magical 5
No values
Traceback (most recent call last):
File "/Users/markhneedham/projects/fluent_python/destruct.py", line 40, in <module>
fn(["bar"])
File "/Users/markhneedham/projects/fluent_python/destruct.py", line 14, in parse_if
raise SyntaxError(f"Invalid expression: {x}")
SyntaxError: Invalid expression: ['bar']
----
A version of that function rewritten to use pattern matching would look like this:
[source, python]
----
def parse_match(x):
match x: <1>
case ['foo']:
print("No values")
case ['foo', 5]: <2>
print(f"Only one value and it's the magical 5")
case ['foo', other]: <3>
print(f"Only one value: {other}")
case ['foo', *other]: <4>
print(f"Multiple values: {'_'.join([str(n) for n in other])}")
case _: <5>
raise SyntaxError(f"Invalid expression: {x}")
----
<1> `x` is our pattern matching subject
<2> We can match literal values inside a case statement
<3> Or we can assign to a variable instead.
<4> The `*` operator matches multiple values
<5> `_` is a wildcard character that matches everything else
I think this second example is a little bit easier to read than the first and I can see this syntax being super useful for more gnarly combinations of predicates. | In this post we'll learn about Python 3.10's pattern matching with the match/case statement. | uploads/2023/05/python-pattern-banner.png | [
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] | python-pattern-matching-match-case | https://markhneedham.com/blog/2023/05/09/python-pattern-matching-match-case |
false | 2023-05-31 02:44:37 | Debezium: Capture changes from MySQL | [
"debezium",
"til"
] | [
"Debezium"
] | I've been working on a Real-Time Analytics https://odsc.com/speakers/building-a-real-time-analytics-application-for-a-pizza-delivery-service/[workshop that I'm going to be presenting^] at the ODSC Europe conference in June 2023 and I wanted to have Debezium publish records from a MySQL database without including the schema.
I'm using the `debezium/connect:2.3` Docker image to run Debezium locally and I have a MySQL database running with the hostname `mysql` on port `3306`.
Below is the way that I configured this:
[source, bash]
----
curl -X PUT \
-H "Content-Type:application/json" \
http://localhost:8083/connectors/mysql/config \
-d '{
"connector.class": "io.debezium.connector.mysql.MySqlConnector",
"database.hostname": "mysql",
"database.port": 3306,
"database.user": "debezium",
"database.password": "dbz",
"database.server.name": "mysql",
"database.server.id": "223344",
"database.allowPublicKeyRetrieval": true,
"database.history.kafka.bootstrap.servers": "kafka:9092",
"database.history.kafka.topic": "mysql-history",
"schema.history.internal.kafka.bootstrap.servers": "kafka:9092",
"schema.history.internal.kafka.topic": "mysql-schema-history",
"database.include.list": "pizzashop",
"time.precision.mode": "connect",
"topic.prefix": "mysql",
"include.schema.changes": false
}'
----
My database `pizzashop` contains the tables `products` and `users`, which will map to the following Kafka topics:
* `products` -> `mysql.pizzashop.products`
* `users` -> `mysql.pizzashop.users`
We can use `kcat` to see the messages published to these topics:
[source, bash]
----
kcat -C -b localhost:29092 -t mysql.pizzashop.users -c1 -u | jq
----
.Output
[source, json]
----
{
"before": null,
"after": {
"id": 1,
"first_name": "Kismat",
"last_name": "Shroff",
"email": "drishyamallick@hotmail.com",
"residence": "575, Edwin Circle, Bahraich-255519",
"lat": "TYu5Xg==",
"lon": "Ac6oUak=",
"created_at": "2023-05-31T08:44:19Z",
"updated_at": 1685522659000
},
"source": {
"version": "2.3.0.Beta1",
"connector": "mysql",
"name": "mysql",
"ts_ms": 1685524951000,
"snapshot": "first_in_data_collection",
"db": "pizzashop",
"sequence": null,
"table": "users",
"server_id": 0,
"gtid": null,
"file": "binlog.000002",
"pos": 156,
"row": 0,
"thread": null,
"query": null
},
"op": "r",
"ts_ms": 1685524951843,
"transaction": null
}
----
We can then go to our MySQL table and update one of the products:
[source, sql]
----
update products
set name = 'Super Awesome Moroccan Pasta Pizza - Veg'
where id = 1;
----
And then if we query the Kafka topic again, we'll see this output:
.Output
[source, json]
----
{
"before": {
"id": 1,
"name": "Moroccan Spice Pasta Pizza - Veg",
"description": "A pizza loaded with a spicy combination of Harissa sauce and delicious pasta.",
"category": "veg pizzas",
"price": 335,
"image": "https://www.dominos.co.in//files/items/MoroccanSpicePPVG_N.jpg",
"created_at": "2023-05-31T08:44:19Z",
"updated_at": 1685522659000
},
"after": {
"id": 1,
"name": "Super Awesome Moroccan Pasta Pizza - Veg",
"description": "A pizza loaded with a spicy combination of Harissa sauce and delicious pasta.",
"category": "veg pizzas",
"price": 335,
"image": "https://www.dominos.co.in//files/items/MoroccanSpicePPVG_N.jpg",
"created_at": "2023-05-31T08:44:19Z",
"updated_at": 1685527662000
},
"source": {
"version": "2.3.0.Beta1",
"connector": "mysql",
"name": "mysql",
"ts_ms": 1685527662000,
"snapshot": "false",
"db": "pizzashop",
"sequence": null,
"table": "products",
"server_id": 1,
"gtid": null,
"file": "binlog.000002",
"pos": 414,
"row": 0,
"thread": 4714,
"query": null
},
"op": "u",
"ts_ms": 1685527691344,
"transaction": null
}
----
Let's now delete one record:
[source, sql]
----
DELETE FROM pizzashop.products WHERE id =1;
----
And back to Kafka:
.Output
[source, json]
----
{
"before": {
"id": 1,
"name": "Super Awesome Moroccan Pasta Pizza - Veg",
"description": "A pizza loaded with a spicy combination of Harissa sauce and delicious pasta.",
"category": "veg pizzas",
"price": 335,
"image": "https://www.dominos.co.in//files/items/MoroccanSpicePPVG_N.jpg",
"created_at": "2023-05-31T08:44:19Z",
"updated_at": 1685527662000
},
"after": null,
"source": {
"version": "2.3.0.Beta1",
"connector": "mysql",
"name": "mysql",
"ts_ms": 1685527882000,
"snapshot": "false",
"db": "pizzashop",
"sequence": null,
"table": "products",
"server_id": 1,
"gtid": null,
"file": "binlog.000002",
"pos": 1143,
"row": 0,
"thread": 4868,
"query": null
},
"op": "d",
"ts_ms": 1685527882035,
"transaction": null
}
---- | In this post we'll learn how to use Debezium to capture records added, updated, and deleted from a MySQL database. | uploads/2023/02/dbz-banner.png | [
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] | debezium-capture-changes-mysql | https://markhneedham.com/blog/2023/05/31/debezium-capture-changes-mysql |
false | 2023-05-30 02:44:37 | Node.js: Minifying JSON documents | [
"til",
"nodejs"
] | [
"nodejs"
] | I often need to minimise the schema and table config files that you use to configure Apache Pinot so that they don't take up so much space.
After doing this manually for ages, I came across the https://www.npmjs.com/package/json-stringify-pretty-compact[json-stringify-pretty-compact^] library, which speeds up the process.
We can install it like this:
[source, bash]
----
npm install json-stringify-pretty-compact
----
And then I have the following script:
.minify.mjs
[source, javascript]
----
import pretty from 'json-stringify-pretty-compact';
let inputData = '';
process.stdin.on('data', (chunk) => {
inputData += chunk;
});
process.stdin.on('end', () => {
const value = JSON.parse(inputData);
console.log(pretty(value));
});
process.stdin.resume();
----
Imagine we then have the following file:
.config/schema.json
[source, json]
----
{
"schemaName": "parkrun",
"primaryKeyColumns": ["competitorId"],
"dimensionFieldSpecs": [
{
"name": "runId",
"dataType": "STRING"
},
{
"name": "eventId",
"dataType": "STRING"
},
{
"name": "competitorId",
"dataType": "LONG"
},
{
"name": "rawTime",
"dataType": "INT"
},
{
"name": "lat",
"dataType": "DOUBLE"
},
{
"name": "lon",
"dataType": "DOUBLE"
},
{
"name": "location",
"dataType": "BYTES"
},
{
"name": "course",
"dataType": "STRING"
}
],
"metricFieldSpecs": [
{
"name": "distance",
"dataType": "DOUBLE"
}
],
"dateTimeFieldSpecs": [{
"name": "timestamp",
"dataType": "TIMESTAMP",
"format" : "1:MILLISECONDS:EPOCH",
"granularity": "1:MILLISECONDS"
}]
}
----
The field specs take up so much unnecessary space, so let's get our script to sort that out:
[source, bash]
----
cat config/schema.json | node minify.mjs
----
.Output
[source, json]
----
{
"schemaName": "parkrun",
"primaryKeyColumns": ["competitorId"],
"dimensionFieldSpecs": [
{"name": "runId", "dataType": "STRING"},
{"name": "eventId", "dataType": "STRING"},
{"name": "competitorId", "dataType": "LONG"},
{"name": "rawTime", "dataType": "INT"},
{"name": "lat", "dataType": "DOUBLE"},
{"name": "lon", "dataType": "DOUBLE"},
{"name": "location", "dataType": "BYTES"},
{"name": "course", "dataType": "STRING"}
],
"metricFieldSpecs": [{"name": "distance", "dataType": "DOUBLE"}],
"dateTimeFieldSpecs": [
{
"name": "timestamp",
"dataType": "TIMESTAMP",
"format": "1:MILLISECONDS:EPOCH",
"granularity": "1:MILLISECONDS"
}
]
}
---- | In this post we'll learn how to minify JSON documents so that they don't take up so much space. | uploads/2023/05/nodejs-banner.png | [
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] | nodejs-minify-json-documents | https://markhneedham.com/blog/2023/05/30/nodejs-minify-json-documents |
false | 2023-05-24 02:44:37 | DuckDB/SQL: Create a list of numbers | [
"sql",
"duckdb",
"til"
] | [
"TIL"
] | :icons: font
While in DuckDB land, I wanted to create a list of numbers, just like you can with Cypher's https://neo4j.com/docs/cypher-manual/current/functions/list/#functions-range[`range`^] function.
After a bit of searching that resulted in very complex solutions, I came across the Postgres https://www.postgresql.org/docs/current/functions-srf.html[`generate_series`^] function, which does the trick.
We can use it in place of a table, like this:
[source, sql]
----
SELECT *
FROM generate_series(1, 10);
----
[options="header"]
.Output
|===
|generate_series
|1
|2
|3
|4
|5
|6
|7
|8
|9
|10
|===
As you can see from the results, this returns 10 rows with one number per row.
Alternatively, we can use it inside a `SELECT` clause, like this:
[source, sql]
----
SELECT generate_series(1, 10);
----
[options="header"]
.Output
|===
|generate_series(1, 10)
|[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
|===
This time it returns one row with an array containing 10 values.
I later learnt that there is also a https://duckdb.org/docs/sql/functions/nested.html#range-functions[`range`^] function, exactly like the Cypher function that I mentioned at the beginning of the post.
The difference between `range` and `generate_series` is that for `range``, the stop parameter is exclusive, whereas for `generate_series` it is inclusive.
So if we asked `range` to generate numbers between 1 and 11 with a step of 2, it would result in the following output, which excludes 11:
[source, sql]
----
SELECT range(1, 11, 2);
----
[options="header"]
.Output
|===
|range(1, 11, 2)
|[1, 3, 5, 7, 9]
|=== | In this post we'll learn how to create a list of numbers using DuckDB. | uploads/2023/05/range-banner.png | [
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] | duckdb-sql-create-list-numbers | https://markhneedham.com/blog/2023/05/24/duckdb-sql-create-list-numbers |
false | 2023-05-23 02:44:37 | Arc Browser: Building a plugin (Boost) with help from ChatGPT | [
"chatgpt",
"arc",
"til"
] | [
"ChatGPT"
] | I've been using the https://arc.net/[Arc Browser^] for a couple of months now and one of my favourite things is the simplicity of the plugin (or as they call it, 'Boost') functionality.
I wanted to port over a Chrome bookmark that I use to capture the podcasts that I've listened to on https://player.fm/[Player.FM^].
In this blog post I'll show how ChatGPT helped me convert the bookmark code to an Arc Boost.
I started with this code that finds all the listened to podcasts and then writes them out to the console:
[source, javascript]
----
javascript:(()=>{
function decodeHtml(html) {
var txt = document.createElement("textarea");
txt.innerHTML = html;
return txt.value;
}
let rows = $("article[data-played-at]").map(function(idx, ele) {
let row = "* Listened to " + $(ele).attr('data-title') + " by " + $(ele).find("div.title").text() + " - https://player.fm/series/" + $(ele).attr('data-series-id') + "/" + $(ele).attr('data-slug');
return decodeHtml(row);
}).get();
setTimeout(async()=>{
console.log(rows.join("\n"));
alert(rows.length + " podcasts copied")
}, 2000)}
)()
----
An example of the output is shown below:
[source, text]
----
* Listened to A groundbreaking new proof for Pythagoras’ Theorem? by More or Less: Behind the Stats - https://player.fm/series/1301260/a-groundbreaking-new-proof-for-pythagoras-theorem
* Listened to Twitter storm by Trending - https://player.fm/series/1301465/twitter-storm
* Listened to After Pulse: Suddenly Unemployed by Community Pulse - https://player.fm/series/1409158/after-pulse-suddenly-unemployed
----
I was relying on jQuery to do find the parts of the page, but that isn't available in an Arc Boost, so I need to use the built in APIs instead.
ChatGPT guided me towards the `document.querySelectorAll` function and converted this:
[source, javascript]
----
$("article[data-played-at]")
----
to:
[source, javascript]
----
document.querySelectorAll("article[data-played-at]")
----
But then I wasn't sure how to call the `map` function on that output, so I asked ChatGPT again.
image::{{<siteurl>}}/uploads/2023/04/chatgpt-opener.png[ChatGPT - Iterate over querySelectorAll]
So I did that and then needed to work out how to extract attributes from each element.
The function calls that ChatGPT told me to make didn't exist, so I had to fall back on the documentation (I know!).
I eventually ended up with the following code to extract the rows:
[source, javascript]
----
let rows = Array.from(document.querySelectorAll("article[data-played-at]")).map(function(ele) {
let row = "* Listened to " + ele.attributes['data-title'].value +
" by " + ele.querySelector("div.title").textContent +
" - https://player.fm/series/" + ele.attributes['data-series-id'].value +
"/" + ele.attributes['data-slug'].value;
return decodeHtml(row);
});
----
Next I asked it to add a button to the page that I could click on to copy the listened episodes to the clipboard.
It first suggested using `navigator.clipboard`, but that wasn't working for me at the time, so it suggested I used `document.execCommand('copy')` instead.
The full code for the plugin looks like this:
[source, javascript]
----
function decodeHtml(html) {
var txt = document.createElement("textarea");
txt.innerHTML = html;
return txt.value;
}
// Create an image
const buttonDiv = document.createElement("div");
buttonDiv.style.display = "flex";
buttonDiv.style.justifyContent = "center";
buttonDiv.style.marginBottom = "16px";
const button = document.createElement("button");
button.innerHTML = `<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16">
<path d="M11 0H3a2 2 0 0 0-2 2v9h2V2h8V2a2 2 0 0 0-2-2zm1 4H4v9h8V4z"/>
</svg> Copy Podcasts`;
button.style.display = "flex";
button.style.alignItems = "center";
button.style.gap = "8px";
button.style.backgroundColor = "white";
button.style.border = "1px solid #ccc";
button.style.borderRadius = "4px";
button.style.padding = "8px";
button.onclick = function () {
let rows = Array.from(document.querySelectorAll("article[data-played-at]")).map(function(ele) {
let row = "* Listened to " + ele.attributes['data-title'].value +
" by " + ele.querySelector("div.title").textContent +
" - https://player.fm/series/" + ele.attributes['data-series-id'].value +
"/" + ele.attributes['data-slug'].value;
return decodeHtml(row);
});
const textToCopy = rows.join("\n");
const textArea = document.createElement('textarea');
textArea.value = textToCopy;
document.body.appendChild(textArea);
textArea.select();
document.execCommand('copy');
document.body.removeChild(textArea);
console.log(textToCopy);
alert(rows.length + " podcasts copied")
};
buttonDiv.appendChild(button);
const preamble = document.querySelector(".preamble");
preamble.insertAdjacentElement("beforebegin", buttonDiv);
----
You can see the Boost in action below:
image::{{<siteurl>}}/uploads/2023/05/arc-plugin.gif[Animation of an Arc Boost copying listened episodes from Player.FM to the clipboard] | In this post I'll explain how I built a plugin for the Arc Browser with help from ChatGPT. | uploads/2023/05/arc-banner.png | [
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] | arc-browser-creating-boost-plugin-with-chatgpt | https://markhneedham.com/blog/2023/05/23/arc-browser-creating-boost-plugin-with-chatgpt |
false | 2023-05-15 02:44:37 | Cropping a video using FFMPEG | [
"ffmpeg",
"til"
] | [
"TIL"
] | :icons: font
I needed to crop a video that I used as part of a video on my YouTube channel, https://www.youtube.com/shorts/7950UzRDZBE[Learn Data With Mark^], and Camtasia kept rendering a black screen.
So I had to call for FFMPEG!
== Cropping the bottom of a video
My initial video was 2160 x 3840 but I didn't need the bottom 1920 pixels because I'm using that part of the screen for a video of me.
If I was just rendering that video exactly as it is I wouldn't have bothered cropping it, but because I wanted to zoom into different sections I needed to crop out the bottom bit.
I adapted an example from https://ottverse.com/crop-a-video-with-ffmpeg-crop-filter/[this article from OTTVerse^] as my first cut:
[source, bash]
----
ffmpeg -i ConfigSegThresholdDemo.mp4 \
-filter_complex "[0:v]crop=in_w:in_h-1920:0:0[cropped]" \
-map "[cropped]" \
-r output.mp4
----
This command tells FFMPEG to crop a region:
* whose width is equal to the source video's width,
* whose height is equal to the source video's height minus 1920 pixels,
* and the origin is at [0,0]
This worked, but it lost the quality that I had in the initial video.
I fixed this by also passing the https://stackoverflow.com/questions/25569180/ffmpeg-convert-without-loss-quality[`qscale`^] flag:
[source, bash]
----
ffmpeg -i ConfigSegThresholdDemo.mp4 \
-filter_complex "[0:v]crop=in_w:in_h-1920:0:0[cropped]" \
-map "[cropped]" \
-qscale 0 \
-r output.mp4
----
I think it worked out pretty well, but https://youtube.com/shorts/7950UzRDZBE?feature=share[watch the video^] and let me know if you agree!
== Cropping the top and bottom of a video
For another video that started off 2160 x 3840, I wanted to take off the top 620 pixels and the bottom 370 pixels.
I did it by running the following command:
[source, bash]
----
ffmpeg -i PivotDemo.mp4 \
-filter_complex "[0:v]crop=2160:2850:0:620[cropped]" \
-map "[cropped]" \
-qscale 0 \
PivotDemo-Cropped.mp4
----
Let's break down the crop parameters, `crop=2160:2850:0:620`
* `2160` - That's the width of region that I want to crop (i.e. keep the width the same)
* `2850` - The height of the region that I want to crop (`3840 - 620 (top crop) - 370 (bottom crop)`)
* `0`- The `x` pixel from where the cropping should begin (i.e. 0 pixels from the left)
* `620` - The `y` pixel from where the cropping should begin (i.e. 620 pixels from the top) | In this post we'll learn how to use the FFMPEG command line tool to crop part of a video. | uploads/2023/05/ffmpeg-banner.png | [
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] | cropping-video-ffmpeg | https://markhneedham.com/blog/2023/05/15/cropping-video-ffmpeg |
false | 2023-05-13 02:44:37 | Python: Naming slices | [
"python",
"til"
] | [
"TIL"
] | :icons: font
Another gem from https://www.oreilly.com/library/view/fluent-python-2nd/9781492056348/[Fluent Python^] is that you can name slices.
How did I not know that?!
Let's have a look how it works using an example of a https://www.autocheck.com/vehiclehistory/vin-basics[Vehicle Identification Number^], which has 17 characters that act as a unique identifier for a vehicle.
Different parts of that string mean different things.
So given the following VIN:
[source, python]
----
vin = "2B3HD46R02H210893"
----
We can extract components like this:
[source, python]
----
print(f"""
World manufacturer identifier: {vin[0:3]}
Vehicle Descriptor: {vin[3:9]}
Vehicle Identifier: {vin[9:17]}
""".strip())
----
If we run this code, we'll see the following output:
.Output
[source, text]
----
World manufacturer identifier: 2B3
Vehicle Descriptor: HD46R0
Vehicle Identifiern: 2H210893
----
Let's say we want to reuse those slices elsewhere in our code.
Maybe when we write the code we can remember what each of the slice indexes mean, but for future us it would help to name them, which we can do using the https://docs.python.org/3.11/library/functions.html#slice[`slice`^] function.
Our code would then look like this:
[source, python]
----
world_manufacturer_id = slice(0,3)
vehicle_descriptor = slice(3,9)
vehicle_identifier = slice(9,17)
print(f"""
World manufacturer identifier: {vin[world_manufacturer_id]}
Vehicle Descriptor: {vin[vehicle_descriptor]}
Vehicle Identifier: {vin[vehicle_identifier]}
""".strip())
---- | In this post we'll learn about Python's named slices and show how to use that to extract parts of a vehicle identification number. | uploads/2023/05/naming-slices-banner.png | [
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] | python-naming-slices | https://markhneedham.com/blog/2023/05/13/python-naming-slices |
false | 2023-05-25 02:44:37 | DuckDB: Ingest a bunch of CSV files from GitHub | [
"duckdb",
"til"
] | [
"TIL"
] | :icons: font
Jeff Sackmann's https://github.com/jeffsackmann/tennis_atp/[tennis_atp^] repository is one of the best collections of tennis data and I wanted to ingest the ATP Tour singles matches using the DuckDB CLI.
In this blog post we'll learn how to do that.
Usually when I'm ingesting data into DuckDB I'll specify the files that I want to ingest using the wildcard syntax.
In this case that would mean running a query like this:
[source, sql]
----
CREATE OR REPLACE TABLE matches AS
SELECT *
FROM "https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_*.csv"
----
But we can't use that technique for files on GitHub because it's not a file system.
If we run the query above, we'll get the following error message:
[source, sql]
----
Error: Invalid Error: HTTP Error: Unable to connect to URL "https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_*.csv": 404 (Not Found)
----
So, instead, we need to construct a list of URLs, which we can do using a combination of the `generate_series` function that I https://www.markhneedham.com/blog/2023/05/24/duckdb-sql-create-list-numbers/[covered in my last blog post^] and the `list_transform` function.
We'll use `generate_series` to create a list of the years, like this:
[source, sql]
----
SELECT range(1968, 1970)
----
[options="header"]
.Output
|===
|range(1968, 1970)
|[1968, 1969]
|===
And then we can use `list_transform` to map or project over each value to construct a URL:
[source, sql]
----
SELECT list_transform(
range(1968, 1970),
y -> 'https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_' || y || '.csv'
) AS files;
----
[options="header"]
.Output
|===
|files
|[https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_1968.csv, https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_1969.csv]
|===
And then finally we can bring it all together to ingest the files from 1968 until 2023:
[source, sql]
----
CREATE OR REPLACE TABLE matches AS
SELECT * FROM read_csv_auto(
list_transform(
range(1968, 2023),
y -> 'https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_' || y || '.csv'
),
types={'winner_seed': 'VARCHAR', 'loser_seed': 'VARCHAR'}
);
----
It'll take a few seconds, but once it's done we can do an exploratory query to make sure everything was ingested:
[source, sql]
----
SELECT count(*)
FROM matches;
----
[options="header"]
.Output
|===
|count(*)
|188191
|=== | In this post we'll learn how to ingest a collection of CSV files from a GitHub repository. | uploads/2023/05/ingest-banner.png | [
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] | duckdb-ingest-csv-files-github | https://markhneedham.com/blog/2023/05/25/duckdb-ingest-csv-files-github |
false | 2023-02-01 02:44:37 | DuckDB: Join based on maximum value in other table | [
"duckdb",
"sql"
] | [
"DuckDB"
] | In this blog post we're going to learn how to write a SQL query to join two tables where one of the tables has multiple rows for each key.
We want to select only the rows that contain the most recent (or maximum) value from that table.
Our story begins with a https://www.youtube.com/watch?v=ogge3kmm_2g[YouTube video^] that I created showing how to query the https://www.kaggle.com/datasets/hugomathien/soccer[European Soccer SQLite database^] with DuckDB.
This database contains lots of different tables, but we are only interested in `Player` and `Player_Attributes`.
Let's have a quick look at the structure of those tables:
[source,sql]
----
DESCRIBE Player;
----
.Output
[format="csv", options="header"]
|===
column_name,column_type,null,key,default,extra
id,BIGINT,YES,,,
player_api_id,BIGINT,YES,,,
player_name,VARCHAR,YES,,,
player_fifa_api_id,BIGINT,YES,,,
birthday,VARCHAR,YES,,,
height,FLOAT,YES,,,
weight,BIGINT,YES,,,
|===
[source,sql]
----
DESCRIBE Player_Attributes;
----
.Output
[format="csv", options="header"]
|===
column_name,column_type,null,key,default,extra
id,BIGINT,YES,,,
player_fifa_api_id,BIGINT,YES,,,
player_api_id,BIGINT,YES,,,
date,VARCHAR,YES,,,
overall_rating,BIGINT,YES,,,
potential,BIGINT,YES,,,
preferred_foot,VARCHAR,YES,,,
attacking_work_rate,VARCHAR,YES,,,
defensive_work_rate,VARCHAR,YES,,,
crossing,BIGINT,YES,,,
finishing,BIGINT,YES,,,
heading_accuracy,BIGINT,YES,,,
short_passing,BIGINT,YES,,,
volleys,BIGINT,YES,,,
dribbling,BIGINT,YES,,,
curve,BIGINT,YES,,,
free_kick_accuracy,BIGINT,YES,,,
long_passing,BIGINT,YES,,,
ball_control,BIGINT,YES,,,
acceleration,BIGINT,YES,,,
sprint_speed,BIGINT,YES,,,
agility,BIGINT,YES,,,
reactions,BIGINT,YES,,,
balance,BIGINT,YES,,,
shot_power,BIGINT,YES,,,
jumping,BIGINT,YES,,,
stamina,BIGINT,YES,,,
strength,BIGINT,YES,,,
long_shots,BIGINT,YES,,,
aggression,BIGINT,YES,,,
interceptions,BIGINT,YES,,,
positioning,BIGINT,YES,,,
vision,BIGINT,YES,,,
penalties,BIGINT,YES,,,
marking,BIGINT,YES,,,
standing_tackle,BIGINT,YES,,,
sliding_tackle,BIGINT,YES,,,
gk_diving,BIGINT,YES,,,
gk_handling,BIGINT,YES,,,
gk_kicking,BIGINT,YES,,,
gk_positioning,BIGINT,YES,,,
gk_reflexes,BIGINT,YES,,,
|===
Let's start by writing a query that finds the `overall_rating` for Lionel Messi by joining the `Player` and `Player_Attributes` tables:
[source, sql]
----
SELECT date, overall_rating
FROM Player
JOIN Player_Attributes ON Player_Attributes.player_api_id = Player.player_api_id
WHERE PLayer.player_name = 'Lionel Messi'
LIMIT 10;
----
.Output
[format="csv", options="header"]
|===
date,overall_rating
"2015-12-17 00:00:00",94
"2015-10-16 00:00:00",94
"2015-09-21 00:00:00",94
"2015-06-26 00:00:00",93
"2015-03-13 00:00:00",93
"2015-02-20 00:00:00",93
"2015-01-23 00:00:00",93
"2014-10-17 00:00:00",93
"2014-09-18 00:00:00",93
"2014-04-25 00:00:00",94
|===
We can see that there are at least 10 entries for Messi in the `Player_Attributes` table.
We only want the most recent ranking, which we can do by using a windowing operation that numbers each row and selects the first one.
We can write the following query to find the top 10 players ordered by their most recent ranking:
[source, sql]
----
SELECT player_name, overall_rating
FROM Player
JOIN (
select *, ROW_NUMBER() OVER (partition by player_api_id ORDER BY date DESC) rn
FROM Player_Attributes
) AS attrs ON attrs.player_api_id = Player.player_api_id AND rn = 1
WHERE overall_rating is not null
ORDER BY overall_rating DESC
LIMIT 10;
----
.Output
[format="csv", options="header"]
|===
player_name,overall_rating
"Lionel Messi",94
"Cristiano Ronaldo",93
"Luis Suarez",90
Neymar,90
"Manuel Neuer",90
"Zlatan Ibrahimovic",89
"Arjen Robben",89
"Andres Iniesta",88
"Mesut Oezil",88
"Eden Hazard",88
|===
This is the query that I included in the video and I didn't know there was a better way until https://www.linkedin.com/feed/update/urn:li:activity:7022269018371682304?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7022269018371682304%2C7023816359096573952%29&dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287023816359096573952%2Curn%3Ali%3Aactivity%3A7022269018371682304%29[Richard Wesley wrote me the following message^], suggesting that I used the `arg_max` function instead.
image::{{<siteurl>}}/uploads/2023/02/richard-wesley-message.png[title="Richard Wesley Message"]
I'd not come across this function before, but I updated my query to use it:
[source, sql]
----
SELECT player_name, arg_max(overall_rating, date) AS overall_rating
FROM Player
JOIN Player_Attributes ON Player_Attributes.player_api_id = Player.player_api_id
GROUP BY ALL
ORDER BY overall_rating DESC
LIMIT 10;
----
The results are the same as with the other query and I can also get rid of the `WHERE overall_rating is not null` clause, which is great.
Thanks Richard! | In this post we'll learn how to write a SQL join where one of the tables has multiple rows per key. | uploads/2023/02/duckdb-join-banner.png | [
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] | duckdb-join-max-value-other-table | https://markhneedham.com/blog/2023/02/01/duckdb-join-max-value-other-table |
false | 2023-11-03 00:44:37 | ClickHouse: Nested type Array(String) cannot be inside Nullable type (ILLEGAL_TYPE_OF_ARGUMENT) | [
"clickhouse",
"clickhouse-local",
"til"
] | [
"TIL"
] | :icons: font
I've been working with some data that's in CSV format but has tab-separated values in some columns.
In this blog post, we're going to learn how to process that data in ClickHouse.
The CSV file that we're working with looks like this:
.data.csv
[format="csv", options="header"]
|===
include::content/2023/11/03/data.csv[]
|===
We'll launch ClickHouse Local (`clickhouse local`) and then run the following:
[source, sql]
----
FROM file('data.csv', CSVWithNames)
SELECT *;
----
.Output
[source, text]
----
┌─value─────┐
│ foo bar │
└───────────┘
----
Let's try to split the `value` field on tab using the `splitByString` function:
[source, sql]
----
FROM file('data.csv', CSVWithNames)
SELECT splitByString('\t', value);
----
.Output
[source, text]
----
Received exception:
Code: 43. DB::Exception: Nested type Array(String) cannot be inside Nullable type: While processing splitByString('\t', value). (ILLEGAL_TYPE_OF_ARGUMENT)
----
The problem we've run into here is that ClickHouse's CSV reader assumes that fields are nullable since it's possible that there could be nulls.
We can disable the `schema_inference_make_columns_nullable` setting to tell it to not treat fields as nullable.
[source, sql]
----
FROM file('data.csv', CSVWithNames)
SELECT splitByString('\t', value)
SETTINGS schema_inference_make_columns_nullable = 0;
----
.Output
[source, text]
----
┌─splitByString('\t', value)─┐
│ ['foo','bar'] │
└────────────────────────────┘
----
Alternatively, we can use the `assumeNotNull` function if we want it to keep all other fields nullable:
[source, sql]
----
FROM file('data.csv', CSVWithNames)
SELECT splitByString('\t', assumeNotNull(value));
----
.Output
[source, text]
----
┌─splitByString('\t', assumeNotNull(value))─┐
│ ['foo','bar'] │
└───────────────────────────────────────────┘
----
But this function will return an arbitrary value if a null is found.
So let's say we update our CSV file to look like this:
.data.csv
[format="csv", options="header"]
|===
include::content/2023/11/03/data-update.csv[]
|===
If we run that last query, we'll see the following output:
.Output
[source, text]
----
┌─splitByString('\t', assumeNotNull(value))─┐
│ ['foo','bar'] │
│ [''] │
│ ['mark','bar'] │
└───────────────────────────────────────────┘
----
So we might prefer to use `ifNull`, which lets us choose our own default value to use if a null value is encountered:
[source, sql]
----
FROM file('data.csv', CSVWithNames)
SELECT splitByString('\t', ifNull(value, 'N/A'));
----
.Output
[source, text]
----
┌─splitByString('\t', ifNull(value, 'N/A'))─┐
│ ['foo','bar'] │
│ ['N/A'] │
│ ['mark','bar'] │
└───────────────────────────────────────────┘
---- | In this post, we'll learn how to process a tab-separated value inside a CSV file with ClickHouse. | uploads/2023/11/clickhouse-null-banner.png | [
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] | clickhouse-nested-type-cannot-be-inside-nullable-type | https://markhneedham.com/blog/2023/11/03/clickhouse-nested-type-cannot-be-inside-nullable-type |
false | 2023-11-02 00:44:37 | Poetry: OSError: Precompiled binaries are not available for the current platform. Please reinstall from source | [
"python",
"poetry",
"til"
] | [
"TIL"
] | :icons: font
I've been playing around with the https://github.com/marella/ctransformers/tree/main[CTransformers^] library recently and getting it installed via Poetry was a bit fiddly.
In this post, we'll run through what I've ended up doing.
If we try to add the library in the usual way:
[source, bash]
----
poetry add ctransformers
----
We'll get the following error:
.Output
[source, text]
----
OSError: Precompiled binaries are not available for the current platform. Please reinstall from source using:
pip uninstall ctransformers --yes
CT_METAL=1 pip install ctransformers --no-binary ctransformers
----
Instead, we need to call the following command to tell Poetry to install the library from source:
[source, bash]
----
poetry config --local installer.no-binary ctransformers
----
We can then run the following command again:
[source, bash]
----
poetry add ctransformers
----
And this time it will install from source.
Unfortunately, I still haven't worked out how to have Poetry pickup the `CT_METAL` environment variable, which means it doesn't get installed with GPU support.
To get GPU support I'm having to go around Poetry's dependency management by running `pip` directly:
[source, bash]
----
CT_METAL=1 poetry run pip install ctransformers
----
Which works, but isn't ideal.
If anybody knows how to have Poetry pick up the environment variable, please let me know in the comments. | In this post, we'll learn how to install a library from source using Poetry. | uploads/2023/10/autocomplete-banner.png | [
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] | poetry-precompiled-binaries-not-available | https://markhneedham.com/blog/2023/11/02/poetry-precompiled-binaries-not-available |
false | 2023-11-07 00:44:37 | ClickHouse - DB::Exception:: there is no writeable access storage in user directories (ACCESS_STORAGE_FOR_INSERTION_NOT_FOUND) | [
"clickhouse",
"til"
] | [
"TIL"
] | :icons: font
I've been working with ClickHouse's access control/account management as part of a video that I created showing https://www.youtube.com/watch?v=Rhe-kUyrFUE[how to login to a ClickHouse server with an SSH key^], but getting it all setup locally was a bit fiddly.
In this blog post, we'll go through the mistakes I made and how to fix them.
I initially tried starting the ClickHouse server:
[source, bash]
----
./clickhouse server
----
Connecting to it with a client:
[source, bash]
----
./clickhouse client
----
And then tried to create a user:
[source, sql]
----
CREATE USER mark
IDENTIFIED WITH sha256_password
BY 'my_password';
----
This resulted in the following error:
.Output
[source, text]
----
Received exception from server (version 23.11.1):
Code: 514. DB::Exception: Received from localhost:9000. DB::Exception: Could not insert user `mark` because there is no writeable access storage in user directories. (ACCESS_STORAGE_FOR_INSERTION_NOT_FOUND)
----
It turns out I hadn't followed https://clickhouse.com/docs/en/operations/access-rights[these instructions from the documentation^]:
[blockquote]
____
Setup a directory for configurations storage.
ClickHouse stores access entity configurations in the folder set in the `access_control_path` server configuration parameter.
Enable SQL-driven access control and account management for at least one user account.
By default, SQL-driven access control and account management is disabled for all users. You need to configure at least one user in the users.xml configuration file and set the values of the `access_management`, `named_collection_control`, `show_named_collections`, and `show_named_collections_secrets` settings to 1.
____
I wasn't sure where I should be setting those properties, but https://www.linkedin.com/in/richraposa[Rich Raposa^] came to the rescue.
It turns out that we need to put those values in config files under the `config.d` directory:
.config.d/folders.xml
[source, xml]
----
include::content/2023/11/07/server/config.d/folders.xml[]
----
.config.d/users.xml
[source, xml]
----
include::content/2023/11/07/server/config.d/users.xml[]
----
If we restart the server and then go back to the client, we can try to create the user again:
[source, sql]
----
CREATE USER mark
IDENTIFIED WITH sha256_password
BY 'my_password';
----
.Output
[source, text]
----
Query id: bdccd4a3-91f9-47c1-846c-29229fe95abe
Ok.
----
And we can see the user if we call `SHOW USERS`:
[source, sql]
----
SHOW USERS;
----
.Output
[source, text]
----
Query id: 668a4379-fd56-4dec-ab96-2e914977a60a
┌─name────┐
│ default │
│ mark │
└─────────┘
----
If we then look under the `my_access_folder` directory, we'll see the following files:
.my_access_folder/users.list
[source, xml]
----
include::content/2023/11/07/server/my_access_folder/users.list[]
----
.my_access_folder/10471a13-2fb4-1bec-7a81-6afc7296df98.sql
[source, xml]
----
include::content/2023/11/07/server/my_access_folder/10471a13-2fb4-1bec-7a81-6afc7296df98.sql[]
----
If you run that yourself, you'll have a different UUID used for the user, but the structure should be similar. | In this post, we'll learn how to convert a date or datetime to epoch in milliseconds in ClickHouse | uploads/2023/11/clickhouse-writeable-access-storage.png | [
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] | clickhouse-no-writeable-access-storage | https://markhneedham.com/blog/2023/11/07/clickhouse-no-writeable-access-storage |
false | 2023-11-09 00:44:37 | ClickHouse - How to get the first 'n' values from an array | [
"clickhouse",
"til"
] | [
"TIL"
] | :icons: font
I was recently working with some very long arrays in ClickHouse and I wanted to select just a few values so that they didn't take up the entire screen.
The way I thought would 'just work' (TM) didn't, so this blog documents how to do it.
If you want to follow along, you'll need to https://clickhouse.com/docs/en/install[install ClickHouse^].
On a Mac, Brew is a pretty good option:
[source, bash]
----
brew install clickhouse
----
Once you've done that, launch ClickHouse Local:
[source, bash]
----
clickhouse local -m
----
.Output
[source, text]
----
ClickHouse local version 23.10.1.1976 (official build).
MN :)
----
And then we're going to return an array that contains 10 values:
[source, sql]
----
SELECT range(0,10);
----
.Output
[source, text]
----
┌─range(0, 10)──────────┐
│ [0,1,2,3,4,5,6,7,8,9] │
└───────────────────────┘
----
I wanted to get just the first 5 values, so I tried to use the Python-esque syntax:
[source, sql]
----
SELECT range(0,10)[:5];
----
But that wasn't too happy with me:
.Output
[source, text]
----
Expected one of: token, Comma, ClosingSquareBracket, CAST operator, NOT, INTERVAL, CASE, DATE, TIMESTAMP, tuple, collection of literals, array, number, literal, NULL, Bool, true, false, string literal, asterisk, qualified asterisk, compound identifier, list of elements, identifier, COLUMNS matcher, COLUMNS, qualified COLUMNS matcher, substitution, MySQL-style global variable
----
What we need is the https://clickhouse.com/docs/en/sql-reference/functions/array-functions#arrayslice[`arraySlice`^] function, which lets us extract a variable number of values from an array.
We have to specify an `offset` (remembering that array indexing in ClickHouse starts from `1`) and then a `length`, which is the number of values to return.
The following does the job:
[source, sql]
----
SELECT arraySlice(range(0,10), 1, 5);
----
.Output
[source, text]
----
┌─arraySlice(range(0, 10), 1, 5)─┐
│ [0,1,2,3,4] │
└────────────────────────────────┘
---- | In this post, we'll learn how to get the first few entries from an array in ClickHouse | uploads/2023/11/clickhouse-arrays-slice.png | [
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] | clickhouse-array-first-n-values | https://markhneedham.com/blog/2023/11/09/clickhouse-array-first-n-values |
false | 2023-11-08 00:44:37 | ClickHouse - AttributeError: 'NoneType' object has no attribute 'array' | [
"clickhouse",
"python",
"til"
] | [
"TIL"
] | I was querying a ClickHouse server from a Python script a couple of days ago and ran into an error message when trying to create a Pandas DataFrame.
In this blog, we'll see the error message and how to fix it.
I'm gonna assume that we have a ClickHouse Server running and we're going to connect to it like this:
[source, bash]
----
./clickhouse client
----
.Output
[source, text]
----
ClickHouse client version 23.10.1.1709 (official build).
Connecting to localhost:9000 as user default.
Connected to ClickHouse server version 23.11.1 revision 54466.
----
Next, let's create a table with one column and populate it with some values:
[source, sql]
----
CREATE TABLE x(value Int32)
ENGINE MergeTree()
ORDER BY tuple();
INSERT INTO x VALUES(1) (4) (7) (12) (24);
----
Now we're going to query this table from a Python script.
We'll first install https://pypi.org/project/clickhouse-connect/[`clickhouse-connect`^]:
[source, bash]
----
pip install clickhouse-connect
----
And then we'll try to get back a Pandas DataFrame containing our result:
[source, python]
----
import clickhouse_connect
client = clickhouse_connect.get_client(host='localhost')
client.query_df("FROM x SELECT *")
----
.Output
[source, text]
----
File ~/Library/Caches/pypoetry/virtualenvs/fastembed-clickhouse-4QwM5Uh0-py3.11/lib/python3.11/site-packages/clickhouse_connect/datatypes/string.py:40, in String._finalize_column(self, column, ctx)
38 def _finalize_column(self, column: Sequence, ctx: QueryContext) -> Sequence:
39 if ctx.use_extended_dtypes and self.read_format(ctx) == 'native':
---> 40 return pd.array(column, dtype=pd.StringDtype())
41 if ctx.use_numpy and ctx.max_str_len:
42 return np.array(column, dtype=f'<U{ctx.max_str_len}')
AttributeError: 'NoneType' object has no attribute 'array'
----
It looks like we need to install Pandas, which I guess kinda makes sense.
[source, bash]
----
pip install pandas
----
And then if we re-run our script, we'll see the following output:
.Output
[source, text]
----
value
0 1
1 4
2 7
3 12
4 24
----
Job done. | In this post, we'll learn how to fix an error that we might get when creating a Pandas DataFrame from a query result. | uploads/2023/11/clickhouse-pandas-missing.png | [
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] | clickhouse-client-array-nonetype-no-attribute | https://markhneedham.com/blog/2023/11/08/clickhouse-client-array-nonetype-no-attribute |
false | 2023-11-06 00:44:37 | ClickHouse: Convert date or datetime to epoch | [
"clickhouse",
"clickhouse-local",
"til"
] | [
"TIL"
] | :icons: font
I've been working with dates in ClickHouse today and I wanted to convert some values into epoch seconds/milliseconds to use with another tool.
We're going to document how to do that in this blog post, for future me if no one else.
Let's start an instance of ClickHouse Local:
[source, bash]
----
clickhouse local -m
----
And now we'll write a query that returns the current date/time:
[source, sql]
----
SELECT now() AS time;
----
.Output
[source, text]
----
┌────────────────time─┐
│ 2023-11-06 14:58:19 │
└─────────────────────┘
----
If we want to convert this value to epoch seconds, we can use the `toUnixTimestamp` function.
[source, sql]
----
SELECT toUnixTimestamp(now()) AS time
----
.Output
[source, text]
----
┌───────time─┐
│ 1699282944 │
└────────────┘
----
There's an equivalent function, `toUnixTimestamp64Milli`, that will give us epoch millis.
Let's try that:
[source, sql]
----
SELECT toUnixTimestamp64Milli(now()) AS time;
----
.Output
[source, text]
----
Received exception:
Code: 43. DB::Exception: The only argument for function toUnixTimestamp64Milli must be DateTime64: While processing toUnixTimestamp64Milli(now()) AS time. (ILLEGAL_TYPE_OF_ARGUMENT)
----
Hmmm, looks like we'll need to do some casting.
[source, sql]
----
SELECT toUnixTimestamp64Milli(cast(now(), 'DateTime64')) AS time;
----
Or we could use the https://clickhouse.com/docs/en/sql-reference/data-types/datetime64[`toDateTime64`^] function
[source, sql]
----
SELECT toUnixTimestamp64Milli(toDateTime64(now(), 3)) AS time; -- <.>
----
<.> `3` means a granularity of `milliseconds`
.Output
[source, text]
----
┌──────────time─┐
│ 1699283200000 │
└───────────────┘
----
If we genuinely do want the current time down to the millisecond, we'll need to use the `now64` function instead:
[source, sql]
----
SELECT toUnixTimestamp64Milli(now64()) AS time;
----
.Output
[source, text]
----
┌──────────time─┐
│ 1699283352267 │
└───────────────┘
----
How about if we only have a date, without a time?
We can get that in epoch seconds like this:
[source, sql]
----
select toUnixTimestamp(today()) AS date;
----
.Output
[source, text]
----
┌───────date─┐
│ 1699228800 │
└────────────┘
----
If we want to get epoch millis, we need to convert the value to a `DateTime64`:
[source, sql]
----
SELECT toUnixTimestamp64Milli(toDateTime64(today(), 3)) AS date;
----
.Output
[source, text]
----
┌──────────date─┐
│ 1699228800000 │
└───────────────┘
---- | In this post, we'll learn how to convert a date or datetime to epoch in milliseconds in ClickHouse | uploads/2023/11/clickhouse-date-epoch.png | [
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] | clickhouse-date-to-epoch | https://markhneedham.com/blog/2023/11/06/clickhouse-date-to-epoch |
false | 2023-11-15 00:44:37 | Summing columns in remote Parquet files using ClickHouse | [
"clickhouse",
"clickhouse-local",
"parquet",
"til"
] | [
"TIL"
] | :icons: font
I'm an avid reader of https://til.simonwillison.net[Simon Willison's TIL blog^] and enjoyed https://til.simonwillison.net/duckdb/remote-parquet[a recent post^] showing how to sum the size of all the Midjourney images stored on Discord.
He did this by querying a bunch of Parquet files stored on Hugging Face with DuckDB.
I was curious whether I could do the same thing using ClickHouse and in this blog post, we're going to find out.
The dataset that we're going to use is available at https://huggingface.co/datasets/vivym/midjourney-messages[vivym/midjourney-messages^].
It contains just over 55 million records spread over 56 Parquet files.
They're a little over 150 MB each, for a total of around 8 GB.
The files have the following structure:
[source, bash]
----
https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet
https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000001.parquet
...
https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000055.parquet
----
== Setting up ClickHouse Local
Let's install ClickHouse on our machine:
[source, bash]
----
curl https://clickhouse.com/ | sh
----
And then launch ClickHouse Local:
[source, bash]
----
./clickhouse local -m
----
== Querying one file
We'll start by getting a row of one of the files, formatting as JSON.
[source, bash]
----
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet')
SELECT *
LIMIT 1
Format JSONEachRow;
----
.Output
[source, text]
----
Received exception:
Code: 483. DB::Exception: Too many redirects while trying to access https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet. You can allow redirects by changing the setting 'max_http_get_redirects'. Example: `SET max_http_get_redirects = 10`. Redirects are restricted to prevent possible attack when a malicious server redirects to an internal resource, bypassing the authentication or firewall.: Cannot extract table structure from Parquet format file. You can specify the structure manually. (TOO_MANY_REDIRECTS)
----
Oops, it doesn't like that the URL redirects elsewhere, but we can have it handle that by configuring `max_http_get_redirects`.
Let's update our query:
[source, sql]
----
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet')
SELECT *
LIMIT 1
Format JSONEachRow
SETTINGS max_http_get_redirects=1;
----
.Output
[source, text]
----
{"id":"1144508197969854484","channel_id":"989268300473192561","content":"**adult Goku in Dragonball Z, walking on a beach, in a Akira Toriyama anime style** - Image #1 <@1016225582566101084>","timestamp":"2023-08-25T05:46:58.330000+00:00","image_id":"1144508197693046875","height":"1024","width":"1024","url":"https:\/\/cdn.discordapp.com\/attachments\/989268300473192561\/1144508197693046875\/anaxagore54_adult_Goku_in_Dragonball_Z_walking_on_a_beach_in_a__987e6fd5-64a1-43f6-83dd-c58d2eb42948.png","size":"1689284"}
1 row in set. Elapsed: 3.424 sec.
----
That took a few seconds and I expect that it pulled down the whole file before selecting the first row.
Let's configure `nettop`, as described in Simon's post, and then run the query again:
[source, bash]
----
nettop -p 70254 -J bytes_in
----
.Output
[source, bash]
----
bytes_in
clickhouse.70254 152 MiB
----
It downloaded 152MB, which is the size of the file.
== Summing the size of one file
Let's now see what happens if we sum the `size` column, as Simon did in his post:
[source, sql]
----
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/000000.parquet')
SELECT sum(size) as totalSize, formatReadableSize(totalSize)
SETTINGS max_http_get_redirects=1;
----
.Output
[source, text]
----
┌─────totalSize─┬─formatReadableSize(sum(size))─┐
│ 3456458790156 │ 3.14 TiB │
└───────────────┴───────────────────────────────┘
1 row in set. Elapsed: 1.097 sec.
----
Just over 3 TB for the first file.
And this is the `nettop` output:
.Output
[source, bash]
----
bytes_in
clickhouse.70254 5386 KiB
----
This time it was quicker and only downloaded about 5 MB because ClickHouse also uses HTTP range header tricks to only pull down the `size` column.
== Summing the size of all the files
Okay, now we're ready to sum up the `size` column in all the files.
ClickHouse has quite a neat 'range syntax' for doing this.
So to sum the size column in all 56 Parquet files, we can write the following query:
[source, sql]
----
SELECT
sum(size) AS totalSize,
formatReadableSize(totalSize)
FROM url('https://huggingface.co/datasets/vivym/midjourney-messages/resolve/main/data/0000{00..55}.parquet')
SETTINGS max_http_get_redirects = 1
----
.Output
[source, text]
----
┌───────totalSize─┬─formatReadableSize(sum(size))─┐
│ 162800469938172 │ 148.07 TiB │
└─────────────────┴───────────────────────────────┘
1 row in set. Elapsed: 9.798 sec. Processed 54.08 million rows, 8.50 GB (5.52 million rows/s., 867.48 MB/s.)
Peak memory usage: 896.00 B.
----
It's kinda insane that Midjourney has created 148 TB of image files!
And what does `nettop` have to say?
.Output
[source, bash]
----
bytes_in
clickhouse.70254 287 MiB
----
Again, pretty similar to what Simon saw - ClickHouse has also only downloaded 287 MB of data instead of the full 8 GB to work out the total size. | In this post, we'll learn how to sum columns in remote Parquet files using ClickHouse. | uploads/2023/11/clickhouse-summing-remote-parquet-files.png | [
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] | clickhouse-summing-columns-remote-files | https://markhneedham.com/blog/2023/11/15/clickhouse-summing-columns-remote-files |
false | 2023-10-03 00:44:37 | Running Mistral AI on my machine with Ollama | [
"ollama",
"mistralai",
"generative-ai",
"llama-index",
"til"
] | [
"TIL"
] | :icons: font
Last week Mistral AI announced the release of their first Large Language Model (LLM), trained with 7 billion parameters, and better than Meta's Llama 2 model with 13 billion parameters.
For those keeping track, https://techcrunch.com/2023/06/13/frances-mistral-ai-blows-in-with-a-113m-seed-round-at-a-260m-valuation-to-take-on-openai/?guccounter=1[Mistral AI was founded in the summer of 2023^] and raised $113m in their seed round.
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/NFgEgqua-fg?si=P67Le7XyVjBwrIHR" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
====
== Ollama: Running LLMs locally
In this blog post, we're going to try out Mistral AI's LLM on a local machine using a tool called https://ollama.ai/[Ollama^].
We'll choose the Mac option from https://ollama.ai/download[the download page^], move it to our `/Applications` directory and then run it.
There is no UI (at least at the moment), so we'll need to head to the terminal.
We can run the following command to see what options we have:
[source, bash]
----
ollama
----
.Output
[source, bash]
----
Large language model runner
Usage:
ollama [command]
Available Commands:
serve Start ollama
create Create a model from a Modelfile
show Show information for a model
run Run a model
pull Pull a model from a registry
push Push a model to a registry
list List models
cp Copy a model
rm Remove a model
help Help about any command
Flags:
-h, --help help for ollama
-v, --version version for ollama
Use "ollama [command] --help" for more information about a command.
----
If we run `ollama list`, we'll get a list of the models that we've installed locally, which will initially be empty.
== Installing Mistral AI
Let's rectify that by installing the Mistral AI model.
Let's navigate to the https://ollama.ai/library[list of models^] and then scroll down to find the https://ollama.ai/library/mistral[Mistral one^].
This page has information about the model, as well as installation instructions.
We can also click on `Tags` to see a bunch of variants of the model that we can use.
If we want to run the instruct version of the model, we can execute the following command:
[source, bash]
----
ollama run mistral:instruct
----
If the model doesn't exist locally, it will first be downloaded.
We can also do the download in a separate step
[source, bash]
----
ollama pull mistral:instruct
----
.Output
[source, text]
----
pulling manifest
pulling 6ae280299950... 100% |██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| (4.1/4.1 GB, 23 TB/s)
pulling fede2d8d6c1f... 100% |████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| (29/29 B, 280 kB/s)
pulling b96850d2e482... 100% |██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| (307/307 B, 834 kB/s)
verifying sha256 digest
writing manifest
removing any unused layers
success
----
My computer can't download at 23 TB/second, the reason it shows that I've already locally installed `mistral:latest`, which happens to be the same as `mistral:instruct`.
Let's have a look at the models that I've installed locally:
[source, bash]
----
ollama list
----
.Output
[source, text]
----
NAME ID SIZE MODIFIED
mistral:instruct 8aa307f73b26 4.1 GB 27 seconds ago
mistral:latest 8aa307f73b26 4.1 GB 4 days ago
----
== Running Mistral AI from the CLI
Now that we've downloaded the model, it's time to take it for a spin.
We're going to interact with the model in interactive mode, by calling the `run` sub-command without providing a prompt:
[source, bash]
----
ollama run mistral --verbose
----
.Output
[source, text]
----
>>> Send a message (/? for help)
----
Let's start with a question about Apache Pinot that we asked of the `lmsys/fastchat-t5-3b-v1.0` model, in https://www.markhneedham.com/blog/2023/06/23/hugging-face-run-llm-model-locally-laptop/[my previous post about running LLMs on your machine^].
[source, bash]
----
>>> What is Apache Pinot?
----
.Output
[source, text]
----
Apache Pinot is an open-source columnar storage database designed to handle large amounts of data in a fast and efficient manner. It is built on top of the Apache Hadoop Distributed File System (HDFS) and
is used to store and query massive volumes of structured and semi-structured data. Pinot provides high performance and low latency for real-time analytics, and can scale horizontally to handle large amounts
of data across many nodes in a cluster. It also supports advanced analytics capabilities such as filtering, aggregation, sorting, and grouping of data.
total duration: 2.497404s
load duration: 1.366417ms
prompt eval count: 15 token(s)
prompt eval duration: 245.655ms
prompt eval rate: 61.06 tokens/s
eval count: 122 token(s)
eval duration: 2.141823s
eval rate: 56.96 tokens/s
----
It's not built on top of HDFS, so that's not right, but the rest of the answer isn't terrible.
Let's tell it that and see what happens:
[source, bash]
----
>>> I don't think it's built on HDFS?
----
.Output
[source, text]
----
You are correct, Apache Pinot is not built on top of the Hadoop Distributed File System (HDFS). Instead, it has its own custom storage system called the "column store" which is optimized for storing and
querying columnar data. The column store is built using a combination of in-memory and disk-based storage to provide fast read and write performance. Pinot also supports various data ingestion methods such
as batch, real-time, and stream processing, making it suitable for use cases that require high throughput and low latency.
total duration: 2.740019708s
load duration: 2.253375ms
prompt eval count: 158 token(s)
prompt eval duration: 505.415ms
prompt eval rate: 312.61 tokens/s
eval count: 118 token(s)
eval duration: 2.129927s
eval rate: 55.40 tokens/s
----
It is a column store, but it's not called 'column store', but there we go!
The description of how it's built isn't quite right either., but let's move on!
An https://thenextweb.com/news/mistral-releases-first-generative-ai-model[article from thenextweb.com^] says the following:
[blockquote]
____
French AI developer Mistral says its Large Language Model is optimal for low latency, text summarisation, classification, text completion, and code completion. The startup has opted to release Mistral 7B under the Apache 2.0 licence, which has no restrictions on use or reproduction beyond attribution.
____
Let's see how well it gets on with some of these tasks using https://www.bbc.co.uk/sport/football/66989159[this BBC article^] about the use of VAR (Video Assitant Referee) in the Premier League.
I've copy/pasted the text into the `bbc.txt` file locally.
To programmatically pass in the contents of this file, we're going to exit interactive mode and pass in the prompt directly.
First up, summarising the article:
[source, bash]
----
ollama run mistral --verbose "Please can you summarise this article: $(cat bbc.txt)"
----
.Output
[source, text]
----
In summary, the article reports that Jamie Carragher feels the crisis around VAR has escalated following the disallowed goal scored by Luis Diaz against Tottenham Hotspur on Saturday. The error was
attributed to a significant human error and PGMOL, the governing body for referees, apologized for the mistake. However, Liverpool criticized the response of PGMOL and requested audio recordings of the
match officials' communication. Shay Given also commented that it was a big mess and questioned how such a big moment could be wrong in a high-profile game. Chris Sutton agreed with Carragher and suggested
that Liverpool fans should not see strong repercussions for the referee and the assistants who made the mistake.
total duration: 5.61016175s
load duration: 1.762583ms
prompt eval count: 910 token(s)
prompt eval duration: 2.349731s
prompt eval rate: 387.28 tokens/s
eval count: 153 token(s)
eval duration: 3.125673s
eval rate: 48.95 tokens/s
----
I'd say it's done a good job with that.
How about if we ask for bullet points instead?
[source, bash]
----
ollama run mistral --verbose "Can you pull out 5 bullet points from the following article: $(cat bbc.txt)"
----
.Output
[source, text]
----
1. The decision made by VAR during the game between Liverpool and Tottenham was incorrect, resulting in Luis Diaz's disallowed goal.
2. PGMOL, the governing body for referees, admitted that the decision was due to "a significant human error".
3. Jamie Carragher has stated that the feeling around VAR is at a crisis point after this incident.
4. Liverpool are planning to appeal against the red card given to Curtis Jones for fouling Yves Bissouma.
5. The controversy has led to criticism of PGMOL and the referees, with some calling for ex-players or people who understand the game more to be involved in VAR decisions.
total duration: 5.490499458s
load duration: 4.554ms
prompt eval count: 918 token(s)
prompt eval duration: 2.308006s
prompt eval rate: 397.75 tokens/s
eval count: 149 token(s)
eval duration: 3.050338s
eval rate: 48.85 tokens/s
----
Again, I like what it's come up with.
How about if we ask it to categorise the document and detect its sentiment?
[source, bash]
----
ollama run mistral --verbose "If you had to categorise this article, what tags would you use?: $(cat bbc.txt)"
----
.Output
[source, text]
----
Football, Sports News, VAR, Referees, Human Error, Controversy, Red Cards, Discipline, Video Assistants, Governing Bodies
total duration: 3.173798667s
load duration: 3.880292ms
prompt eval count: 925 token(s)
prompt eval duration: 2.358585s
prompt eval rate: 392.18 tokens/s
eval count: 38 token(s)
eval duration: 769.03ms
eval rate: 49.41 tokens/s
----
[source, bash]
----
ollama run mistral --verbose "What's the sentiment of this article: $(cat bbc.txt)"
----
.Output
[source, text]
----
The sentiment of this article is negative as it discusses criticism and controversy surrounding the video assistant referee (VAR) system. The incorrect decision made by VAR to flag Luis Diaz for offside
resulted in a controversial match between Liverpool and Tottenham, with Liverpool losing 2-1 after an own goal deep into stoppage time. The club is appealing against the red card given to Curtis Jones, and
there have been criticisms of PGMOL's response to the incident. Many people believe that this mistake was unprecedented and has undermined sporting integrity.
total duration: 4.857536625s
load duration: 2.101ms
prompt eval count: 915 token(s)
prompt eval duration: 2.344016s
prompt eval rate: 390.36 tokens/s
eval count: 118 token(s)
eval duration: 2.40703s
eval rate: 49.02 tokens/s
----
== Running Mistral AI from the HTTP API
Ollama also provides an HTTP API that we can use to interact with the models.
Let's give it a try:
[source, bash]
----
curl -X POST http://localhost:11434/api/generate -d '{
"model": "mistral",
"prompt": "What is the sentiment of this sentence: The situation surrounding the video assistant referee is at crisis point."
}'
----
.Output
[source, json]
----
{"model":"mistral","created_at":"2023-10-03T06:18:43.766825Z","response":"The","done":false}
{"model":"mistral","created_at":"2023-10-03T06:18:43.785518Z","response":" sentiment","done":false}
{"model":"mistral","created_at":"2023-10-03T06:18:43.804972Z","response":" of","done":false}
{"model":"mistral","created_at":"2023-10-03T06:18:43.825034Z","response":" this","done":false}
{"model":"mistral","created_at":"2023-10-03T06:18:43.84423Z","response":" sentence","done":false}
{"model":"mistral","created_at":"2023-10-03T06:18:43.862961Z","response":" is","done":false}
{"model":"mistral","created_at":"2023-10-03T06:18:43.881794Z","response":" negative","done":false}
{"model":"mistral","created_at":"2023-10-03T06:18:43.900369Z","response":".","done":false}
{"model":"mistral","created_at":"2023-10-03T06:18:43.91954Z","done":true,"context":[733,16289,28793,1824,349,272,21790,302,456,12271,28747,415,4620,12028,272,3798,13892,1792,14447,349,438,10788,1305,28723,733,28748,16289,28793,13,1014,21790,302,456,12271,349,7087,28723],"total_duration":397082750,"load_duration":1504000,"prompt_eval_count":26,"prompt_eval_duration":240025000,"eval_count":8,"eval_duration":141869000}
----
It works well, but the output is pretty tricky to work with, we'd have to do some work to massage that data.
Luckily there are a bunch of LLM libraries that have done hard work for us!
== Running Mistral AI with Llama Index
Llama Index is a data framework for LLM applications to ingest, structure, and access private or domain-specific data.
We can use it to call the Mistral model as part of a Python application:
We'll first install Llama Index:
[source, bash]
----
pip install llama-index
----
Now let's initialise the model:
[source, python]
----
from llama_index.llms import Ollama
llm = Ollama(model="mistral:instruct")
----
And call it:
[source, python]
----
llm.complete("What is the sentiment of this sentence: The situation surrounding the video assistant referee is at crisis point.")
----
.Output
[source, text]
----
CompletionResponse(text='The sentiment in the given sentence is negative, as it mentions that the situation is at a "crisis point" which suggests a state of extreme difficulty and urgency.', additional_kwargs={}, raw=None, delta=None)
---- | In this post, we'll learn how to run Mistral AI's Large Language Model (LLM) on our own machine using Ollama. | uploads/2023/10/mistral-banner.png | [
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] | mistral-ai-own-machine-ollama | https://markhneedham.com/blog/2023/10/03/mistral-ai-own-machine-ollama |
false | 2023-10-04 00:44:37 | Poetry: Updating a package to a new version | [
"poetry",
"python",
"til"
] | [
"TIL"
] | :icons: font
I'm using the Poetry package manager for all my Python projects these days and wanted to upgrade as library that I installed a few weeks ago.
I got myself all tangled up and wanted to write down how to do it for future me.
Let's create a simple project to demonstrate what to do:
[source, bash]
----
poetry init
----
.pyproject.toml
[source, toml]
----
[tool.poetry]
name = "update-blog"
version = "0.1.0"
description = ""
authors = ["Mark Needham <m.h.needham@gmail.com>"]
readme = "README.md"
packages = [{include = "update_blog"}]
[tool.poetry.dependencies]
python = "^3.11"
duckdb = "0.8.1"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
----
Next, we're going to add DuckDB version 0.8.1 to our project:
[source, bash]
----
poetry add duckdb@0.8.1
----
I want to update this to version 0.9.0, so I initially did this:
[source, bash]
----
poetry update duckdb
----
.Output
[source, text]
----
Updating dependencies
Resolving dependencies... (0.4s)
No dependencies to install or update
----
Hmmm, that doesn't do anything.
I came across https://nick.groenen.me/notes/upgrade-python-packages-to-their-latest-version-when-using-poetry/[Nick Groene's] blog post and he said you need to append `@latest` to force an update.
Let's try taht:
[source, bash]
----
poetry add duckdb@latest
----
.Output
[source, text]
----
Using version ^0.9.0 for duckdb
Updating dependencies
Resolving dependencies... (0.1s)
Package operations: 0 installs, 1 update, 0 removals
• Updating duckdb (0.8.1 -> 0.9.0)
Writing lock file
----
Happy days!
I still don't know how to update all the dependencies for a project to the latest version so if you know how to do that, please let me know! | In this post, we'll learn how to update a package when using the Poetry Python package manager. | uploads/2023/10/poetry-update-package-banner.png | [
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] | poetry-package-update | https://markhneedham.com/blog/2023/10/04/poetry-package-update |
false | 2023-10-05 00:44:37 | Poetry: The current project's Python requirement is not compatible | [
"poetry",
"python",
"til"
] | [
"TIL"
] | :icons: font
A few times this week I've run into an interesting problem with Python version requirements when trying to install various packages.
In this blog post, we'll learn what's going on and how to fix it.
Our story begins with the innocent creation of a Poetry project:
[source, bash]
----
poetry init
----
Next, we're going to add `dlt`, the data loading tool:
[source, bash]
----
poetry add dlt
----
.Output
[source, text]
----
Creating virtualenv incompatible-blog-Bp2VMsrx-py3.11 in /Users/markhneedham/Library/Caches/pypoetry/virtualenvs
Using version ^0.3.18 for dlt
Updating dependencies
Resolving dependencies... (2.0s)
Package operations: 38 installs, 1 update, 0 removals
• Installing six (1.16.0)
• Installing smmap (5.0.1)
• Installing certifi (2023.7.22): Installing...
• Installing certifi (2023.7.22)
• Installing charset-normalizer (3.3.0)
• Installing gitdb (4.0.10)
• Installing idna (3.4)
• Installing ply (3.11)
• Installing python-dateutil (2.8.2)
• Installing pytzdata (2020.1)
• Installing types-setuptools (68.2.0.0)
• Installing typing-extensions (4.8.0)
• Installing urllib3 (2.0.6)
• Installing wrapt (1.15.0)
• Installing astunparse (1.6.3)
• Installing click (8.1.7)
• Installing deprecated (1.2.14)
• Installing fsspec (2023.9.2)
• Installing gitpython (3.1.37)
• Installing giturlparse (0.12.0): Installing...
• Installing hexbytes (0.3.1): Installing...
• Installing giturlparse (0.12.0)
• Installing hexbytes (0.3.1)
• Installing humanize (4.8.0)
• Installing jsonpath-ng (1.6.0)
• Installing makefun (1.15.1)
• Installing orjson (3.9.7)
• Installing packaging (23.2)
• Installing pathvalidate (3.2.0): Pending...
• Installing pendulum (2.1.2): Installing...
• Installing pathvalidate (3.2.0)
• Installing pendulum (2.1.2)
• Installing pytz (2023.3.post1)
• Installing pyyaml (6.0.1)
• Installing requests (2.31.0)
• Installing requirements-parser (0.5.0)
• Installing semver (3.0.1)
• Updating setuptools (68.2.0 -> 68.2.2)
• Installing simplejson (3.19.1)
• Installing sqlalchemy (2.0.21)
• Installing tenacity (8.2.3)
• Installing tomlkit (0.12.1)
• Installing tzdata (2023.3)
• Installing dlt (0.3.18)
Writing lock file
----
We want to ingest data into DuckDB, so let's add that dependency as well:
[source, bash]
----
poetry add duckdb
----
.Output
[source, text]
----
Using version ^0.9.0 for duckdb
Updating dependencies
Resolving dependencies... (0.1s)
Package operations: 1 install, 0 updates, 0 removals
• Installing duckdb (0.9.0)
Writing lock file
----
So far so good.
But what about if we want to add pandas as well?
[source, bash]
----
poetry add pandas
----
.Output
[source, text]
----
Using version ^2.1.1 for pandas
Updating dependencies
Resolving dependencies... (0.1s)
The current project's Python requirement (>=3.11,<4.0) is not compatible with some of the required packages Python requirement:
- numpy requires Python <3.13,>=3.9, so it will not be satisfied for Python >=3.13,<4.0
Because no versions of pandas match >2.1.1,<3.0.0
and pandas (2.1.1) depends on numpy (>=1.26.0), pandas (>=2.1.1,<3.0.0) requires numpy (>=1.26.0).
Because numpy (1.26.0) requires Python <3.13,>=3.9
and no versions of numpy match >1.26.0, numpy is forbidden.
Thus, pandas is forbidden.
So, because incompatible-blog depends on pandas (^2.1.1), version solving failed.
• Check your dependencies Python requirement: The Python requirement can be specified via the `python` or `markers` properties
For numpy, a possible solution would be to set the `python` property to ">=3.11,<3.13"
https://python-poetry.org/docs/dependency-specification/#python-restricted-dependencies,
https://python-poetry.org/docs/dependency-specification/#using-environment-markers
----
Disaster strikes!
It's complaining about Poetry's Python requirement being incompatible with numpy's requirement.
Our project allows any version from 3.11 -> 4.0 and numpy only allows 3.9 -> 3.13.
Right now we're using 3.11, so we're currently fine, but it could break in the future.
If we have a look at our `pyproject.toml` file, we can see the Python version is configured like this:
[source, toml]
----
[tool.poetry.dependencies]
python = ">=3.11"
----
At the moment it will allow any version that's 3.11 or above.
We need to restrict the version to a maximum of 3.13, which we can do by updating it to read like this:
[source, toml]
----
[tool.poetry.dependencies]
python = ">=3.11.0,<3.13.0"
----
And now if we run `poetry add pandas` again:
.Output
[source, text]
----
Using version ^2.1.1 for pandas
Updating dependencies
Resolving dependencies... (0.2s)
Package operations: 2 installs, 0 updates, 0 removals
• Installing numpy (1.26.0)
• Installing pandas (2.1.1)
Writing lock file
----
Happy days! | In this post, we'll learn how to work around an issue when packages have different Python version requirements. | uploads/2023/10/poetry-not-compatible-banner.png | [
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] | poetry-project-python-not-compatible | https://markhneedham.com/blog/2023/10/05/poetry-project-python-not-compatible |
false | 2023-10-02 00:44:37 | DuckDB: Show a list of views | [
"duckdb",
"til"
] | [
"TIL"
] | :icons: font
I recently wanted to get a list of the views that I'd created in a DuckDB database and it took me a while to figure out how to do it.
So this blog post is for future Mark more than anyone else!
We're going to start with the following CSV file:
.data/sales.csv
[source, csv]
----
date,product_id,quantity,sales_amount
2021-01-01,101,5,50
2021-01-02,102,3,30
2021-02-01,101,4,40
2021-02-02,103,6,60
----
And now we'll create a table from the https://duckdb.org/docs/api/cli.html[DuckDB CLI^]:
[source, sql]
----
CREATE TABLE sales AS
SELECT * from 'data/sales.csv';
----
The next task is to create a view called `monthly_sales` that aggregates the sales by month:
[source, sql]
----
CREATE VIEW monthly_sales AS
SELECT
DATE_TRUNC('MONTH', date) AS month,
product_id,
SUM(quantity) AS total_quantity,
SUM(sales_amount) AS total_sales_amount
FROM sales
GROUP BY ALL
ORDER BY ALL
----
Now that I've created the view, I wanted to figure out how to list the views in my database.
It took me a while to figure out how to do this from the docs, but I eventually found the solution on the https://duckdb.org/docs/sql/duckdb_table_functions[DuckDB_% Metadata Functions^] page.
To get a list of all the views, we can run this query:
[source, sql]
----
FROM duckdb_views();
----
But that returns a lot of rows as it includes all the internal views as well.
To find just the ones that we've defined, we need to add the following `WHERE` clause:
[source, sql]
----
FROM duckdb_views()
SELECT * EXCLUDE(sql)
WHERE NOT internal;
----
.Output
[options="header"]
|===
| database_name | database_oid | schema_name | schema_oid | view_name | view_oid | internal | temporary | column_count
| sales | 992 | main | 994 | monthly_sales | 1006 | false | false | 4
|===
Or, if we want to write even less code, we can query the `duckdb_views` view rather than the function.
The view automatically strips out the internal views:
[source, sql]
----
FROM duckdb_views
SELECT * EXCLUDE(sql);
----
.Output
[options="header"]
|===
| database_name | database_oid | schema_name | schema_oid | view_name | view_oid | internal | temporary | column_count
| sales | 992 | main | 994 | monthly_sales | 1006 | false | false | 4
|===
Job done! | In this post, we'll learn how to get a list of views in DuckDB. | uploads/2023/10/duckdb-views-banner.png | [
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] | duckdb-list-show-views | https://markhneedham.com/blog/2023/10/02/duckdb-list-show-views |
false | 2023-10-18 00:44:37 | Ollama: Running GGUF Models from Hugging Face | [
"ollama",
"hugging-face",
"gguf",
"til"
] | [
"TIL"
] | :icons: font
https://medium.com/@phillipgimmi/what-is-gguf-and-ggml-e364834d241c[GGUF (GPT-Generated Unified Format)^] has emerged as the de facto standard file format for storing large language models for inference.
We are starting to see a lot of models in this format on Hugging Face, many of them uploaded by https://huggingface.co/TheBloke[The Bloke^].
One cool thing about GGUF models is that it's super easy to get them running on your own machine using https://ollama.ai/[Ollama^].
In this blog post, we're going to look at how to download a GGUF model from Hugging Face and run it locally.
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/7BH4C6-HP14?si=jZMYLcOot5ItDyNZ" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
====
There are over 1,000 models on Hugging Face that match the search term `GGUF`, but we're going to download the https://huggingface.co/TheBloke/MistralLite-7B-GGUF[TheBloke/MistralLite-7B-GGUF^] model.
We'll do this using the Hugging Face Hub CLI, which we can install like this:
[source, bash]
----
pip install huggingface-hub
----
We can then download one of the MistalLite models by running the following:
[source, bash]
----
huggingface-cli download \
TheBloke/MistralLite-7B-GGUF \
mistrallite.Q4_K_M.gguf \
--local-dir downloads \
--local-dir-use-symlinks False
----
Make sure you specify the name of the `gguf` file that you want to download, otherwise, it will download all of them!
You can find a list of the model files to choose from on the https://huggingface.co/TheBloke/MistralLite-7B-GGUF/tree/main[files and versions^] page.
This file is over 4GB in size, so connect your ethernet cable if you're going to try this at home.
Once it's done, you'll have the file in your `downloads` directory.
We're then going to create a `Modelfile` with the following contents:
.Modelfile
[source, dockerfile]
----
FROM ./downloads/mistrallite.Q4_K_M.gguf
----
We then build an Ollama model using the following command:
[source, dockerfile]
----
ollama create mistrallite -f Modelfile
----
And now let's see if we can get the model to tell us all about the Grafana visualisation tool:
[source, dockerfile]
----
ollama run mistrallite "What is Grafana?"
----
The result you get will be different to what I get as these models are stochastic, but this is the output I got on one of the tries:
.Output
[source, text]
----
Grafana is an open source tool that allows the user to query, visualize and alert
on time series data. It can be used as a drop in replacement for other monitoring
tools such as Cacti, Munin or RRDtool.
Grafana does not store any of the time series data itself but will connect to an
external data source and display information in graphs and charts. Grafana’s
flexible query editor allows users to extract the information they need from their
time series data using the most popular query languages such as PromQL, InfluxDB
Query Language (IL) and Graphite. It is even possible to create your own custom
queries.
Grafana also supports a number of plugins which can extend its functionality or
connect it to other systems such as Slack, HipChat, PagerDuty and others.
Why should I use Grafana?
One major benefit of using Grafana is that it provides the user with the ability to
visualize their time series data in a variety of ways including bar graphs, line
charts, pie charts and more. This makes it easier for the user to identify
patterns and trends in the data which can help them make better decisions.
Another advantage of using Grafana is that it provides users with a centralized
place where they can view all of their time series data from multiple sources.
This makes it easy for users to compare data from different systems or even between
different time periods.
In addition, Grafana offers some unique features such as the ability to set alerts
based on specific conditions being met in the data. This allows users to be
proactive in identifying potential issues before they become a problem.
----
I don't think this is the greatest model I've ever used, but it is cool that we now have a choice of over 1,000 models on Hugging Face to run on our own machines. | In this post, we'll learn how to run a GGUF model from Hugging Face on our machine using Ollama. | uploads/2023/10/ollama-hugging-face-gguf.png | [
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] | ollama-hugging-face-gguf-models | https://markhneedham.com/blog/2023/10/18/ollama-hugging-face-gguf-models |
false | 2023-10-11 00:44:37 | Ollama: Experiments with few-shot prompting on Llama2 7B | [
"ollama",
"llama2",
"til"
] | [
"TIL"
] | :icons: font
A problem that I'm currently trying to solve is how to work out whether a given sentence is a question.
If there's a question mark on the end we can assume it is a question, but what about if the question mark's been left off?
https://www.promptingguide.ai/techniques/fewshot[Few-shot prompting^] is a technique where we provide some examples in our prompt to try to guide the LLM to do what we want.
And, this seemed like a good opportunity to try it out on Meta's Llama2 7B Large Language Model using https://ollama.ai/[Ollama^].
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/ocfzGBnhhDE?si=IpqGQ3C_hvvCDVWv" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
====
But before we do that, we're going to create a baseline where we don't provide any examples.
I've created a file, `sentences.csv`, which contains sentences in one column and whether I think they're a question in the other.
[.scrollable]
.sentences.csv
[format="csv", options="header"]
|===
include::content/2023/10/11/data/sentences.csv[]
|===
== A sentence-focused model with no examples
Next, we're going to create a model specifically for determining if something is a question.
We're going to create a model file, `Modelfile-question-llama2-base`, which should look like this:
.Modelfile-question-llama2-base
[source, docker]
----
FROM llama2
TEMPLATE """
<s>
[INST]
{{- if .First }}
<<SYS>>
{{.System}}
<</SYS>>
{{- end }}
{{.Prompt}}
[/INST]
"""
SYSTEM """You are a question analyzer. You will receive text and output true or false depending on if you think the text is a question. Do NOT try to answer the question."""
----
The exact format used in the `TEMPLATE` section will vary depending on the model that you're using, but this is the one for Llama2.
We can then run the following command:
[source, bash]
----
ollama create \
question-llama2-base \
-f Modelfile-question-llama2-base
----
.Output
[source, text]
----
parsing modelfile
looking for model
creating model template layer
creating model system layer
creating config layer
using already created layer sha256:8daa9615cce30c259a9555b1cc250d461d1bc69980a274b44d7eda0be78076d8
using already created layer sha256:8c17c2ebb0ea011be9981cc3922db8ca8fa61e828c5d3f44cb6ae342bf80460b
using already created layer sha256:7c23fb36d80141c4ab8cdbb61ee4790102ebd2bf7aeff414453177d4f2110e5d
using already created layer sha256:e35ab70a78c78ebbbc4d2e2eaec8259938a6a60c34ebd9fd2e0c8b20f2cdcfc5
writing layer sha256:259d89c66e53cd019f48e7f41127a0f9d7cc522b58c35cfd1411395e5e81cf8f
writing layer sha256:04f603753dacd8b5f855cdde37290d26ce45b283114fb40c00646c3f063333f4
writing layer sha256:846424fbd2c37f30a9ccc3a33efe20b24ff44988293b92d35bb80c1d4fcb4b09
writing manifest
removing any unused layers
success
----
We can then call the model passing in say one of the sentences:
[source, bash]
----
ollama run \
question-llama2-base \
"The weather is quite pleasant today."
----
.Output
[source, text]
----
True
----
It's kinda working, but we can already see that it's not returning the result in the format that I suggested.
Let's have a look at how it gets on with all the sentences using the Llama Index library.
We're also going to install a utility library called `mpu` that we'll use to coerce the boolean string in the CSV file to a boolean value, as well as `scikit-learn` to see how well the model does.
[source, bash]
----
pip install llama_index mpu scikit-learn
----
We'll first load the sentences:
[source, python]
----
import csv
from mpu.string import str2bool
with open('sentences.csv', 'r') as sentences_file:
reader = csv.reader(sentences_file, delimiter=",")
next(reader)
sentences = [(row[0], str2bool(row[1])) for row in reader]
sentences
----
We're also going to create a couple of helper functions to render the results:
[source, python]
----
def handle_result(is_sentence, llm_answer):
result = f"Actual: {str(is_sentence).ljust(5)} LLM: {llm_answer}"
print(f"{sentence.ljust(60)} {result}")
def handle_error(is_sentence, ex, llm_says):
result = f"Actual: {str(is_sentence).ljust(5)} LLM: {ex} - {llm_says}"
print(f"{sentence.ljust(60)} {result}")
----
Now, let's initialise the Llama2 model:
[source, python]
----
from llama_index.llms import Ollama
base_llama2 = Ollama(model="question-llama2-base")
----
And iterate over each of the sentences, asking the LLM whether it's a sentence, and then checking the answer.
We're using the JSON module to parse the result since `true` and `false` are valid JSON values.
[source, python]
----
import json
llm_answers=[]
for sentence, is_sentence in sentences:
llm_says = base_llama2.complete(sentence).text
try:
llm_answer = json.loads(llm_says)
handle_result(is_sentence, llm_answer)
llm_answers.append(llm_answer)
except json.JSONDecodeError as ex:
handle_error(is_sentence, ex, llm_says)
llm_answers.append(None)
----
If we run that code, we see the following output:
.Output
[source, text]
----
The weather is quite pleasant today. Actual: False LLM: Expecting value: line 1 column 1 (char 0) - True
Have you ever been to Paris Actual: True LLM: Expecting value: line 1 column 1 (char 0) - True
I enjoy reading books on rainy days. Actual: False LLM: Expecting value: line 1 column 1 (char 0) - True
What is your favorite type of cuisine Actual: True LLM: Expecting value: line 1 column 1 (char 0) - True
The movie last night was exhilarating. Actual: False LLM: Expecting value: line 1 column 1 (char 0) - True
She has a collection of vintage postcards. Actual: False LLM: Expecting value: line 1 column 1 (char 0) - True
Can you believe it’s already October Actual: True LLM: Expecting value: line 1 column 1 (char 0) - True
I think the library closes at 6 pm. Actual: False LLM: Expecting value: line 1 column 1 (char 0) - True
Where do you usually go for vacation Actual: True LLM: Expecting value: line 1 column 1 (char 0) - True
It’s amazing how time flies. Actual: False LLM: Expecting value: line 1 column 1 (char 0) - True
The concert last night was incredible. Actual: False LLM: Expecting value: line 1 column 1 (char 0) - True
Have you finished the report yet Actual: True LLM: Expecting value: line 1 column 1 (char 0) - True
He likes to play soccer on weekends. Actual: False LLM: Expecting value: line 1 column 1 (char 0) - True
What time does the meeting start Actual: True LLM: Expecting value: line 1 column 1 (char 0) - True
I can't believe how fast the kids are growing up. Actual: False LLM: Expecting value: line 1 column 1 (char 0) - True
Is the grocery store open on Sundays Actual: True LLM: Expecting value: line 1 column 1 (char 0) - True
She has a knack for solving complex puzzles. Actual: False LLM: Expecting value: line 1 column 1 (char 0) - True
Guess who just got promoted Actual: True LLM: Expecting value: line 1 column 1 (char 0) - True
The painting in the hallway is quite striking. Actual: False LLM: Expecting value: line 1 column 1 (char 0) - True
Could be that he forgot our appointment. Actual: False LLM: Expecting value: line 1 column 1 (char 0) - True
----
So it hasn't returned a value in the correct format for any of the 20 questions and it's returning `True` every single time, which isn't very useful.
We can see how many we computed correctly by running the following code:
[source, python]
----
len([
predicted
for predicted, actual in zip(llm_answers, [s[1] for s in sentences])
if predicted == actual
])
----
.Output
[source, text]
----
0
----
Unsurprisingly we have none right since the LLM didn't return a value in the correct format.
== A sentence-focused model with examples
Let's see if we fare any better with a model that's been given a few examples of what we want it to do.
The Modelfile for this approach is shown below:
.Modelfile-question-llama2
[source, docker]
----
FROM llama2
TEMPLATE """
<s>
[INST]
{{- if .First }}
<<SYS>>
{{.System}}
<</SYS>>
{{- end }}
My name is Mark
[/INST]
false
</s>
<s>
[INST]
Can you do it with OpenAI?
[/INST]
true
</s>
<s>
[INST]
Will you make another tutorial
[/INST]
true
</s>
<s>
[INST]
{{.Prompt}}
[/INST]
"""
SYSTEM """You are a question analyzer. You will receive text and output true or false depending on if you think the text is a question. Do NOT try to answer the question."""
----
Let's build that model:
[source, bash]
----
ollama create \
question-llama2 \
-f Modelfile-question-llama2
----
Now let's go back to our Python environment and give this one a try.
First up, initialising the model:
[source, python]
----
llama2 = Ollama(model="question-llama2")
----
And now let's loop over the sentences like we did before:
[source, python]
----
llm_answers=[]
for sentence, is_sentence in sentences:
llm_says = llama2.complete(sentence).text
try:
llm_answer = json.loads(llm_says)
handle_result(is_sentence, llm_answer)
llm_answers.append(llm_answer)
except json.JSONDecodeError as ex:
handle_error(is_sentence, ex, llm_says)
llm_answers.append(None)
----
.Output
[source, text]
----
The weather is quite pleasant today. Actual: False LLM: False
Have you ever been to Paris Actual: True LLM: False
I enjoy reading books on rainy days. Actual: False LLM: True
What is your favorite type of cuisine Actual: True LLM: False
The movie last night was exhilarating. Actual: False LLM: False
She has a collection of vintage postcards. Actual: False LLM: False
Can you believe it’s already October Actual: True LLM: True
I think the library closes at 6 pm. Actual: False LLM: False
Where do you usually go for vacation Actual: True LLM: True
It’s amazing how time flies. Actual: False LLM: True
The concert last night was incredible. Actual: False LLM: Extra data: line 2 column 1 (char 6) - false
</s>
Have you finished the report yet Actual: True LLM: True
He likes to play soccer on weekends. Actual: False LLM: False
What time does the meeting start Actual: True LLM: True
I can't believe how fast the kids are growing up. Actual: False LLM: True
Is the grocery store open on Sundays Actual: True LLM: True
She has a knack for solving complex puzzles. Actual: False LLM: True
Guess who just got promoted Actual: True LLM: True
The painting in the hallway is quite striking. Actual: False LLM: False
Could be that he forgot our appointment. Actual: False LLM: True
----
That looks better already, although it looks like we're sometimes getting an `</s>` token returned as well.
Let's update the code to strip those out:
[source, python]
----
llm_answers=[]
for sentence, is_sentence in sentences:
llm_says = llama2.complete(sentence).text
try:
llm_answer = json.loads(llm_says.replace("</s>", ""))
handle_result(is_sentence, llm_answer)
llm_answers.append(llm_answer)
except json.JSONDecodeError as ex:
handle_error(is_sentence, ex, llm_says)
llm_answers.append(None)
----
If we run it again, we see the following output:
.Output
[source, text]
----
The weather is quite pleasant today. Actual: False LLM: False
Have you ever been to Paris Actual: True LLM: False
I enjoy reading books on rainy days. Actual: False LLM: True
What is your favorite type of cuisine Actual: True LLM: False
The movie last night was exhilarating. Actual: False LLM: False
She has a collection of vintage postcards. Actual: False LLM: False
Can you believe it’s already October Actual: True LLM: True
I think the library closes at 6 pm. Actual: False LLM: False
Where do you usually go for vacation Actual: True LLM: True
It’s amazing how time flies. Actual: False LLM: True
The concert last night was incredible. Actual: False LLM: False
Have you finished the report yet Actual: True LLM: True
He likes to play soccer on weekends. Actual: False LLM: False
What time does the meeting start Actual: True LLM: True
I can't believe how fast the kids are growing up. Actual: False LLM: True
Is the grocery store open on Sundays Actual: True LLM: True
She has a knack for solving complex puzzles. Actual: False LLM: True
Guess who just got promoted Actual: True LLM: True
The painting in the hallway is quite striking. Actual: False LLM: False
Could be that he forgot our appointment. Actual: False LLM: True
----
Keep in mind that LLMs aren't deterministic, so we may well see different results between runs.
Now that we've got a proper result for each sentence, we can compute some metrics.
Import the following functions from scikit-learn:
[source, python]
----
from sklearn.metrics import confusion_matrix, precision_score, recall_score
----
First, we're going to compute the precision score, which measures the `True Positive / (True Positive + False Positive)`.
This metric describes the ability of the LLM not to label as a question a sentence that is not a question.
We can compute this metric with the following code:
[source, python]
----
precision_score([s[1] for s in sentences], llm_answers, zero_division=0.0)
----
.Output
[source, text]
----
0.5454545454545454
----
We can also compute the recall score, which measures the `True Positive / (True Positive + False Negative)`.
This metric describes the ability of the LLM to find all the questions in the dataset.
We can compute this metric with the following code:
[source, python]
----
recall_score([s[1] for s in sentences], llm_answers)
----
.Output
[source, text]
----
0.75
----
We can also compute a confusion matrix, which shows the true/false positives/negatives on a 2x2 grid.
[source, python]
----
matrix = confusion_matrix([s[1] for s in sentences], llm_answers)
matrix
----
.Output
[source, text]
----
array([[7, 5],
[2, 6]])
----
This one is better visualised, so let's create a diagram with the following function, which uses the seaborn library:
[source, python]
----
import seaborn as sns
def render_confusion_matrix(matrix):
ax = sns.heatmap(matrix, annot=False, cmap=['white'], cbar=False,
linecolor='black', linewidths=1,
xticklabels=['Negative', 'Positive'],
yticklabels=['Negative', 'Positive']
)
labels = ['TN', 'FP', 'FN', 'TP']
for i in range(2):
for j in range(2):
ax.text(j+0.5, i+0.4, f"{str(matrix[i, j])}",
fontsize=16,
horizontalalignment='center',
verticalalignment='center')
ax.text(j+0.5, i+0.55, f"{labels[i * 2 + j]}",
color='green' if labels[i * 2 + j][0] == "T" else 'red',
fontsize=14,
horizontalalignment='center',
verticalalignment='center')
ax.xaxis.tick_top()
ax.xaxis.set_ticks_position('top')
ax.set_ylabel('Data (Truth)')
ax.set_xlabel('LLM prediction')
----
If we render the matrix:
[source, python]
----
render_confusion_matrix(matrix)
----
.Confusion matrix of Llama2 predicting questions
image::{{<siteurl>}}/uploads/2023/10/confusion-matrix.png[]
== Summary
Overall, we can see that our LLM isn't doing a great job here!
It's better at working out if something isn't a question than if it is a question, but even there it still gets some wrong.
My next experiment will be to see if I can get better results with one of the Llama2 models that has more parameters. | In this post, we'll learn how to do few-shot prompting with the Llama2 7B model on our own machine using Ollama. | uploads/2023/10/ollama-few-prompt-learning-banner.png | [
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] | ollama-few-shot-prompting-experiments-llama2-7b | https://markhneedham.com/blog/2023/10/11/ollama-few-shot-prompting-experiments-llama2-7b |
false | 2023-10-29 00:44:37 | iPython: How to disable autocomplete | [
"python",
"ipython",
"poetry",
"til"
] | [
"TIL"
] | :icons: font
I've been toying with the idea of using iPython as the Python REPL for videos on https://www.youtube.com/channel/UCKEk670ECmteGBehmDjVSSg[@LearnDataWithMark^], but I wanted to disable the autocomplete functionality as I find it too distracting.
In this blog post, I'll show how to do it.
First, let's install iPython:
[source, bash]
----
poetry add ipython
----
And now we'll launch the iPython REPL:
[source, bash]
----
poetry run ipython
----
.Output
[source, text]
----
Python 3.11.4 (main, Jun 20 2023, 17:23:00) [Clang 14.0.3 (clang-1403.0.22.14.1)]
Type 'copyright', 'credits' or 'license' for more information
IPython 8.16.1 -- An enhanced Interactive Python. Type '?' for help.
----
Now, let's say I type `x = ` to populate a variable.
It will suggest something like the following:
.Not so helpful autocomplete
image::{{<siteurl>}}/uploads/2023/10/ipython-autocomplete.png[]
I've only just started the session so this isn't particularly useful.
I'm not sure where it's getting the suggestions from - perhaps from my previous normal Python REPL?
In any case, I want to turn them off, which we can do from the https://ipython.org/ipython-doc/3/config/intro.html[iPython configuration file^].
You can create one of those by running the following command:
[source, bash]
----
poetry run ipython profile create
----
.Output
[source, text]
----
[ProfileCreate] Generating default config file: PosixPath('/Users/markhneedham/.ipython/profile_default/ipython_config.py')
----
If we open that file, the first few lines will read like this:
.~/.ipython/profile_default/ipython_config.py
[source, python]
----
# Configuration file for ipython.
c = get_config() #noqa
----
To disable autocomplete, let's add the following line:
[source, python]
----
c.TerminalInteractiveShell.autosuggestions_provider = None
----
If we then close the iPython REPL and launch it again, we can try the `x = ` code fragment again:
.No autocomplete
image::{{<siteurl>}}/uploads/2023/10/ipython-autocomplete-off.png[]
Job done! | In this post, we'll learn how to disable autocomplete when using the iPython REPL. | uploads/2023/10/autocomplete-banner.png | [
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] | ipython-disable-autocomplete | https://markhneedham.com/blog/2023/10/29/ipython-disable-autocomplete |
false | 2023-10-16 00:44:37 | ClickHouse: Code: 60. DB::Exception: Table does not exist | [
"clickhouse",
"clickhouse-local",
"til"
] | [
"TIL"
] | :icons: font
I've been playing with https://www.markhneedham.com/blog/2023/07/03/clickhouse-unpack-unnest-array/[clickhouse-local again^] this week and ran into an interesting issue when persisting a table that I thought I'd document for future Mark.
You can install ClickHouse on your machine by running the following command:
[source, bash]
----
curl https://clickhouse.com/ | sh
----
Or you could use HomeBrew if you're working on a Mac:
[source, bash]
----
brew install clickhouse
----
We can then launch clickhouse-local, which lets you run ClickHouse in what I think of as an embedded mode.
I'm also going to specify a directory for it to store any data that I create:
[source, bash]
----
./clickhouse local -m --path people.chdb
----
We're going to create a table based on the following CSV file:
.people.csv
[format="csv", options="header"]
|===
include::content/2023/10/16/data/people.csv[]
|===
Which we can do by running this query:
[source, sql]
----
create table people ENGINE=MergeTree() ORDER BY tuple() AS
select *
from file('people.csv', CSVWithNames);
----
If we then query the `peoples` table:
[source, sql]
----
SELECT *
FROM people
----
.Output
[source, text]
----
Query id: 8567a2c0-5a98-4981-9097-fd5ed33d173e
┌─name────┬─hobby────┐
│ Mark │ tennis │
│ Michael │ football │
│ James │ cricket │
└─────────┴──────────┘
3 rows in set. Elapsed: 0.002 sec.
----
All good so far!
But if we close that session, launch ClickHouse Local again, and then re-run the query we'll get the following error:
.Output
[source, text]
----
Query id: ffab38e6-b39c-4be4-88b0-834f43a67cd9
0 rows in set. Elapsed: 0.262 sec.
Received exception:
Code: 60. DB::Exception: Table _local.people does not exist. (UNKNOWN_TABLE
----
The problem that we've run into is described on https://github.com/ClickHouse/ClickHouse/issues/31163[this GitHub issue^]:
[quote]
____
The behavior is expected. The default database of clickhouse-local is using the Memory engine that does not persist the `.sql` metadata files.
____
So if we want the data to be persisted, we need to first create a database, rather than using the default one.
Let's do that:
[source, sql]
----
CREATE DATABASE mark;
----
.Output
[soruce, text]
----
Query id: 104191d4-9d63-4888-8e40-f754270233d4
Ok.
0 rows in set. Elapsed: 0.003 sec.
----
Not forgetting to then switch to that database:
[source, sql]
----
USE mark;
----
And then we'll run our create table command again.
Once we've done that, we'll close clickhouse-local and launch it again.
Let's check that our database is still there:
[source, sql]
----
SHOW DATABASES:
----
.Output
[soruce, text]
----
Query id: 56eb7dd5-20b5-46db-89da-f0f0d0b87293
┌─name───────────────┐
│ INFORMATION_SCHEMA │
│ _local │
│ information_schema │
│ mark │
│ system │
└────────────────────┘
5 rows in set. Elapsed: 0.001 sec.
----
And we can query it too:
[source, sql]
----
select * from mark.people;
----
.Output
[soruce, text]
----
Query id: 77c8c500-3309-4dd3-8244-e178af40fb41
┌─name────┬─hobby────┐
│ Mark │ tennis │
│ Michael │ football │
│ James │ cricket │
└─────────┴──────────┘
3 rows in set. Elapsed: 0.002 sec.
---- | In this post, we'll learn how to make sure a table is really persisted when using clickhouse-local. | uploads/2023/10/clickhouse-table-banner.png | [
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] | clickhouse-local-table-does-not-exist | https://markhneedham.com/blog/2023/10/16/clickhouse-local-table-does-not-exist |
false | 2023-10-26 00:44:37 | Poetry: Install does not contain any element | [
"python",
"poetry",
"til"
] | [
"TIL"
] | :icons: font
I've run into an interesting error a few times when using the Poetry package manager over the last few weeks and wanted to document it in case anyone else has the same problem.
I'm still not sure how to avoid it in the first place, so if you know, please let me know!
Anyway, let's get started.
Imagine we're creating a new project and we type the following:
[source, bash]
----
$ poetry init
----
It will pop up the following dialogue and we'll select the defaults, won't define anything interactively, and will then have it create the file:
.Output
[source, text]
----
This command will guide you through creating your pyproject.toml config.
Package name [tei-tutorial]:
Version [0.1.0]:
Description []:
Author [Mark Needham <m.h.needham@gmail.com>, n to skip]:
License []:
Compatible Python versions [^3.11]:
Would you like to define your main dependencies interactively? (yes/no) [yes] no
Would you like to define your development dependencies interactively? (yes/no) [yes] no
Generated file
[tool.poetry]
name = "tei-tutorial"
version = "0.1.0"
description = ""
authors = ["Mark Needham <m.h.needham@gmail.com>"]
readme = "README.md"
packages = [{include = "tei_tutorial"}]
[tool.poetry.dependencies]
python = "^3.11"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
----
As you can see from the output at the end, this is what my `pyproject.yaml` file contains:
[source, toml]
----
[tool.poetry]
name = "tei-tutorial"
version = "0.1.0"
description = ""
authors = ["Mark Needham <m.h.needham@gmail.com>"]
readme = "README.md"
packages = [{include = "tei_tutorial"}]
[tool.poetry.dependencies]
python = "^3.11"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
----
If I then run the following:
[source, bash]
----
poetry install
----
I get the following error message:
.Output
[source, text]
----
Installing dependencies from lock file
No dependencies to install or update
/Users/markhneedham/projects/learndatawithmark/tei-tutorial/tei_tutorial does not contain any element
----
The line in `pyproject.toml` that's causing the problem is this one:
[source, toml]
----
packages = [{include = "tei_tutorial"}]
----
I'm not entirely sure what makes that line get added because for some of my projects, it's added and for others, it isn't!
The line itself does make sense if you're using Poetry to create a library as you'd probably then have your code under the `tei-tutorial` directory in this case.
But in my case, I'm using Poetry to manage projects with a few scripts in the root directory, so I don't have that folder.
So if we want to get rid of the error, we either need to create the `tei_tutorial` directory or we need to delete the `packages` line in `pyproject.toml`
I'm going to delete it, and if I then run `poetry install` again, it's happy:
.Output
[source, text]
----
Installing dependencies from lock file
No dependencies to install or update
---- | In this post, we'll learn how to work around the Poetry 'does not contain any element' error message | uploads/2023/10/poetry-install-banner.png | [
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] | poetry-install-does-not-contain-any-element | https://markhneedham.com/blog/2023/10/26/poetry-install-does-not-contain-any-element |
false | 2023-10-09 00:44:37 | Poetry/Dagster: ImportError: cannot import name 'appengine' from 'requests.packages.urllib3.contrib' | [
"poetry",
"python",
"dagster",
"til"
] | [
"TIL"
] | :icons: font
I'm taking some tentative steps into the world of batch data pipelines and I've been following https://docs.dagster.io/integrations/duckdb/using-duckdb-with-dagster[Dagster's DuckDB tutorial] when I ran into a dependency issue that I had to work around.
In this blog post, I'll share the steps that I took in case you run into the same issue.
I'm using the https://python-poetry.org/[Poetry^] dependency management tool, but I think you'd get the same issue even if you used `pip` directly.
We're going to start by adding the following dependencies:
[source, bash]
----
poetry add dagster duckdb dagster-duckdb dagster-duckdb-pandas dagster-webserver
----
And then we'll create a file called `dagster_iris.py` that defines one asset, which ingests the Iris dataset into DuckDB.
.dagster_iris.py
[source, python]
----
from dagster_duckdb import DuckDBResource
from dagster import Definitions, asset
@asset
def iris_dataset(duckdb: DuckDBResource) -> None:
iris_df = pd.read_csv(
"https://docs.dagster.io/assets/iris.csv",
names=[
"sepal_length_cm",
"sepal_width_cm",
"petal_length_cm",
"petal_width_cm",
"species",
],
)
with duckdb.get_connection() as conn:
conn.execute("CREATE TABLE iris.iris_dataset AS SELECT * FROM iris_df")
defs = Definitions(
assets=[iris_dataset],
resources={
"duckdb": DuckDBResource(
database="/tmp/iris.duckdb", # required
)
},
)
----
Next, we're going to launch Dagster's UI so that we can materialise this asset:
[source, bash]
----
poetry run dagster dev -f dagster_iris.py
----
But we immediately run into this error:
.Output
[source, text]
----
Traceback (most recent call last):
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/requests_toolbelt/_compat.py", line 48, in <module>
from requests.packages.urllib3.contrib import appengine as gaecontrib
ImportError: cannot import name 'appengine' from 'requests.packages.urllib3.contrib' (/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/urllib3/contrib/__init__.py)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/dagster_webserver/__main__.py", line 1, in <module>
from .cli import main # pragma: no cover
^^^^^^^^^^^^^^^^^^^^^
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/dagster_webserver/cli.py", line 28, in <module>
from .app import create_app_from_workspace_process_context
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/dagster_webserver/app.py", line 9, in <module>
from .webserver import DagsterWebserver
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/dagster_webserver/webserver.py", line 17, in <module>
from dagster_graphql import __version__ as dagster_graphql_version
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/dagster_graphql/__init__.py", line 3, in <module>
from .client import (
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/dagster_graphql/client/__init__.py", line 1, in <module>
from .client import DagsterGraphQLClient as DagsterGraphQLClient
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/dagster_graphql/client/client.py", line 12, in <module>
from gql.transport.requests import RequestsHTTPTransport
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/gql/transport/requests.py", line 11, in <module>
from requests_toolbelt.multipart.encoder import MultipartEncoder
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/requests_toolbelt/__init__.py", line 12, in <module>
from .adapters import SSLAdapter, SourceAddressAdapter
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/requests_toolbelt/adapters/__init__.py", line 12, in <module>
from .ssl import SSLAdapter
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/requests_toolbelt/adapters/ssl.py", line 16, in <module>
from .._compat import poolmanager
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/requests_toolbelt/_compat.py", line 50, in <module>
from urllib3.contrib import appengine as gaecontrib
ImportError: cannot import name 'appengine' from 'urllib3.contrib' (/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/urllib3/contrib/__init__.py)
2023-10-09 10:54:31 +0100 - dagster.daemon - INFO - Instance is configured with the following daemons: ['AssetDaemon', 'BackfillDaemon', 'SchedulerDaemon', 'SensorDaemon']
2023-10-09 10:54:35 +0100 - dagster - ERROR - An unexpected exception has occurred
Traceback (most recent call last):
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/tmp-Ef4UpWP3-py3.11/lib/python3.11/site-packages/dagster/_cli/dev.py", line 176, in dev_command
raise Exception(
Exception: dagster-webserver process shut down unexpectedly with return code 1
2023-10-09 10:54:35 +0100 - dagster - INFO - Shutting down Dagster services...
2023-10-09 10:54:35 +0100 - dagster.daemon - INFO - Received interrupt, shutting down daemon threads...
2023-10-09 10:54:35 +0100 - dagster.daemon - INFO - Daemon threads shut down.
2023-10-09 10:54:35 +0100 - dagster - INFO - Dagster services shut down.
----
I came across https://stackoverflow.com/questions/76175487/sudden-importerror-cannot-import-name-appengine-from-requests-packages-urlli[this thread^], which describes a way to fix it.
[quote]
____
The cause is that the latest version of requests does not support urllib3 2.0.0. This is fixed in kfp-2.0.0b16 (see PR with the change), so you can either upgrade to that, or create a new image that downgrades urllib.
____
So I ran the following command:
[source, bash]
----
poetry add urllib3==1.26.15 requests-toolbelt==0.10.1
----
When I then re-run the `dagster dev` command, I can happily load the UI:
[source, text]
----
2023-10-09 10:55:58 +0100 - dagster - INFO - Using temporary directory /private/tmp/tmpswb90dld for storage. This will be removed when dagster dev exits.
2023-10-09 10:55:58 +0100 - dagster - INFO - To persist information across sessions, set the environment variable DAGSTER_HOME to a directory to use.
2023-10-09 10:55:58 +0100 - dagster - INFO - Launching Dagster services...
2023-10-09 10:55:59 +0100 - dagster.daemon - INFO - Instance is configured with the following daemons: ['AssetDaemon', 'BackfillDaemon', 'SchedulerDaemon', 'SensorDaemon']
2023-10-09 10:56:00 +0100 - dagster-webserver - INFO - Serving dagster-webserver on http://127.0.0.1:3000 in process 31935
---- | In this post, we'll learn how to work around a dependency issue when trying to run Dagster. | uploads/2023/10/dagster-banner.png | [
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] | dagster-poetry-importerror-cannot-import-appengine-requests | https://markhneedham.com/blog/2023/10/09/dagster-poetry-importerror-cannot-import-appengine-requests |
false | 2023-10-01 00:44:37 | dbt-duckdb: KeyError: "'winner_seed'" | [
"duckdb",
"dbt",
"til"
] | [
"TIL"
] | :icons: font
I've been building a little demo with dbt and DuckDB to transform CSV files from https://github.com/JeffSackmann/tennis_atp/[Jeff Sackmann's tennis dataset^] and ran into an error that initially puzzled me.
In this blog post, we'll learn how to deal with it.
But first things first, we're going to install https://github.com/duckdb/dbt-duckdb[`dbt-duckdb`^] as well as the latest version of DuckDB, which at the time of writing is 0.9.0.
[source, bash]
----
pip install dbt-duckdb duckdb
----
I then cloned Mehdi Ouazza's https://github.com/mehd-io/dbt-duckdb-tutorial[demo project] and adjusted it to work with my dataset.
I want to create a pipeline that takes one CSV file and removes all the columns that start with `w_` or `l_`.
The following query returns one row from the 2023 CSV file with the `winner_seed` and `loser_seed` fields explicitly coerced to a `VARCHAR` type:
[source, sql]
----
.mode line
FROM read_csv_auto('https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_2023.csv',
types={'winner_seed': 'VARCHAR', 'loser_seed': 'VARCHAR'}
)
LIMIT 1;
----
.Output
[source, text]
----
tourney_id = 2023-9900
tourney_name = United Cup
surface = Hard
draw_size = 18
tourney_level = A
tourney_date = 20230102
match_num = 300
winner_id = 126203
winner_seed = 3
winner_entry =
winner_name = Taylor Fritz
winner_hand = R
winner_ht = 193
winner_ioc = USA
winner_age = 25.1
loser_id = 126610
loser_seed = 5
loser_entry =
loser_name = Matteo Berrettini
loser_hand = R
loser_ht = 196
loser_ioc = ITA
loser_age = 26.7
score = 7-6(4) 7-6(6)
best_of = 3
round = F
minutes = 135
w_ace = 15
w_df = 2
w_svpt = 85
w_1stIn = 52
w_1stWon = 45
w_2ndWon = 16
w_SvGms = 12
w_bpSaved = 0
w_bpFaced = 0
l_ace = 7
l_df = 2
l_svpt = 97
l_1stIn = 62
l_1stWon = 47
l_2ndWon = 15
l_SvGms = 12
l_bpSaved = 9
l_bpFaced = 9
winner_rank = 9
winner_rank_points = 3355
loser_rank = 16
loser_rank_points = 2375
----
You can see that there are a lot of fields that begin with `w_` and `l_`, but I'm not particularly interested in them for my use case.
So, I created a source that contained the `read_csv_auto` part of the query as the `external_location`:
.models/atp/sources.yml
[source, yml]
----
version: 2
- name: github_2023
meta:
external_location: >
read_csv_auto(
'https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_2023.csv',
types={'winner_seed': 'VARCHAR', 'loser_seed': 'VARCHAR'}
)
tables:
- name: matches_file
----
Once I'd done that, I created a model to query that CSV file, removed the fields, and wrote the output to a new CSV file.
The model file looks like this:
.models/atp/matches_csv.sql
[source, sql]
----
{{ config(materialized='external', location='output/matches_2023.csv', format='csv')}} -- <.>
SELECT
COLUMNS(col -> NOT regexp_matches(col, 'w_.*') AND NOT regexp_matches(col, 'l_.*'))
FROM {{ source('github', 'matches_file') }} -- <.>
----
<.> Write the results to `output/matches_2023.sql`
<.> Query the CSV file that we defined in the sources file
I then ran the following command:
[source, bash]
----
dbt run
----
Which resulted in the following error:
.Output
[source, text]
----
16:03:38 Running with dbt=1.6.4
16:03:38 Registered adapter: duckdb=1.6.0
16:03:38 Encountered an error:
"'winner_seed'"
16:03:38 Traceback (most recent call last):
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-pipeline-fzeuMXbM-py3.11/lib/python3.11/site-packages/dbt/cli/requires.py", line 87, in wrapper
result, success = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-pipeline-fzeuMXbM-py3.11/lib/python3.11/site-packages/dbt/cli/requires.py", line 72, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-pipeline-fzeuMXbM-py3.11/lib/python3.11/site-packages/dbt/cli/requires.py", line 143, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-pipeline-fzeuMXbM-py3.11/lib/python3.11/site-packages/dbt/cli/requires.py", line 172, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-pipeline-fzeuMXbM-py3.11/lib/python3.11/site-packages/dbt/cli/requires.py", line 219, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-pipeline-fzeuMXbM-py3.11/lib/python3.11/site-packages/dbt/cli/requires.py", line 246, in wrapper
manifest = ManifestLoader.get_full_manifest(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-pipeline-fzeuMXbM-py3.11/lib/python3.11/site-packages/dbt/parser/manifest.py", line 316, in get_full_manifest
manifest = loader.load()
^^^^^^^^^^^^^
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-pipeline-fzeuMXbM-py3.11/lib/python3.11/site-packages/dbt/parser/manifest.py", line 505, in load
patcher.construct_sources()
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-pipeline-fzeuMXbM-py3.11/lib/python3.11/site-packages/dbt/parser/sources.py", line 85, in construct_sources
parsed = self.parse_source(patched)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-pipeline-fzeuMXbM-py3.11/lib/python3.11/site-packages/dbt/parser/sources.py", line 189, in parse_source
parsed_source.relation_name = self._get_relation_name(parsed_source)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-pipeline-fzeuMXbM-py3.11/lib/python3.11/site-packages/dbt/parser/sources.py", line 290, in _get_relation_name
return str(relation_cls.create_from(self.root_project, node))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-pipeline-fzeuMXbM-py3.11/lib/python3.11/site-packages/dbt/adapters/base/relation.py", line 259, in create_from
return cls.create_from_source(node, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-pipeline-fzeuMXbM-py3.11/lib/python3.11/site-packages/dbt/adapters/duckdb/relation.py", line 34, in create_from_source
ext_location = ext_location_template.format_map(source_config.as_dict())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
KeyError: "'winner_seed'"
----
It seems to be treating the `{}` in the `external_location` as if it were a Jinja template variable, which isn't what we want.
Luckily https://github.com/duckdb/dbt-duckdb#configuring-dbt-duckdb-plugins[the docs describe a way to work around this^], by specifying `formatter: oldstyle`:
.models/atp/sources.yml
[source, yml]
----
version: 2
- name: github_2023
meta:
external_location: >
read_csv_auto(
'https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_2023.csv',
types={'winner_seed': 'VARCHAR', 'loser_seed': 'VARCHAR'}
)
formatter: oldstyle
tables:
- name: matches_file
----
Once we make that change the pipeline runs as expected and writes the results to `output/matches_2023.csv`. | In this post, we'll learn how to work around an error when using the dbt-duckdb library. | uploads/2023/10/dbt-duckdb-banner.png | [
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] | dbt-duckdb-key-error | https://markhneedham.com/blog/2023/10/01/dbt-duckdb-key-error |
false | 2023-10-13 00:44:37 | Apache Superset: Refusing to start due to insecure SECRET_KEY | [
"apache-superset",
"til"
] | [
"TIL"
] | :icons: font
I've been trying to install https://pypi.org/project/apache-superset/[Apache Superset^] so that I can use it for a demo and ran into an issue with a secret key when trying to install it.
In this blog post, I'll explain how to work around it.
We're going to be using Poetry and will follow the https://superset.apache.org/docs/installation/installing-superset-from-scratch[installing from scratch^] guide.
First up is installing the library:
[source, bash]
----
poetry add apache-superset
----
And then after initialising the database with `poetry run superset db upgrade`, we'll try to create the admin user:
[source, bash]
----
export FLASK_APP=superset
poetry run superset fab create-admin
----
Running this command resulted in the following output:
.Output
[source, text]
----
--------------------------------------------------------------------------------
WARNING
--------------------------------------------------------------------------------
A Default SECRET_KEY was detected, please use superset_config.py to override it.
Use a strong complex alphanumeric string and use a tool to help you generate
a sufficiently random sequence, ex: openssl rand -base64 42
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Refusing to start due to insecure SECRET_KEY
----
I learnt that it wants me to specify the `SUPERSET_SECRET_KEY`, a problem that's been https://github.com/apache/superset/discussions/23598[encountered by several others^].
So let's set that environment variable and try again:
[source, bash]
----
export SUPERSET_SECRET_KEY="oh-so-secret" # <.>
poetry run superset fab create-admin
----
<.> I should probably use a more secret, but this is just for fun so I'll be lazy for now.
And this time it's happy:
.Output
[source, text]
----
logging was configured successfully
2023-10-13 13:54:52,555:INFO:superset.utils.logging_configurator:logging was configured successfully
2023-10-13 13:54:52,557:INFO:root:Configured event logger of type <class 'superset.utils.log.DBEventLogger'>
/Users/markhneedham/Library/Caches/pypoetry/virtualenvs/data-app-er_2mvJz-py3.11/lib/python3.11/site-packages/flask_limiter/extension.py:336: UserWarning: Using the in-memory storage for tracking rate limits as no storage was explicitly specified. This is not recommended for production use. See: https://flask-limiter.readthedocs.io#configuring-a-storage-backend for documentation about configuring the storage backend.
warnings.warn(
Username [admin]:
User first name [admin]:
User last name [user]:
Email [admin@fab.org]:
Password:
Repeat for confirmation:
Recognized Database Authentications.
Admin User admin created.
---- | In this post, we'll learn how to configure a secret for using Apache Superset. | uploads/2023/10/superset-banner.png | [
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] | apache-superset-refusing-start-insecure-key | https://markhneedham.com/blog/2023/10/13/apache-superset-refusing-start-insecure-key |
false | 2023-07-03 04:44:37 | ClickHouse: How to unpack or unnest an array | [
"clickhouse",
"clickhouse-local",
"til"
] | [
"TIL"
] | :icons: font
I recently came across https://clickhouse.com/docs/en/operations/utilities/clickhouse-local[clickhouse-local^] via https://www.vantage.sh/blog/clickhouse-local-vs-duckdb[this article^] in the https://motherduck.com/blog/duckdb-ecosystem-newsletter-seven/[MotherDuck monthly newsletter^] and I wanted to give it a try on my https://github.com/mneedham/xg[expected goals dataset^].
One of the first things that I wanted to do was unpack an array and in this blog post, we'll learn how to do that.
I installed Clickhouse by running the following command:
[source, bash]
----
curl https://clickhouse.com/ | sh
----
And then launched the clickhouse-local CLI like this:
[source, bash]
----
./clickhouse local -m # <1>
----
<1> I'm passing in the `-m` flag to enable multi-line queries, otherwise queries are executed when you press enter.
This dataset consists of a bunch of JSON files that live in the `data` directory.
Let's start by having a look at one row of one of them, which I initially tried to do like this:
[source, sql]
----
SELECT *
FROM file('data/*.json')
LIMIT 1
FORMAT Vertical
----
But that didn't work so well:
.Output
[source, text]
----
Query id: 6b1ed5fa-4e7a-4844-87f5-6c89169e63ea
0 rows in set. Elapsed: 0.411 sec.
Received exception:
Code: 117. DB::Exception: Expected field "meta" with columns names and types, found field h: Cannot extract table structure from JSON format file. You can specify the structure manually. (INCORRECT_DATA)
----
We need to tell it how to process the files.
There are a series of options that you can pass in, one of which is `JSONEachRow`, so let's try that:
[source, sql]
----
select *
from file('data/*.json', 'JSONEachRow')
LIMIT 1
FORMAT Vertical;
----
Note that I'm using `FORMAT Verticall` as the structure of the data messes up the column view.
The result of running this query is shown below:
.Output
[source, text]
----
Query id: 33b54e04-c73e-4fde-a0fb-5fb9b00fba99
Row 1:
──────
h: ['{"id": "54521", "minute": "43", "result": "MissedShots", "X": "0.9419999694824219", "Y": "0.52", "xG": "0.07078909873962402", "player": "Chancel Mbemba", "h_a": "h", "player_id": "849", "situation": "FromCorner", "season": "2015", "shotType": "Head", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "Papiss Demba Ciss\\u00e9", "lastAction": "Pass"}','{"id": "54522", "minute": "44", "result": "MissedShots", "X": "0.6940000152587891", "Y": "0.3840000152587891", "xG": "0.033031001687049866", "player": "Georginio Wijnaldum", "h_a": "h", "player_id": "771", "situation": "DirectFreekick", "season": "2015", "shotType": "RightFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": null, "lastAction": "Standard"}','{"id": "54525", "minute": "50", "result": "MissedShots", "X": "0.9319999694824219", "Y": "0.39", "xG": "0.12170200049877167", "player": "Georginio Wijnaldum", "h_a": "h", "player_id": "771", "situation": "OpenPlay", "season": "2015", "shotType": "RightFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "Moussa Sissoko", "lastAction": "Pass"}','{"id": "54527", "minute": "54", "result": "BlockedShot", "X": "0.785", "Y": "0.4809999847412109", "xG": "0.03719690069556236", "player": "Georginio Wijnaldum", "h_a": "h", "player_id": "771", "situation": "OpenPlay", "season": "2015", "shotType": "RightFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "Jack Colback", "lastAction": "Pass"}','{"id": "54528", "minute": "68", "result": "MissedShots", "X": "0.925", "Y": "0.31799999237060544", "xG": "0.027039799839258194", "player": "Georginio Wijnaldum", "h_a": "h", "player_id": "771", "situation": "OpenPlay", "season": "2015", "shotType": "RightFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "Moussa Sissoko", "lastAction": "Cross"}','{"id": "54534", "minute": "92", "result": "Goal", "X": "0.889000015258789", "Y": "0.5609999847412109", "xG": "0.3565869927406311", "player": "Georginio Wijnaldum", "h_a": "h", "player_id": "771", "situation": "OpenPlay", "season": "2015", "shotType": "RightFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "Moussa Sissoko", "lastAction": "Throughball"}']
a: ['{"id": "54518", "minute": "1", "result": "BlockedShot", "X": "0.8109999847412109", "Y": "0.500999984741211", "xG": "0.03640669956803322", "player": "Martin Skrtel", "h_a": "a", "player_id": "607", "situation": "FromCorner", "season": "2015", "shotType": "RightFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": null, "lastAction": "None"}','{"id": "54519", "minute": "20", "result": "MissedShots", "X": "0.9669999694824218", "Y": "0.48200000762939454", "xG": "0.5759689807891846", "player": "Christian Benteke", "h_a": "a", "player_id": "606", "situation": "FromCorner", "season": "2015", "shotType": "RightFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "Joe Allen", "lastAction": "HeadPass"}','{"id": "54520", "minute": "30", "result": "BlockedShot", "X": "0.7480000305175781", "Y": "0.28899999618530275", "xG": "0.014502700418233871", "player": "Nathaniel Clyne", "h_a": "a", "player_id": "603", "situation": "OpenPlay", "season": "2015", "shotType": "RightFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "Christian Benteke", "lastAction": "Pass"}','{"id": "54523", "minute": "45", "result": "MissedShots", "X": "0.8069999694824219", "Y": "0.7290000152587891", "xG": "0.023353099822998047", "player": "Jordon Ibe", "h_a": "a", "player_id": "481", "situation": "OpenPlay", "season": "2015", "shotType": "RightFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "Christian Benteke", "lastAction": "Pass"}','{"id": "54524", "minute": "45", "result": "MissedShots", "X": "0.8580000305175781", "Y": "0.764000015258789", "xG": "0.024165699258446693", "player": "Roberto Firmino", "h_a": "a", "player_id": "482", "situation": "OpenPlay", "season": "2015", "shotType": "RightFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "James Milner", "lastAction": "Pass"}','{"id": "54526", "minute": "52", "result": "MissedShots", "X": "0.7930000305175782", "Y": "0.49700000762939456", "xG": "0.04398920014500618", "player": "Joe Allen", "h_a": "a", "player_id": "480", "situation": "OpenPlay", "season": "2015", "shotType": "RightFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "Roberto Firmino", "lastAction": "Pass"}','{"id": "54529", "minute": "68", "result": "OwnGoal", "X": "0.03200000047683716", "Y": "0.5520000076293945", "xG": "0", "player": "Martin Skrtel", "h_a": "a", "player_id": "607", "situation": "OpenPlay", "season": "2015", "shotType": "RightFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": null, "lastAction": "None"}','{"id": "54530", "minute": "73", "result": "MissedShots", "X": "0.7030000305175781", "Y": "0.51", "xG": "0.014298499561846256", "player": "Daniel Sturridge", "h_a": "a", "player_id": "483", "situation": "OpenPlay", "season": "2015", "shotType": "LeftFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "Jordon Ibe", "lastAction": "Pass"}','{"id": "54531", "minute": "74", "result": "MissedShots", "X": "0.899000015258789", "Y": "0.345", "xG": "0.09887190163135529", "player": "Daniel Sturridge", "h_a": "a", "player_id": "483", "situation": "OpenPlay", "season": "2015", "shotType": "RightFoot", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "Adam Lallana", "lastAction": "Throughball"}','{"id": "54532", "minute": "85", "result": "MissedShots", "X": "0.9540000152587891", "Y": "0.46599998474121096", "xG": "0.12833499908447266", "player": "Dejan Lovren", "h_a": "a", "player_id": "602", "situation": "FromCorner", "season": "2015", "shotType": "Head", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "James Milner", "lastAction": "Cross"}','{"id": "54533", "minute": "88", "result": "SavedShot", "X": "0.8930000305175781", "Y": "0.5720000076293945", "xG": "0.039151400327682495", "player": "Dejan Lovren", "h_a": "a", "player_id": "602", "situation": "FromCorner", "season": "2015", "shotType": "Head", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "Alberto Moreno", "lastAction": "Cross"}']
1 row in set. Elapsed: 0.011 sec.
----
We have two properties, `h` and `a`, which both contain arrays of objects.
When I analysed this dataset with DuckDB, I was able to unpack the arrays using the `unnest` function:
.DuckDB
[source, sql]
----
FROM 'data/shots_*.json'
SELECT unnest(h) AS row
LIMIT 1;
----
The equivalent function in ClickHouse is https://clickhouse.com/docs/en/sql-reference/functions/array-join[`arrayJoin`^], so let's give that a try:
[source, sql]
----
SELECT arrayJoin(h)
FROM file('data/*.json', 'JSONEachRow')
LIMIT 1
FORMAT Vertical;
----
.Output
[source, text]
----
Query id: ac00a1d1-3109-4d9b-997f-e7daaf277f70
Row 1:
──────
arrayJoin(h): {"id": "54521", "minute": "43", "result": "MissedShots", "X": "0.9419999694824219", "Y": "0.52", "xG": "0.07078909873962402", "player": "Chancel Mbemba", "h_a": "h", "player_id": "849", "situation": "FromCorner", "season": "2015", "shotType": "Head", "match_id": "229", "h_team": "Newcastle United", "a_team": "Liverpool", "h_goals": "2", "a_goals": "0", "date": "2015-12-06 20:00:00", "player_assisted": "Papiss Demba Ciss\u00e9", "lastAction": "Pass"}
1 row in set. Elapsed: 0.004 sec.
----
Not bad!
Alternatively, we can use the https://clickhouse.com/docs/en/sql-reference/statements/select/array-join[`ARRAY JOIN`^] clause:
[source, sql]
----
SELECT h
FROM file('data/shots_*.json', 'JSONEachRow')
ARRAY JOIN h
LIMIT 1
FORMAT Vertical;
----
This clause lets us do more things than `arrayJoin`, but for this use case, either option will do.
To unpack all the shot events, we end up with the following query:
[source, sql]
----
SELECT h AS shot
FROM file('data/shots_*.json', 'JSONEachRow')
ARRAY JOIN h
UNION ALL
SELECT a AS shot
FROM file('data/shots_*.json', 'JSONEachRow')
ARRAY JOIN a
FORMAT Vertical;
----
.Truncated Output
[source, text]
----
Query id: 21c865f6-6130-4fb1-8b6e-8fb948415ec7
Row 1:
──────
shot: {"id": "186307", "minute": "28", "result": "MissedShots", "X": "0.91", "Y": "0.34200000762939453", "xG": "0.013558999635279179", "player": "Harry Maguire", "h_a": "h", "player_id": "1687", "situation": "OpenPlay", "season": "2017", "shotType": "Head", "match_id": "7334", "h_team": "Leicester", "a_team": "Huddersfield", "h_goals": "3", "a_goals": "0", "date": "2018-01-01 15:00:00", "player_assisted": "Christian Fuchs", "lastAction": "Aerial"}
...
Row 10000:
──────────
shot: {"id": "14564", "minute": "42", "result": "BlockedShot", "X": "0.8819999694824219", "Y": "0.655", "xG": "0.06507989764213562", "player": "Charlie Austin", "h_a": "h", "player_id": "848", "situation": "OpenPlay", "season": "2014", "shotType": "LeftFoot", "match_id": "4751", "h_team": "Queens Park Rangers", "a_team": "Hull", "h_goals": "0", "a_goals": "1", "date": "2014-08-16 15:00:00", "player_assisted": null, "lastAction": "BallRecovery"}
Showed first 10000.
81860 rows in set. Elapsed: 0.260 sec. Processed 1.43 thousand rows, 8.41 MB (5.49 thousand rows/s., 32.38 MB/s.)
---- | In this post, we're going to learn how to unnest an array when using clickhouse-local. | uploads/2023/07/clickhouse-unnest-banner.png | [
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] | clickhouse-unpack-unnest-array | https://markhneedham.com/blog/2023/07/03/clickhouse-unpack-unnest-array |
false | 2023-07-20 04:44:37 | Docker: Failed to create network: Error response from daemon: could not find an available, non-overlapping IPv4 address pool among the defaults to assign to the network | [
"docker",
"til"
] | [
"TIL"
] | :icons: font
I use Docker for pretty much every demo I create and this sometimes results in me running out of IP addresses to serve all those networks.
In this blog post, we'll learn how to diagnose and solve this issue.
Our story starts with the following command on a new project:
[source, bash]
----
docker compose up
----
Usually this purs along nicely and all our components spin up just fine, but today is not our lucky day and we get the following error:
[source, bash]
----
[+] Building 0.0s (0/0)
[+] Running 1/0
✘ Network avro-ingestion Error 0.0s
failed to create network avro-ingestion: Error response from daemon: could not find an available, non-overlapping IPv4 address pool among the defaults to assign to the network
----
Hmmm, we're out of IPv4 addresses, that doesn't sound too good!
We can run the following command to check how many networks we have:
[source, bash]
----
docker network ls
----
I get the following results when running it right now:
.Output
[source, bash]
----
NETWORK ID NAME DRIVER SCOPE
49a68a4670a5 avro-ingestion bridge local
286c2a9ed537 bridge bridge local
e3d97260204b filtering bridge local
...
8ef1a57d8df2 stock-upserts bridge local
a86781534e85 traffic-upserts bridge local
----
If you see a lot of results then it's probably the case that, like me, you've run out of local IP addresses to assign to new networks.
You can also run the following script to check which of these networks are in use by running containers:
[source, bash]
----
for network in $(docker network ls --format "{{.Name}}"); do
container_names=$(docker network inspect $network |
jq -r '.[] | .Containers[] | .Name'
)
echo "$network:"
if [ -z "$container_names" ]; then
echo -e "\tNo containers"
else
while IFS= read -r line
do
echo -e "\t$line"
done <<< "$container_names"
fi
done
----
If I run this, I see the following (truncated) output:
.Output
[source, text]
----
avro-ingestion:
No containers
bridge:
No containers
filtering:
No containers
...
traffic-upserts:
zookeeper-traffic-upserts
pinot-controller-traffic-upserts
pinot-broker-traffic-upserts
pinot-server-traffic-upserts
kafka-traffic-upserts
----
It's very likely that nearly all our your networks won't have any running containers, in which case you can prune them using the following command:
[source, bash]
----
docker network prune
----
You'll see the following output as it iterates through all the networks that aren't currently in use:
[source, text]
----
Deleted Networks:
avro-ingestion
filtering
...
traffic-upserts
----
And now you should be able to create a new network.
If this doesn't solve the problem, check out this https://stackoverflow.com/questions/43720339/docker-error-could-not-find-an-available-non-overlapping-ipv4-address-pool-am[StackOverflow^] thread for some other things to try. | In this post, we're going to learn how to deal with a Docker error when we've run out of IP addresses. | uploads/2023/07/docker-network-banner.png | [
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] | docker-network-could-not-find-non-overlapping-address-pool | https://markhneedham.com/blog/2023/07/20/docker-network-could-not-find-non-overlapping-address-pool |
false | 2023-07-18 04:44:37 | Redpanda: Configure pruning/retention of data | [
"redpanda",
"til"
] | [
"TIL"
] | :icons: font
I wanted to test how Apache Pinot deals with data being truncated from the underlying stream from which it's consuming, so I've been trying to work out how to prune data in Redpanda.
In this blog post, I'll share what I've learnt so far.
We're going to spin up a Redpanda cluster using the following Docker Compose file:
.docker-compose.yml
[source, yaml]
----
version: '3.7'
services:
redpanda:
container_name: "redpanda-pruning"
image: docker.redpanda.com/vectorized/redpanda:v22.2.2
command:
- redpanda start
- --smp 1
- --overprovisioned
- --node-id 0
- --kafka-addr PLAINTEXT://0.0.0.0:29092,OUTSIDE://0.0.0.0:9092
- --advertise-kafka-addr PLAINTEXT://redpanda:29092,OUTSIDE://localhost:9092
- --pandaproxy-addr 0.0.0.0:8082
- --advertise-pandaproxy-addr localhost:8082
ports:
- 8081:8081
- 8082:8082
- 9092:9092
- 9644:9644
- 29092:29092
----
We can launch Redpanda by running `docker compose up`.
Next, we're going to ingest some data using the following script:
.datagen.py
[source, python]
----
import datetime, uuid, random, json
def generate(sleep):
while True:
ts = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%fZ")
id = str(uuid.uuid4())
count = random.randint(0, 1000)
print(json.dumps({"tsString": ts, "uuid": id, "count": count}))
if __name__ == '__main__':
generate()
----
We'll pipe the output of that script into a combination of jq and kcat, as shown below:
[source, bash]
----
python datagen.py --sleep 0.0001 2>/dev/null |
jq -cr --arg sep 😊 '[.uuid, tostring] | join($sep)' |
kcat -P -b localhost:9092 -t events -K😊
----
The data will now be ingested into the `events` topic.
We can then get an overview of the `events` topic by running the following command
[source, bash]
----
rpk topic describe events -a
----
.Output
[source, text]
----
SUMMARY
=======
NAME events
PARTITIONS 1
REPLICAS 1
CONFIGS
=======
KEY VALUE SOURCE
cleanup.policy delete DEFAULT_CONFIG
compression.type producer DEFAULT_CONFIG
message.timestamp.type CreateTime DEFAULT_CONFIG
partition_count 1 DYNAMIC_TOPIC_CONFIG
redpanda.datapolicy function_name: script_name: DEFAULT_CONFIG
redpanda.remote.read false DEFAULT_CONFIG
redpanda.remote.write false DEFAULT_CONFIG
replication_factor 1 DYNAMIC_TOPIC_CONFIG
retention.bytes -1 DEFAULT_CONFIG
retention.ms 604800000 DEFAULT_CONFIG
segment.bytes 1073741824 DEFAULT_CONFIG
PARTITIONS
==========
PARTITION LEADER EPOCH REPLICAS LOG-START-OFFSET HIGH-WATERMARK
0 0 1 [0] 0 55585
----
Redpanda stores data in segment files, which have a default size of 1GB.
All log rotation and truncation happen https://docs.redpanda.com/docs/manage/cluster-maintenance/disk-utilization/[when we reach a segment rotation^], so if we want to increase the frequency or size of rotation, we need to first reduce segment size.
The segment size is controlled at the topic level by the `segment.bytes` property (or `log_segment_size` globally).
We can run the following command using the rpk command line tool to set the segment size to 100MB:
[source, bash]
----
rpk topic alter-config events --set segment.bytes=10000000
----
We can check the size of the segment files by running the following commmand:
[source, bash]
----
docker exec -it redpanda-pruning sh -c \
"ls -alh --full-time /var/lib/redpanda/data/kafka/events/**/*.log |
cut --d ' ' -f5-"
----
.Output
[source, text]
----
153M 2023-07-18 14:08:47.463652007 +0000 /var/lib/redpanda/data/kafka/events/0_15/0-1-v1.log
----
We'll need to wait for this file to get to 1GB before the updated segment size config gets used.
We can check that the config has been picked up by re-running `rpk topic describe events -a`:
.Output
[source, text]
----
SUMMARY
=======
NAME events
PARTITIONS 1
REPLICAS 1
CONFIGS
=======
KEY VALUE SOURCE
cleanup.policy delete DEFAULT_CONFIG
compression.type producer DEFAULT_CONFIG
message.timestamp.type CreateTime DEFAULT_CONFIG
partition_count 1 DYNAMIC_TOPIC_CONFIG
redpanda.datapolicy function_name: script_name: DEFAULT_CONFIG
redpanda.remote.read false DEFAULT_CONFIG
redpanda.remote.write false DEFAULT_CONFIG
replication_factor 1 DYNAMIC_TOPIC_CONFIG
retention.bytes -1 DEFAULT_CONFIG
retention.ms 604800000 DEFAULT_CONFIG
segment.bytes 10000000 DYNAMIC_TOPIC_CONFIG
PARTITIONS
==========
PARTITION LEADER EPOCH REPLICAS LOG-START-OFFSET HIGH-WATERMARK
0 0 1 [0] 0 1824800
----
While we're waiting, we're also going to reduce the retention time for the topic so old data gets purged sooner.
We can do that with a couple of properties, `retention.ms` (`delete_retention_ms` globally) and `retention.bytes` (`retention_bytes` globally):
[source, bash]
----
rpk topic alter-config events --set retention.ms=60000 # <1>
rpk topic alter-config events --set retention.bytes=100000000 # <2>
----
<.> Delete segments that are older than 60 seconds.
<.> Default 100 million bytes per partition on disk before triggering deletion of the oldest messages.
We're setting the retention time to 60 seconds and the retention size to 100,000,000 bytes.
These parameters will be applied straight away, but they won't have any impact until we have a segment rotation.
If we re-run `rpk topic describe events -a` again, we'll see that the following output:
.Output
[source, text]
----
SUMMARY
=======
NAME events
PARTITIONS 1
REPLICAS 1
CONFIGS
=======
KEY VALUE SOURCE
cleanup.policy delete DEFAULT_CONFIG
compression.type producer DEFAULT_CONFIG
message.timestamp.type CreateTime DEFAULT_CONFIG
partition_count 1 DYNAMIC_TOPIC_CONFIG
redpanda.datapolicy function_name: script_name: DEFAULT_CONFIG
redpanda.remote.read false DEFAULT_CONFIG
redpanda.remote.write false DEFAULT_CONFIG
replication_factor 1 DYNAMIC_TOPIC_CONFIG
retention.bytes 100000000 DYNAMIC_TOPIC_CONFIG
retention.ms 60000 DYNAMIC_TOPIC_CONFIG
segment.bytes 10000000 DYNAMIC_TOPIC_CONFIG
PARTITIONS
==========
PARTITION LEADER EPOCH REPLICAS LOG-START-OFFSET HIGH-WATERMARK
0 0 1 [0] 0 4437102
----
If we wait a little bit longer, the segment file will have reached 1GB and our settings will kick into action.
We'll see something like the following entry in the Docker logs:
.Output
[source, text]
----
redpanda-pruning | INFO 2023-07-18 14:24:43,377 [shard 0] storage - segment.cc:623 - Creating new segment /var/lib/redpanda/data/kafka/events/0_15/7589093-1-v1.log
redpanda-pruning | INFO 2023-07-18 14:24:44,272 [shard 0] storage - disk_log_impl.cc:997 - remove_prefix_full_segments - tombstone & delete segment: {offset_tracker:{term:1, base_offset:0, committed_offset:7589092, dirty_offset:7589092}, compacted_segment=0, finished_self_compaction=0, generation={98553}, reader={/var/lib/redpanda/data/kafka/events/0_15/0-1-v1.log, (1107034409 bytes)}, writer=nullptr, cache={cache_size=49276}, compaction_index:nullopt, closed=0, tombstone=0, index={file:/var/lib/redpanda/data/kafka/events/0_15/0-1-v1.base_index, offsets:{0}, index:{header_bitflags:0, base_offset:{0}, max_offset:{7589092}, base_timestamp:{timestamp: 1689689163391}, max_timestamp:{timestamp: 1689690283332}, index(31961,31961,31961)}, step:32768, needs_persistence:0}}
----
And the output of the Docker command that tails the log directory will look like this:
.Output
[source, text]
----
9.9M 2023-07-18 14:24:53.697925010 +0000 /var/lib/redpanda/data/kafka/events/0_15/7589093-1-v1.log
9.9M 2023-07-18 14:25:04.066882001 +0000 /var/lib/redpanda/data/kafka/events/0_15/7659797-1-v1.log
9.9M 2023-07-18 14:25:14.705882006 +0000 /var/lib/redpanda/data/kafka/events/0_15/7730618-1-v1.log
9.9M 2023-07-18 14:25:25.243882011 +0000 /var/lib/redpanda/data/kafka/events/0_15/7801321-1-v1.log
9.9M 2023-07-18 14:25:35.684553002 +0000 /var/lib/redpanda/data/kafka/events/0_15/7872140-1-v1.log
300K 2023-07-18 14:25:35.989553002 +0000 /var/lib/redpanda/data/kafka/events/0_15/7942843-1-v1.log
----
And for old time's sake, let's describe the topic again:
.Output
[source, text]
----
SUMMARY
=======
NAME events
PARTITIONS 1
REPLICAS 1
CONFIGS
=======
KEY VALUE SOURCE
cleanup.policy delete DEFAULT_CONFIG
compression.type producer DEFAULT_CONFIG
message.timestamp.type CreateTime DEFAULT_CONFIG
partition_count 1 DYNAMIC_TOPIC_CONFIG
redpanda.datapolicy function_name: script_name: DEFAULT_CONFIG
redpanda.remote.read false DEFAULT_CONFIG
redpanda.remote.write false DEFAULT_CONFIG
replication_factor 1 DYNAMIC_TOPIC_CONFIG
retention.bytes 100000000 DYNAMIC_TOPIC_CONFIG
retention.ms 60000 DYNAMIC_TOPIC_CONFIG
segment.bytes 10000000 DYNAMIC_TOPIC_CONFIG
PARTITIONS
==========
PARTITION LEADER EPOCH REPLICAS LOG-START-OFFSET HIGH-WATERMARK
0 0 1 [0] 7730617 8227515
----
7 million messages have been truncated, just like that! | In this post, we're going to learn how to prune data in Redpanda. | uploads/2023/07/redpanda-pruning-banner.png | [
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] | redpanda-prune-retention | https://markhneedham.com/blog/2023/07/18/redpanda-prune-retention |
false | 2023-07-27 11:44:37 | Python/Poetry: Library not loaded: no such file, not in dyld cache | [
"poetry",
"python",
"til"
] | [
"TIL"
] | As I https://www.markhneedham.com/blog/2023/07/24/vscode-poetry-python-interpreter/[mentioned in a previous blog post^], I've been using Python's Poetry library, but today it stopped working!
In this blog post, I'll explain what happened and how I got it working again.
It started off innocent enough, with me trying to create a new project:
[source, bash]
----
poetry init
----
But instead of seeing the usual interactive wizard, I got the following error:
.Output
[source, text]
----
dyld[20269]: Library not loaded: /opt/homebrew/Cellar/python@3.11/3.11.3/Frameworks/Python.framework/Versions/3.11/Python
Referenced from: <1B2377F9-2187-39A9-AA98-20E438024DE2> /Users/markhneedham/Library/Application Support/pypoetry/venv/bin/python
Reason: tried: '/opt/homebrew/Cellar/python@3.11/3.11.3/Frameworks/Python.framework/Versions/3.11/Python' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/opt/homebrew/Cellar/python@3.11/3.11.3/Frameworks/Python.framework/Versions/3.11/Python' (no such file), '/opt/homebrew/Cellar/python@3.11/3.11.3/Frameworks/Python.framework/Versions/3.11/Python' (no such file), '/Library/Frameworks/Python.framework/Versions/3.11/Python' (no such file), '/System/Library/Frameworks/Python.framework/Versions/3.11/Python' (no such file, not in dyld cache)
----
It seems to be looking for Python version 3.11.3 in a variety of different places.
I installed Python using brew, so let's check what's in that directory:
[source, bash]
----
ls -lh /opt/homebrew/Cellar/python@3.11
----
.Output
[source, text]
----
total 0
drwxr-xr-x@ 13 markhneedham admin 416B 30 Jun 12:50 3.11.4_1
----
There isn't a Python 3.11.3 and I don't recall moving it, but it is possible that a `brew update` might have updated the Python version.
It also mentions that the Python version was referenced from `/Users/markhneedham/Library/Application Support/pypoetry/venv/bin/python`, which I thought might be a symbolic link that I could update.
Unfortunately, it's a binary file so we can't do that.
[source, bash]
----
file "/Users/markhneedham/Library/Application Support/pypoetry/venv/bin/python"
----
.Output
[source, text]
----
/Users/markhneedham/Library/Application Support/pypoetry/venv/bin/python: Mach-O 64-bit executable arm64
----
Luckily https://stackoverflow.com/questions/70920378/poetry-returns-dyld-library-not-loaded-image-not-found-following-brew-ins[StackOverflow came to the rescue^] with some classic turn it off and on again advice.
So let's first uninstall Poetry:
[source, bash]
----
curl -sSL https://install.python-poetry.org | python - --uninstall
----
And then reinstall it:
[source, bash]
----
curl -sSL https://install.python-poetry.org | python -
----
It works for me after doing this and Poetry's `python` executable now uses brew's version of Python:
[source, bash]
----
"/Users/markhneedham/Library/Application Support/pypoetry/venv/bin/python" --version
----
.Output
[source, text]
----
Python 3.11.4
---- | In this post, we'll learn how to work around an error when using Python's Poetry library. | uploads/2023/07/poetry-python-reinstall-banner.png | [
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] | poetry-library-not-loaded-no-such-file-dyld-cache | https://markhneedham.com/blog/2023/07/27/poetry-library-not-loaded-no-such-file-dyld-cache |
false | 2023-07-27 01:44:37 | OpenAI/GPT: Returning consistent/valid JSON from a prompt | [
"gpt",
"openai",
"python",
"til"
] | [
"TIL"
] | :icons: font
When using OpenAI it can be tricky to get it to return a consistent response for a prompt.
In this blog post, we're going to learn how to use functions to return a consistent JSON format for a basic sentiment analysis prompt.
[NOTE]
====
I've created a video showing how to do this on https://www.youtube.com/@learndatawithmark[my YouTube channel, Learn Data with Mark^], so if you prefer to consume content through that medium, I've embedded it below:
++++
<iframe width="560" height="315" src="https://www.youtube.com/embed/lJJkBaO15Po" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
++++
====
Let's start by installing OpenAI and Pandas:
[source, bash]
----
pip install openai pandas
----
And now, we'll open a Python REPL and import the following libraries:
[source, python]
----
import openai
import json
import pandas as pd
----
We're going to analyse the sentiment of some reviews from goodreads about https://www.goodreads.com/book/show/22085568-the-culture-map?from_search=true&from_srp=true&qid=JOGX5h8YsY&rank=1[The Culture Map book^].
I've put these into a CSV file, which we can load using the following code:
[source, python]
----
reviews_df = pd.read_csv("reviews.csv")
reviews = reviews_df['review'].tolist()
reviews
----
.Output
[source, text]
----
["Possibly the worst book I've ever read.It's a huge collection of biases for all the possible countries and cultures. The whole book is structured with examples like: if you are working with Chinese people, you should take this approach, instead if your team is composed by German people you should do this etc....",
'A book full of oversimplifications, generalisations and self-contradiction. Plus many of the examples felt simply made up. Although it had one or two good ideas thrown in there, I am honestly not sure if this book can hardly help anyone.',
'I had it on my recommendations list for a long time, but my impression was always like: "damn, I don\'t need a book on cultural differences; I\'ve worked in many international enterprises, I have been trained, I have practical experience - it would be just a waste of time". In the end, it wasn\'t (a waste of time).',
'Candidate for the best book I have read in 2016 unless another one can beat it. The author made is fun to read with great examples that I could easily relate to.',
'A practical and comprehensive guide to how different cultures should be approached regarding business relations, but it can also be used outside of that.',
'The book was OK. It offers a good overview of differences between cultures. Sometimes we may assume that 2 cultures are similar, but in the end there is a possibility of conflict, because they have different "mentality" on a certain point (trust or time perception, for instance). But Erin often limits herself to personal stories and doesn\'t cite almost any researcher or study.']
----
We're then going to write a function that computes the sentiment for a list of reviews using OpenAI:
[source, python]
----
def analyse_reviews(user_input):
prompt = f""" # <.>
{user_input}
Analyse the sentiment of the reviews above and return a JSON array as the result.
Provide sentiment on a scale of 1-100?
The JSON must have these fields: sentiment, sentiment_score.
"""
completion = openai.ChatCompletion.create( # <.>
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful review analysis tool."},
{"role": "user", "content": prompt},
]
)
try:
generated_text = completion.choices[0].message.content # <3>
return json.loads(generated_text)
except Exception as e:
print(f"An error occurred: {e}")
return None
----
<1> Create a prompt asking GPT to return the sentiment of provided reviews
<2> Create and execute prompt
<3> Parse result
We can then run the prompt like this:
[source, python]
----
analyse_reviews(reviews)
----
If we run that a few times, we'll likely get a different result each time:
.Output
[source, text]
----
{'sentiment': 'mixed', 'sentiment_score': 64.2}
----
.Output
[source, text]
----
[{'sentiment': 'negative', 'sentiment_score': 15},
{'sentiment': 'negative', 'sentiment_score': 25},
{'sentiment': 'positive', 'sentiment_score': 70},
{'sentiment': 'positive', 'sentiment_score': 85},
{'sentiment': 'positive', 'sentiment_score': 90},
{'sentiment': 'neutral', 'sentiment_score': 50}]
----
.Output
[source, text]
----
{'reviews': [{'sentiment': 'negative', 'sentiment_score': 12},
{'sentiment': 'negative', 'sentiment_score': 22},
{'sentiment': 'neutral', 'sentiment_score': 50},
{'sentiment': 'positive', 'sentiment_score': 85},
{'sentiment': 'positive', 'sentiment_score': 70},
{'sentiment': 'neutral', 'sentiment_score': 65}]}
----
.Output
[source, text]
----
{'reviews': [{'review': "Possibly the worst book I've ever read.It's a huge collection of biases for all the possible countries and cultures. The whole book is structured with examples like: if you are working with Chinese people, you should take this approach, instead if your team is composed by German people you should do this etc....",
'sentiment': 'negative',
'sentiment_score': 15},
{'review': 'A book full of oversimplifications, generalisations and self-contradiction. Plus many of the examples felt simply made up. Although it had one or two good ideas thrown in there, I am honestly not sure if this book can hardly help anyone.',
'sentiment': 'negative',
'sentiment_score': 20},
{'review': 'I had it on my recommendations list for a long time, but my impression was always like: "damn, I don\'t need a book on cultural differences; I\'ve worked in many international enterprises, I have been trained, I have practical experience - it would be just a waste of time". In the end, it wasn\'t (a waste of time).',
'sentiment': 'positive',
'sentiment_score': 80}]}
----
These are just 4 of the responses that I saw after running this multiple times.
Either way, we need a more deterministic response.
Lucky for us, OpenAI recently added the concept of https://platform.openai.com/docs/guides/gpt/function-calling[functions^].
With functions, you can tell OpenAI that you're going to pass its response to a function that takes in a specific JSON structure.
We aren't going to pass the result to a function, but we'll co-opt this functionality to get ourselves a consistent response!
Using this functionality, we end up with the following function:
[source, python]
----
def analyse_reviews(user_input):
prompt = f"""
{user_input}
Analyse the sentiment of the reviews above and return a JSON array as the result.
Provide sentiment on a scale of 1-100?
The JSON must have these fields: sentiment, sentiment_score.
"""
completion = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful review analysis tool."},
{"role": "user", "content": prompt},
],
functions=[{"name": "dummy_fn_set_sentiment", "parameters": { # <.>
"type": "object",
"properties": { # <.>
"sentiments": {
"type": "array",
"items": {
"type": "object",
"properties": {
"sentiment": {"type": "string", "description": "Sentiment of the review"},
"sentiment_score": {"type": "integer","description": "Score between 1-100 of the sentiment"}
}
}
}
}
}}],
)
try:
generated_text = completion.choices[0].message.function_call.arguments
return json.loads(generated_text)
except Exception as e:
print(f"An error occurred: {e}")
return None
----
<1> Specify a function name (we'll use a made-up name since we aren't going to call it)
<2> Define the arguments expected by this function in JSON schema format.
We can then call this function the same way as we did with the other one:
[source, python]
----
sentiments = analyse_reviews(reviews)
sentiments
----
.Output
[source, text]
----
{'sentiments': [{'sentiment': 'negative', 'sentiment_score': 30},
{'sentiment': 'negative', 'sentiment_score': 25},
{'sentiment': 'positive', 'sentiment_score': 70},
{'sentiment': 'positive', 'sentiment_score': 90},
{'sentiment': 'positive', 'sentiment_score': 80},
{'sentiment': 'neutral', 'sentiment_score': 50}]}
----
We won't get the same `sentiment` or `sentiment_score` each time, but the structure will be consistent.
Finally, let's put everything together into a nice DataFrame:
[source, python]
----
sentiment_df = pd.DataFrame(sentiments["sentiments"])
result = pd.concat([reviews_df, sentiment_df], axis=1)
pd.set_option('max_colwidth', 100)
result
----
.Output
[opts="header", cols="2,1,1"]
|===
|review | sentiment | sentiment_score
|Possibly the worst book I've ever read.It's a huge collection of biases for all the possible cou... |negative |30
|A book full of oversimplifications, generalisations and self-contradiction. Plus many of the exa... |negative |25
|I had it on my recommendations list for a long time, but my impression was always like: "damn, I... |positive |70
|Candidate for the best book I have read in 2016 unless another one can beat it. The author made ... |positive |90
|A practical and comprehensive guide to how different cultures should be approached regarding bus... |positive |80
|The book was OK. It offers a good overview of differences between cultures. Sometimes we may ass... |neutral |50
|=== | In this post, we'll learn how to return a consistent and predictable JSON response to a sentiment analysis prompt in OpenAI/GPT. | uploads/2023/07/openai-json-banner.png | [
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] | return-consistent-predictable-valid-json-openai-gpt | https://markhneedham.com/blog/2023/07/27/return-consistent-predictable-valid-json-openai-gpt |
false | 2023-07-11 04:44:37 | Quix Streams: Consuming and Producing JSON messages | [
"python",
"quixstreams",
"til"
] | [
"TIL"
] | :icons: font
I've been meaning to take https://quix.io/docs/client-library/quickstart.html[Quix Streams^] for a spin for a while and got the chance while building a recent demo.
Quix Streams is a library for building streaming applications on time-series data, but I wanted to use it to do some basic consuming and producing of JSON messages.
That's what we're going to do in this blog post.
We're going to use Redpanda to store our messages.
We'll launch a Redpanda instance using the following Docker Compose file:
.docker-compose.yml
[source, yml]
----
version: '3.7'
services:
redpanda:
container_name: "redpanda-quix"
image: docker.redpanda.com/vectorized/redpanda:v22.2.2
command:
- redpanda start
- --smp 1
- --overprovisioned
- --node-id 0
- --kafka-addr PLAINTEXT://0.0.0.0:29092,OUTSIDE://0.0.0.0:9092
- --advertise-kafka-addr PLAINTEXT://redpanda:29092,OUTSIDE://localhost:9092
- --pandaproxy-addr 0.0.0.0:8082
- --advertise-pandaproxy-addr localhost:8082
ports:
- 9093:9092
----
Run `docker compose up` to start the cluster and then we're going to create some topics:
[source, bash]
----
rpk topic create \
-c cleanup.policy=compact \
-r 1 -p 5 \
events big-events
----
`events` is where we'll write our generated data and `big-events` will be used later in the blog post.
Our data generator is shown below:
[source, python]
----
import datetime
import uuid
import random
import json
while True:
ts = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%fZ")
id = str(uuid.uuid4())
count = random.randint(0, 1000)
print(
json.dumps({"tsString": ts, "uuid": id[:3], "count": count})
)
----
We can then call it to generate some data and ingest it into Redpanda using the kcat tool:
[source, bash]
----
python datagen.py 2>/dev/null |
jq -cr --arg sep 😊 '[.uuid, tostring] | join($sep)' |
kcat -P -b localhost:9092 -t events -K😊
----
Let's check the messages have been ingested using the https://docs.redpanda.com/docs/reference/rpk/[rpk^] tool:
[source, bash]
----
rpk topic consume events --brokers localhost:9092 |
jq -Cc '.value | fromjson' |
head -n5
----
.Output
[source, text]
----
{"tsString":"2023-07-11T13:42:34.986675Z","uuid":"7cc","count":779}
{"tsString":"2023-07-11T13:42:34.986734Z","uuid":"122","count":234}
{"tsString":"2023-07-11T13:42:34.986811Z","uuid":"1b9","count":13}
{"tsString":"2023-07-11T13:42:34.986819Z","uuid":"22f","count":298}
{"tsString":"2023-07-11T13:42:34.986841Z","uuid":"688","count":986}
----
So far, so good.
Next, let's install Quix Streams:
[source, bash]
----
pip install quixstreams
----
And now we're going to create a little application that consumes data from the `events` topic and writes to the `big-events` topic any events that have a `count` bigger than 500:
.filter_events.py
[source, python]
----
import quixstreams as qx
from quixstreams import StreamConsumer, EventData
import json
client = qx.KafkaStreamingClient('127.0.0.1:9092')
topic_consumer = client.get_topic_consumer( #<1>
topic="events",
auto_offset_reset=qx.AutoOffsetReset.Earliest,
consumer_group="events-consumer-group"
)
def on_event_data_received_handler(stream: StreamConsumer, data: EventData):
with data:
payload = json.loads(data.value)
if payload["count"] > 500: # <2>
with (producer := client.get_raw_topic_producer("big-events")): # <3>
message = qx.RawMessage(json.dumps(payload, indent=2).encode('utf-8'))
message.key = payload["uuid"].encode('utf-8')
producer.publish(message) # <4>
def on_stream_received_handler(stream_received: StreamConsumer):
stream_received.events.on_data_received = on_event_data_received_handler
print("Listening to streams. Press CTRL-C to exit.")
topic_consumer.on_stream_received = on_stream_received_handler
topic_consumer.subscribe()
qx.App.run()
----
<.> Create consumer for `events` topic
<.> Only keep events that have a `count` bigger than 500
<.> Create producer for `big-events` topic
<.> Publish to `big-events`
Let's run this file:
[source, bash]
----
python filter_events.py
----
We can then check if any events have made it into the `big-events` topic:
[source, bash]
----
rpk topic consume big-events --brokers localhost:9092 |
jq -Cc '.value | fromjson' |
head -n5
----
.Output
[source,text]
----
{"tsString":"2023-07-11T13:42:34.986716Z","uuid":"277","count":794}
{"tsString":"2023-07-11T13:42:34.986975Z","uuid":"280","count":585}
{"tsString":"2023-07-11T13:42:34.987040Z","uuid":"92c","count":505}
{"tsString":"2023-07-11T13:42:34.987062Z","uuid":"d12","count":929}
{"tsString":"2023-07-11T13:42:34.987081Z","uuid":"6f6","count":582}
----
Job done! | In this post, we're going to learn how to consume and produce JSON messages using the Quix Streams stream processor. | uploads/2023/07/quix-banner.png | [
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] | quix-streams-consume-produce-json-messages | https://markhneedham.com/blog/2023/07/11/quix-streams-consume-produce-json-messages |
false | 2023-07-28 11:44:37 | Apache Pinot: Experimenting with the StarTree Index | [
"poetry",
"python",
"til"
] | [
"TIL"
] | :icons: font
My colleagues Sandeep Dabade and Kulbir Nijjer recently wrote a https://startree.ai/blog/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance[three^] https://startree.ai/blog/star-tree-indexes-in-apache-pinot-part-2-understanding-the-impact-during-high-concurrency[part^] https://startree.ai/blog/star-tree-index-in-apache-pinot-part-3-understanding-the-impact-in-real-customer[blog post series^] about the StarTree index, an Apache Pinot indexing technique that dynamically builds a tree structure to maintain aggregates across a group of dimensions.
I've not used this index before and wanted to give it a try and in this blog post, I'll share what I learned.
I've put all the code in the https://github.com/startreedata/pinot-recipes/tree/main/recipes/startree-index[startreedata/pinot-recipes^] GitHub repository in case you want to try it out yourself.
== Setup
Let's start by cloning the repository and navigating to the StarTree index recipe:
[source, bash]
----
git clone git@github.com:startreedata/pinot-recipes.git
cd pinot-recipes/recipes/startree-index
----
We can then spin up Apache Pinot and friends using Docker Compose:
[source, bash]
----
docker compose up
----
== Data Generator
I've created a data generator that simulates website traffic.
It used the Faker library, so make sure that you install that:
[source, bash]
----
pip install faker
----
Now let's run it and have a look at one of the generated messages:
[source, bash]
----
python datagen.py 2>/dev/null | head -n1 | jq
----
.Output
[source, json]
----
{
"ts": 1690559577897,
"userID": "a56717ef",
"timeSpent": 622,
"pageID": "page032",
"country": "Saint Lucia",
"deviceType": "Mobile",
"deviceBrand": "Dell",
"deviceModel": "Google Pixel 6",
"browserType": "Chrome",
"browserVersion": "92.0.4515.159",
"locale": "zh_CN"
}
----
We have a variety of information about the page visit, including details about the device and browser used.
We're going to ingest that data into Apache Kafka, so let's first create a topic:
[source, bash]
----
rpk topic create -p 5 webtraffic
----
And now let's ingest data into that topic:
[source, bash]
----
python datagen.py 2>/dev/null |
jq -cr --arg sep ø '[.userID, tostring] | join($sep)' |
kcat -P -b localhost:9092 -t webtraffic -Kø
----
== Apache Pinot Tables
We're going to create several tables in Apache Pinot to explore this dataset:
`webtraffic`::
No indexes
`webtraffic_inverted`::
Inverted index on `country`, `browserType`, and `deviceBrand`.
The config looks like this:
[source, yml]
----
{
"tableIndexConfig": {
"invertedIndexColumns": [
"country", "browserType", "deviceBrand"
]
}
}
----
`webtraffic_stree`::
StarTree index splitting on `country`, `browserType`, and `deviceBrand`, aggregating `COUNT(*)`, `SUM(timeSpent)`, and `AVG(timeSpent)`.
The config looks like this:
[source, yml]
----
{
"tableIndexConfig": {
"starTreeIndexConfigs": [
{
"dimensionsSplitOrder": [
"country",
"browserType",
"deviceBrand"
],
"skipStarNodeCreationForDimensions": [],
"functionColumnPairs": [
"COUNT__*",
"SUM__timeSpent",
"AVG__timeSpent"
],
"maxLeafRecords": 10000
}
]
}
}
----
== Queries
Once I'd created the tables, I waited for the data generator to get a bunch of data ingested.
I stopped the generator once it had got to 175 million rows, which isn't big data, but should be enough records for us to see how the indexes work.
From my understanding, the StarTree index should work well when we have to do filtering and then aggregation of data.
An inverted index is also good at filtering, so that configuration will serve as a useful comparison.
I came up with the following queries to kick the tyres:
Aggregating time spent by country::
This is a pure aggregation query, so the inverted index shouldn't help at all.
[source, sql]
----
select country, sum(timeSpent) AS totalTime
from webtraffic
group by country
order by totalTime DESC
limit 10
----
Filtering by country and aggregating by count::
This query filters by the `country` column and then counts the records returned (2.2 million) by browser type.
We'd expect the inverted index to do better than the one with no indexes, but StarTree's pre indexing should give it the edge.
[source, sql]
----
select browserType, count(*)
from webtraffic
WHERE country IN ('Germany', 'United Kingdom', 'Spain')
GROUP BY browserType
limit 10
----
== Comparing query performance
I ran these queries a few times in the Pinot UI to see how well they performed, but I figured that I should probably use a load generator to get more consistent results.
I'm going to use a Python performance testing tool called https://docs.locust.io/en/stable/what-is-locust.html[Locust^] to do this.
[NOTE]
====
Keep in mind that I'm doing all these experiments on my laptop, so you can undoubtedly achieve better results if you use a cluster and don't have the load-testing tool running on the same machine.
But for my purposes of doing something quick and dirty to understand how these indexes help with query performance, this setup does the job.
====
After installing Locust:
[source, bash]
----
pip install locust
----
I created a `locustfile.py` that looked like this:
.locustfile.py
[source, python]
----
from locust import FastHttpUser, task
import requests
import random
query1 = """
select country, sum(timeSpent) AS totalTime
from webtraffic
group by country
order by totalTime DESC
limit 10
"""
query2 = """
select browserType, count(*)
from webtraffic
WHERE country IN ('Germany', 'United Kingdom', 'Spain')
GROUP BY browserType
limit 10
"""
class PinotUser(FastHttpUser):
@task
def run_q1(self):
with super().rest("POST", "/query/sql", json={"sql": query3}, name="Web Traffic (StarTree)") as r:
if r.status_code == requests.codes.ok:
# print("/query/sql - q1" + ': success (200)')
pass
elif r.status_code == 0:
print("/query/sql - q1" + ': success (0)')
r.success()
else:
print("/query/sql - q1" + ': failure (' + str(r.status_code) + ')')
r.failure(r.status_code)
----
I then ran the load generator configured to simulate a single user executing the query lots of times:
[source, bash]
----
locust --host http://localhost:8099 \
--autostart \
-u 1 \ # <1>
--run-time 1m \
--autoquit 5 # <2>
----
<1> Simulate having 1 user.
<2> Exit 5 seconds after the test has been completed.
I ran this script 3 times for each query, manually updating the script to run each query against each table.
I then collected the results that were printed to the console into the tables shown below:
.Aggregating time spent by country
[options="header", cols="2,1,1,1,1,1,1"]
|===
| Name | # reqs| Avg | Min | Max | Med | req/s
| Web Traffic | 223 | 266 | 242 | 547 | 250 | 3.74
| Web Traffic (Inverted) | 187 | 317 | 271 | 859 | 300 | 3.14
| Web Traffic (StarTree) | 610 | 97 | 56 | 223 | 98 | 10.19
|===
I asked ChatGPT to create a box and whisker plot of this data, which is shown below:
.Box and whisker chart - aggregate time spent by country query
image::{{<siteurl>}}/uploads/2023/07/box_whisker_plot_web_traffic.png[]
.Filtering by country and aggregating by count
[options="header", cols="2,1,1,1,1,1,1"]
|===
| Name | # reqs | Avg | Min | Max | Med | req/s
| Web Traffic Filter | 501 | 118 | 114 | 706 | 120 | 8.38
| Web Traffic Filter (Inverted) | 2741 | 20 | 18 | 766 | 20 | 45.79
| Web Traffic Filter (StarTree) | 7288 | 7 | 6 | 158 | 7 | 121.73
|===
.Box and whisker chart - aggregate time spent by country query
image::{{<siteurl>}}/uploads/2023/07/box_whisker_plot_filter.png[]
From these results, we can see that the StarTree index does best on both queries, but there's not much in it on the filtering query.
I still haven't quite worked out how many records you need in the aggregation step to see a noticeable improvement compared to doing normal aggregation.
A fun experiment though and I'll have to do some more of these! | In this post, we'll experiment with Apache Pinot's StarTree index. | uploads/2023/07/pinot-startree-banner.png | [
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] | apache-pinot-experimenting-with-startree-index | https://markhneedham.com/blog/2023/07/28/apache-pinot-experimenting-with-startree-index |
false | 2023-07-17 04:44:37 | Puppeteer: Button click doesn't work when zoomed in | [
"puppeteer",
"javascript",
"til"
] | [
"TIL"
] | :icons: font
I'm still playing around with https://pptr.dev/[Puppeteer^], a Nodejs library that provides an API to control Chrome/Chromium.
I want to load the Pinot UI zoomed to 250% and then write and run some queries.
We can install Puppeteer by running the following command:
[source, bash]
----
npm i puppeteer-core
----
I then created the file `drive_pinot.mjs` and added the following code, which opens the Pinot query console and then clicks on the 'Run Query' button:
.drive_pinot.mjs
[source, javascript]
----
import puppeteer from 'puppeteer-core';
async function run() {
const browser = await puppeteer.launch({
headless: false,
executablePath: '/Applications/Google Chrome.app/Contents/MacOS/Google Chrome',
ignoreDefaultArgs: ['--enable-automation'],
defaultViewport: null,
args: ['--start-maximized', '--disable-infobars',],
});
const page = await browser.newPage();
await page.goto('http://localhost:9000/#/query');
const [initPage] = await browser.pages();
initPage.close()
const runQueryButton = await page.$x("//button[contains(., 'Run Query')]");
await runQueryButton[0].click() // <1>
await new Promise(r => setTimeout(r, 1000));
await browser.close();
}
run();
----
<1> Click on the run query button
This works completely fine although there isn't a useful result as we haven't written a query yet.
Next, I tried zooming the page to 250% after navigating to the Pinot query console:
[source, javascript]
----
import puppeteer from 'puppeteer-core';
async function run() {
const browser = await puppeteer.launch({
headless: false,
executablePath: '/Applications/Google Chrome.app/Contents/MacOS/Google Chrome',
ignoreDefaultArgs: ['--enable-automation'],
defaultViewport: null,
args: ['--start-maximized', '--disable-infobars',],
});
const page = await browser.newPage();
await page.goto('http://localhost:9000/#/query');
await page.evaluate(() => document.body.style.zoom = "250%" ); // <1>
const [initPage] = await browser.pages();
initPage.close()
const runQueryButton = await page.$x("//button[contains(., 'Run Query')]");
await runQueryButton[0].click()
await new Promise(r => setTimeout(r, 1000));
await browser.close();
}
run();
----
<.> Zoom the page several times
But this time when it clicked the 'Run Query' button nothing happened.
I came across a https://stackoverflow.com/questions/49979069/puppeteer-element-click-not-working-and-not-throwing-an-error/50032302#50032302[StackOverflow thread^] that suggested the issue might be that the location of the button had moved because of the zoom.
I'm not entirely sure that would be the case when I selected the button after zooming the page, but I thought I'd give the solution a try:
[source, javascript]
----
import puppeteer from 'puppeteer-core';
async function run() {
const browser = await puppeteer.launch({
headless: false,
executablePath: '/Applications/Google Chrome.app/Contents/MacOS/Google Chrome',
ignoreDefaultArgs: ['--enable-automation'],
defaultViewport: null,
args: ['--start-maximized', '--disable-infobars',],
});
const page = await browser.newPage();
await page.goto('http://localhost:9000/#/query');
await page.evaluate(() => document.body.style.zoom = "250%" );
const [initPage] = await browser.pages();
initPage.close()
const runQueryButton = await page.$x("//button[contains(., 'Run Query')]");
await page.evaluate((el) => { // <1>
el.click();
}, runQueryButton[0]);
await new Promise(r => setTimeout(r, 1000));
await browser.close();
}
run();
----
<.> Click the button inside `page.evaluate`
And now the button happily clicks at 250% zoom! | In this post, we're going to learn how to solve the problem of clicking a button when the browser is zoomed using Puppeteer. | uploads/2023/07/puppeteer-button-click-banner.png | [
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] | puppeteer-button-click-not-working-after-zoom | https://markhneedham.com/blog/2023/07/17/puppeteer-button-click-not-working-after-zoom |
false | 2023-07-19 04:44:37 | Plotly: Visualising a normal distribution given average and standard deviation | [
"plotly",
"javascript",
"til"
] | [
"TIL"
] | :icons: font
I've been playing around with Microsoft's https://trueskill.org/[TrueSkill^] algorithm, which attempts to quantify the skill of a player using the Bayesian inference algorithm.
A rating in this system is a Gaussian distribution that starts with an average of 25 and a confidence of 8.333.
I wanted to visualise various ratings using Plotly and that's what we'll be doing in this blog post.
To save you from having to install TrueSkill, we're going to create a named tuple to simulate a TrueSkill `Rating` object:
[source, python]
----
from collections import namedtuple
Rating = namedtuple('Rating', ['mu', 'sigma'])
base = Rating(25, 25/3)
----
If we print that object, we'll see the following output:
.Output
[source, text]
----
Rating(mu=25, sigma=8.333333333333334)
----
To create a visualisation of this distribution, we'll need to install the following libraries:
[source, bash]
----
pip install plotly numpy scipy kaleido==0.2.1
----
Next, import the following libraries:
[source, python]
----
import numpy as np
from scipy.stats import norm
import plotly.graph_objects as go
----
Next, we're going to create some values for the x-axis.
We'll use numpy's https://numpy.org/doc/stable/reference/generated/numpy.arange.html[`arange`^] function to create a series of values starting from -4 standard deviations to +4 standard deviations:
[source, python]
----
x = np.arange(base.mu-4*base.sigma, base.mu+4*base.sigma, 0.001)
----
.Output
[source, text]
----
array([-8.33333333, -8.33233333, -8.33133333, ..., 58.33066667,
58.33166667, 58.33266667])
----
Next, we're going to run scipy's probability density function over the x values for our mu and sigma values.
In other words, we're going to compute the likelihood of each of those x values for our normal distribution:
[source, python]
----
y = norm.pdf(x, base.mu, base.sigma)
----
.Output
[source, text]
----
array([1.60596271e-05, 1.60673374e-05, 1.60750513e-05, ...,
1.60801958e-05, 1.60724796e-05, 1.60647669e-05]
----
Now that we've got x and y values, we can create a visualisation by running the following code:
[source, python]
----
fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y, mode='lines', fill='tozeroy', line_color='black'))
fig.write_image("fig1.png", width=1000, height=800)
----
If we run that code, the following image will be generated:
.Normal distribution with mu=25, sigma=8.33
image::{{<siteurl>}}/uploads/2023/07/fig1.png[]
We can then wrap that all together into a function that can take in multiple ratings:
[source, python]
----
def visualise_distribution(**kwargs):
fig = go.Figure()
min_x = min([r.mu-4*r.sigma for r in kwargs.values()])
max_x = max([r.mu+4*r.sigma for r in kwargs.values()])
x = np.arange(min_x, max_x, 0.001)
for key, value in kwargs.items():
y = norm.pdf(x, value.mu, value.sigma)
fig.add_trace(go.Scatter(x=x, y=y, mode='lines', fill='tozeroy', name=key))
fig.write_image(f"fig_{'_'.join(kwargs.keys())}.png", width=1000, height=800)
----
Let's give it a try with 3 different ratings:
[source, python]
----
visualise_distribution(p1=Rating(25, 8.333), p2=Rating(50, 4.12), p3=Rating(10, 5.7))
----
We can see the resulting image below:
.A visualisation of multiple normal distributions
image::{{<siteurl>}}/uploads/2023/07/fig_p1_p2_p3.png[]
We can see that the curve for `p2` is much narrower than the other two, which is because we used a small sigma value.
`p1` is the default score and as a result we have the most uncertainty in that rating.
`p3` hasn't performed well and there's reasonably certainty around their low score. | In this post, we're going to learn how to visualise normal distribution curves using Plotly. | uploads/2023/07/plotly-banner.png | [
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] | plotly-normal-distribution-average-stdev | https://markhneedham.com/blog/2023/07/19/plotly-normal-distribution-average-stdev |
false | 2023-07-26 04:44:37 | How to delete a Kafka topic | [
"kafka",
"redpanda",
"til",
"docker"
] | [
"TIL"
] | A few years ago I wrote a blog post showing https://www.markhneedham.com/blog/2019/05/23/deleting-kafka-topics-on-docker/[how to delete a Kafka topic when running on Docker^] and while that approach still works, I think I've now got a better way.
And that's what we're going to learn about in this blog post.
== Spin up Kafka Cluster
We're going to spin up Kafka using the following Docker Compose file:
.docker-compose.yml
[source]
----
version: "3"
services:
zookeeper:
image: zookeeper:3.8.0
hostname: zookeeper
container_name: zookeeper-delete
ports:
- "2181:2181"
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ZOOKEEPER_TICK_TIME: 2000
kafka:
image: wurstmeister/kafka:latest
restart: unless-stopped
container_name: "kafka-delete"
ports:
- "9092:9092"
expose:
- "9093"
depends_on:
- zookeeper
environment:
KAFKA_ZOOKEEPER_CONNECT: zookeeper-delete:2181/kafka
KAFKA_BROKER_ID: 0
KAFKA_ADVERTISED_HOST_NAME: kafka-delete
KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka-delete:9093,OUTSIDE://localhost:9092
KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9093,OUTSIDE://0.0.0.0:9092
KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,OUTSIDE:PLAINTEXT
----
A `docker compose up` later and we're ready to go.
== Installing Redpanda Keeper
As with the other post, we want to create a topic called `blog-dummy`, but this time we're going to use a command line tool called https://docs.redpanda.com/docs/reference/rpk/[Redpanda Keeper^] or `rpk` for short.
The name of the tool might imply that it only works with https://redpanda.com/[Redpanda^], but I was pleased to learn that it also works with Kafka as well.
`rpk` can be https://docs.redpanda.com/docs/get-started/rpk-install/[installed in a variety of ways^] depending on your operating system, but I'm going to use brew since I'm using a Mac.
The following command does the trick:
[source, bash]
----
brew install redpanda-data/tap/redpanda
----
We can then check the version that we've installed:
[source, bash]
----
rpk version
----
When I ran this initially I got the following version:
.Output
[source, text]
----
v23.1.8 (rev e8cf768bd)
----
Which is a little bit behind, but we can just re-run the installation command and it will update to the latest version:
.Output
[source, text]
----
v23.2.2 (rev 3aae933c6)
----
== Creating a topic
Now that we've done that, let's create the topic:
[source, bash]
----
rpk topic create blog-dummy -p5
----
This creates a topic with 5 partitions and if we run the command, we'll see the following output:
.Output
[source, text]
----
TOPIC STATUS
blog-dummy OK
----
We can list all the topics in Kafka using the following command:
[source, bash]
----
rpk topic list
----
.Output
[source, text]
----
NAME PARTITIONS REPLICAS
blog-dummy 5 1
----
There's our topic!
== Describing a topic
We can also describe the topic:
[source, bash]
----
rpk topic describe blog-dummy
----
.Output
[source, text]
----
SUMMARY
=======
NAME blog-dummy
PARTITIONS 5
REPLICAS 1
CONFIGS
=======
KEY VALUE SOURCE
cleanup.policy delete DEFAULT_CONFIG
compression.type producer DEFAULT_CONFIG
delete.retention.ms 86400000 DEFAULT_CONFIG
file.delete.delay.ms 60000 DEFAULT_CONFIG
flush.messages 9223372036854775807 DEFAULT_CONFIG
flush.ms 9223372036854775807 DEFAULT_CONFIG
follower.replication.throttled.replicas DEFAULT_CONFIG
index.interval.bytes 4096 DEFAULT_CONFIG
leader.replication.throttled.replicas DEFAULT_CONFIG
max.compaction.lag.ms 9223372036854775807 DEFAULT_CONFIG
max.message.bytes 1048588 DEFAULT_CONFIG
message.downconversion.enable true DEFAULT_CONFIG
message.format.version 2.8-IV1 DEFAULT_CONFIG
message.timestamp.difference.max.ms 9223372036854775807 DEFAULT_CONFIG
message.timestamp.type CreateTime DEFAULT_CONFIG
min.cleanable.dirty.ratio 0.5 DEFAULT_CONFIG
min.compaction.lag.ms 0 DEFAULT_CONFIG
min.insync.replicas 1 DEFAULT_CONFIG
preallocate false DEFAULT_CONFIG
retention.bytes -1 DEFAULT_CONFIG
retention.ms 604800000 DEFAULT_CONFIG
segment.bytes 1073741824 STATIC_BROKER_CONFIG
segment.index.bytes 10485760 DEFAULT_CONFIG
segment.jitter.ms 0 DEFAULT_CONFIG
segment.ms 604800000 DEFAULT_CONFIG
unclean.leader.election.enable false DEFAULT_CONFIG
----
There's a lot of information going on here, but I'm mostly interested in the offsets for each of our partitions.
We can return that data by passing in the `-p` flag:
[source, bash]
----
rpk topic describe blog-dummy -p
----
.Output
[source, text]
----
PARTITION LEADER EPOCH REPLICAS LOG-START-OFFSET HIGH-WATERMARK
0 0 0 [0] 0 0
1 0 0 [0] 0 0
2 0 0 [0] 0 0
3 0 0 [0] 0 0
4 0 0 [0] 0 0
----
We have no data, which is pretty much what we'd expect.
== Ingesting some records
Let's put some dummy data in, just for fun:
[source, bash]
----
for ((i=1; i<=10; i++))
do
echo '{"name": "Mark"}' | kcat -P -b localhost:9092 -t blog-dummy
done
----
Let's check on the partition offsets again:
[source, bash]
----
rpk topic describe blog-dummy -p
----
.Output
[source, text]
----
PARTITION LEADER EPOCH REPLICAS LOG-START-OFFSET HIGH-WATERMARK
0 0 0 [0] 0 0
1 0 0 [0] 0 4
2 0 0 [0] 0 1
3 0 0 [0] 0 2
4 0 0 [0] 0 3
----
We have data!
== Deleting a topic
It's time to delete that topic, which we can do with the following command:
[source, bash]
----
rpk topic delete blog-dummy
----
.Output
[source, text]
----
TOPIC STATUS
blog-dummy OK
----
And now if we list all topics:
[source, bash]
----
rpk topic list blog-dummy
----
.Output
[source, text]
----
NAME PARTITIONS REPLICAS
----
It's gone! | In this post, we're going to learn how to delete a Kafka with the Redpanda Keeper CLI tool. | uploads/2023/07/kafka-delete-topic-banner.png | [
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] | how-to-delete-kafka-topic | https://markhneedham.com/blog/2023/07/26/how-to-delete-kafka-topic |