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false | 2013-03-02 22:19:11 | Ruby/Haml: Maintaining white space/indentation in a <pre> tag | [
"ruby",
"haml"
] | [
"Ruby"
] | I've been writing a little web app in which I wanted to display cypher queries inside a
....
tag which was then prettified using SyntaxHighlighter but I was having problems with how code on new lines was being displayed.</p>
I had the following Haml code to display a query looking up Gareth Bale in a graph:
~~~haml
%pre{ :class => "brush: cypher; gutter: false; toolbar: false;"}
START player = node:players('name:"Gareth Bale"')
RETURN player.name
~~~
When I rendered the page it looked like this:
After a bit of googling I ended up on this Stack Overflow post which described the preserve helper which seems to do the job:
~~~haml
%pre{ :class => "brush: cypher; gutter: false; toolbar: false;"}
= preserve do
START player = node:players('name:"Gareth Bale"')
RETURN player.name
~~~
That part of the page now looks much better:
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] | rubyhaml-maintaining-white-spaceindentation-in-a-pre-tag | https://markhneedham.com/blog/2013/03/02/rubyhaml-maintaining-white-spaceindentation-in-a-pre-tag |
false | 2013-03-02 23:22:50 | Ruby/Haml: Conditionally/Optionally setting an attribute/class | [
"ruby",
"haml"
] | [
"Ruby"
] | One of the things that we want to do reasonably frequently is set an attribute (most often a class) on a HTML element depending on the value of a variable.
I always forget how to do this in Haml so I thought I better write it down so I'll remember next time!
Let's say we want to add a +++<cite>+++success+++</cite>+++ class to a paragraph if the variable +++<cite>+++correct+++</cite>+++ is true and not have any value if it's false.
The following code does what we want:
[source,haml]
----
- correct = true
%p{:class => (correct ? "success" : nil) }
important text
----
This generates the following HTML is +++<cite>+++correct+++</cite>+++ is true:
[source,html4strict]
----
<p class="success">
important text
</p>
----
And the following HTML if it's false
[source,html4strict]
----
<p>
important text
</p>
----
To summarise, http://stackoverflow.com/questions/3841116/conditionally-set-html-element-id-with-haml[if we set an attribute to nil in Haml it just won't be rendered at all] which is exactly what we want in this situation. | null | null | [
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] | rubyhaml-conditionallyoptionally-setting-an-attributeclass | https://markhneedham.com/blog/2013/03/02/rubyhaml-conditionallyoptionally-setting-an-attributeclass |
false | 2013-03-20 00:25:00 | neo4j/cypher: Getting the hang of the WITH statement | [
"neo4j",
"cypher"
] | [
"neo4j"
] | I wrote a post a few weeks ago http://www.markhneedham.com/blog/2013/02/24/neo4jcypher-combining-count-and-collect-in-one-query/[showing an example of a cypher query which made use of the WITH statement] but I still don't completely understand how it works so I thought I'd write some more queries that use it.
I wanted to find out whether Luis Suárez has a better scoring record depending on which day a match is played on.
We start out by finding all the matches that he's played in and which days those matches were on:
[source,cypher]
----
START player = node:players('name:"Luis Suárez"')
MATCH game-[:in]-stats-[:played]-player, game-[:on_day]-day
RETURN day.name, game.name
----
[source,text]
----
+---------------------------------------------------+
| day.name | game.name |
+---------------------------------------------------+
| "Saturday" | "Liverpool vs Southampton" |
| "Saturday" | "Southampton vs Liverpool" |
| "Saturday" | "Liverpool vs Reading" |
| "Saturday" | "West Bromwich Albion vs Liverpool" |
...
+---------------------------------------------------+
29 rows
----
We can then group those matches by day to find out how many games he played in on a particular day:
[source,cypher]
----
START player = node:players('name:"Luis Suárez"')
MATCH game-[:in]-stats-[:played]-player, game-[:on_day]-day
RETURN day.name, COUNT(game.name)
----
[source,text]
----
+--------------------------------+
| day.name | COUNT(game.name) |
+--------------------------------+
| "Sunday" | 13 |
| "Wednesday" | 4 |
| "Monday" | 1 |
| "Saturday" | 11 |
+--------------------------------+
4 rows
----
Now we want to find out which days the games that Suarez scored in were on so we start out by returning each day that Suarez played in a match and then return a collection containing information about the games he played on that day and whether he scored:
[source,cypher]
----
START player = node:players('name:"Luis Suárez"')
MATCH game-[:in]-stats-[:played]-player-[r?:scored_in]-game-[:on_day]-day
RETURN day, COLLECT(DISTINCT([type(r), game.name])) AS games
----
[source,text]
----
+----------------------------------------------------------------------------------------+
| day.name | games |
+----------------------------------------------------------------------------------------+
| "Sunday" | [["scored_in","Liverpool vs Manchester City"]…] |
| "Wednesday" | [[<null>,"Tottenham Hotspur vs Liverpool"],[<null>,"Stoke City vs Liverpool"]...] |
| "Monday" | [[<null>,"Liverpool vs West Bromwich Albion"]] |
| "Saturday" | [[<null>,"Liverpool vs Southampton"]…] |
+----------------------------------------------------------------------------------------+
4 rows
----
This query has got a little bit more complicated than our previous ones because we wanted to return all the days that Suarez played matches on even if he didn't score on that day.
The only interesting thing in the first couple of lines is that we match the 'scored_in' relationship http://www.markhneedham.com/blog/2012/06/24/neo4j-handling-optional-relationships/[optionally] so that we can handle the situation where Suarez didn't score while still returning a row.
On the third line we return the day and then we get a collection of tuples of the 'scored_in' relationship and the corresponding game.
We use +++<cite>+++DISTINCT+++</cite>+++ on this line to take care of the situation where Suarez scored multiple times in the same match. We're working out the number of games that Suarez scored in so counting multiple goals in the same match would ruin that count.
We then need to tweak that query slightly to get a count of the matches that Suarez scored in rather than just returning them. We end up with the following:
[source,cypher]
----
START player = node:players('name:"Luis Suárez"')
MATCH game-[:in]-stats-[:played]-player-[r?:scored_in]-game-[:on_day]-day
WITH day, COLLECT(DISTINCT([type(r), game.name])) AS games
RETURN day.name, REDUCE(totalGames = 0, game in FILTER(x in games : head(x) = "scored_in"): totalGames + 1) AS gamesScoredIn
----
[source,text]
----
+-----------------------------+
| day.name | gamesScoredIn |
+-----------------------------+
| "Wednesday" | 2 |
| "Saturday" | 6 |
| "Sunday" | 7 |
| "Monday" | 0 |
+-----------------------------+
4 rows
----
We start off by http://docs.neo4j.org/chunked/milestone/query-function.html#functions-filter[filtering] the games so that we only keep the ones that Suarez scored in. We then run a http://docs.neo4j.org/chunked/milestone/query-function.html#functions-reduce[+++<cite>+++REDUCE+++</cite>+++] over the resulting collection which just adds 1 to an accumulator for each record in the collection.
Now that we've got that the next step is to combine our games played and games scored in queries together so that we can see what % of games Suarez scores in on each day.
We end up with the following:
[source,cypher]
----
START player = node:players('name:"Luis Suárez"')
MATCH game-[:in]-stats-[:played]-player, game-[:on_day]-day
WITH player, day, COUNT(game) AS playedGames
MATCH game-[:in]-stats-[:played]-player-[r?:scored_in]-game-[:on_day]-day
WITH day, COLLECT(DISTINCT([type(r), game.name])) AS games, playedGames
WITH day, REDUCE(totalGames = 0, game in FILTER(x in games : head(x) = "scored_in"): totalGames + 1) AS scoredGames, playedGames
RETURN day.name, playedGames, scoredGames, (scoredGames*1.0/playedGames*1.0) * 100 AS percentage
----
[source,text]
----
+-------------------------------------------------------------+
| day.name | playedGames | scoredGames | percentage |
+-------------------------------------------------------------+
| "Saturday" | 11 | 6 | 54.54545454545454 |
| "Monday" | 1 | 0 | 0.0 |
| "Wednesday" | 4 | 2 | 50.0 |
| "Sunday" | 13 | 7 | 53.84615384615385 |
+-------------------------------------------------------------+
4 rows
----
One thing I kept getting confused about here is that we need to pass everything that we want to eventually return in each WITH statement otherwise it won't be available to us at the end.
If we're going to do multiple MATCH statements we need to pass the starting node in the preceding WITH statement which in this case means that we need to pass the player variable along.
Other than that this query is the amalgamation of the previous two except we've added some arithmetic on the final line to work out the % of matches that Suarez scores in. I had to multiply each number by 1.0 to force float based arithmetic rather than integer based.
In answer to our initial question it doesn't seem to matter which day a match is played on, Suarez scores in approximately every other game. | null | null | [
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] | neo4jcypher-getting-the-hang-of-the-with-statement | https://markhneedham.com/blog/2013/03/20/neo4jcypher-getting-the-hang-of-the-with-statement |
false | 2013-03-20 02:54:43 | neo4j/cypher: WITH, COLLECT & EXTRACT | [
"neo4j",
"cypher"
] | [
"neo4j"
] | As I http://www.markhneedham.com/blog/2013/03/20/neo4jcypher-getting-the-hang-of-the-with-statement/[mentioned in my last post] I'm trying to get the hang of the +++<cite>+++http://docs.neo4j.org/chunked/milestone/query-with.html[WITH]+++</cite>+++ statement in neo4j's http://docs.neo4j.org/chunked/milestone/cypher-query-lang.html[cypher] query language and I found another application when trying to work out which opponents teams played on certain days.
I started out with a query which grouped the data set by day and showed the opponents that were played on that day:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH team-[h:home_team|away_team]-game-[:on_day]-day
RETURN DISTINCT day.name, COLLECT(TRIM(REPLACE(REPLACE(game.name, "Manchester United", ""), "vs", "")))
----
[source,text]
----
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| day.name | opponents |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "Sunday" | ["Liverpool","Everton","Southampton","Liverpool","Newcastle United","Chelsea","Manchester City","Swansea City","Tottenham Hotspur"] |
| "Wednesday" | ["Southampton","West Ham United","Newcastle United"] |
| "Monday" | ["Everton"] |
| "Saturday" | ["Reading","Fulham","Wigan Athletic","Tottenham Hotspur","Stoke City","Arsenal","Queens Park Rangers","Sunderland","West Bromwich Albion","Norwich City","Reading","Aston Villa","Norwich City","Fulham","Queens Park Rangers"] |
| "Tuesday" | ["Wigan Athletic"] |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
5 rows
----
The way we've got the opponents is a bit of a hack - the name of the two teams is in the 'name' property of a game node and we've removed 'Manchester United' and the word 'vs' to get the opponent's name.
I thought it'd be cool if we could separate the games on each day based on whether Manchester United were playing at home or away.
With https://groups.google.com/forum/?fromgroups=#!topic/neo4j/D4M1gXKwQ3U[a lot of help from Wes Freeman] we ended up with the following query which does the job:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH team-[h:home_team|away_team]-game-[:on_day]-day
WITH day.name as d, game, team, h
MATCH team-[:home_team|away_team]-game-[:home_team|away_team]-opp
WITH d, COLLECT([type(h),opp.name]) AS games
RETURN d,
EXTRACT(c in FILTER(x in games: HEAD(x) = "home_team") : HEAD(TAIL(c))) AS home,
EXTRACT(c in FILTER(x in games: HEAD(x) = "away_team") : HEAD(TAIL(c))) AS away
----
We use a similar approach with COLLECT as in the http://www.markhneedham.com/blog/2013/03/20/neo4jcypher-getting-the-hang-of-the-with-statement/[previous post] whereby we have a collection of tuples describing whether Manchester United were at home or not and who they were playing.
A neat thing that Wes pointed out is that since there are only 2 teams per game we're able to get the opponent node easily because it's the only other node that can match the 'home_team|away_team" relationship since we've already matched our team.
If we run the query just up to the last WITH we get the following result:
[source,text]
----
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| d | games |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "Sunday" | [["home_team","Liverpool"],["home_team","Everton"],["away_team","Southampton"],["away_team","Liverpool"],["away_team","Newcastle United"],["away_team","Chelsea"],["away_team","Manchester City"],["away_team","Swansea City"],["away_team","Tottenham Hotspur"]] |
| "Wednesday" | [["home_team","Southampton"],["home_team","West Ham United"],["home_team","Newcastle United"]] |
| "Monday" | [["away_team","Everton"]] |
| "Saturday" | [["home_team","Reading"],["home_team","Fulham"],["home_team","Wigan Athletic"],["home_team","Tottenham Hotspur"],["home_team","Stoke City"],["home_team","Arsenal"],["home_team","Queens Park Rangers"],["home_team","Sunderland"],["home_team","West Bromwich Albion"],["home_team","Norwich City"],["away_team","Reading"],["away_team","Aston Villa"],["away_team","Norwich City"],["away_team","Fulham"],["away_team","Queens Park Rangers"]] |
| "Tuesday" | [["away_team","Wigan Athletic"]] |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
5 rows
----
We then use the http://docs.neo4j.org/chunked/milestone/query-function.html#functions-filter[FILTER] function to choose either the opponents Manchester United played at home or away and then we use the http://docs.neo4j.org/chunked/milestone/query-function.html#functions-extract[EXTRACT] function to get the opponent from the tuple:
[source,text]
----
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| d | home |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "Sunday" | ["Liverpool","Everton"] |
| "Wednesday" | ["Southampton","West Ham United","Newcastle United"] |
| "Monday" | [] |
| "Saturday" | ["Reading","Fulham","Wigan Athletic","Tottenham Hotspur","Stoke City","Arsenal","Queens Park Rangers","Sunderland","West Bromwich Albion","Norwich City"] |
| "Tuesday" | [] |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
5 rows
+-----------------------------------------------------------------------------------------------------------------------------+
| d | away |
+-----------------------------------------------------------------------------------------------------------------------------+
| "Sunday" | ["Southampton","Liverpool","Newcastle United","Chelsea","Manchester City","Swansea City","Tottenham Hotspur"] |
| "Wednesday" | [] |
| "Monday" | ["Everton"] |
| "Saturday" | ["Reading","Aston Villa","Norwich City","Fulham","Queens Park Rangers"] |
| "Tuesday" | ["Wigan Athletic"] |
+-----------------------------------------------------------------------------------------------------------------------------+
5 rows
----
_(I ran the query twice alternating between the last two lines so that it's readable here. In actual fact the away teams would be in a column next to the home teams)_
I thought it was quite interesting how many games Manchester United play away on a Sunday - I think all of those games were probably televised so I thought they'd be more evenly split between home and away matches. Adding televised matches is perhaps http://www.markhneedham.com/blog/2012/07/21/neo4j-embracing-the-sub-graph/[another layer to add to the graph].
It's probably more useful to summarise how many games were played on each day at home and away rather than who they're against and we can use the +++<cite>+++REDUCE+++</cite>+++ function to do this:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH team-[h:home_team|away_team]-game-[:on_day]-day
WITH day.name as dayName, game, team, h
MATCH team-[:home_team|away_team]-game-[:home_team|away_team]-opp
WITH dayName, COLLECT([type(h),opp.name]) AS games
RETURN dayName,
REDUCE(homeGames=0, game in EXTRACT(c in FILTER(x in games: head(x) = "home_team") : HEAD(TAIL(c))) : homeGames + 1) as home,
REDUCE(awayGames=0, game in EXTRACT(c in FILTER(x in games: head(x) = "away_team") : HEAD(TAIL(c))) : awayGames + 1) as away,
REDUCE(totalGames=0, game in games : totalGames + 1) as total
----
[source,text]
----
+-----------------------------------+
| dayName | home | away | total |
+-----------------------------------+
| "Sunday" | 2 | 7 | 9 |
| "Wednesday" | 3 | 0 | 3 |
| "Monday" | 0 | 1 | 1 |
| "Saturday" | 10 | 5 | 15 |
| "Tuesday" | 0 | 1 | 1 |
+-----------------------------------+
5 rows
----
An alternative way of writing the initial query would be the following which Michael Hunger suggested on the thread:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH p=team-[:home_team|away_team]-game-[:home_team|away_team]-(), game-[:on_day]-day
WITH day.name as dayName, COLLECT([LAST(p), HEAD(RELS(p))]) AS opponents
WITH dayName,
EXTRACT(y in FILTER(x in opponents: TYPE(HEAD(TAIL(x))) = "home_team") : HEAD(y)) AS home,
EXTRACT(y in FILTER(x in opponents : TYPE(HEAD(TAIL(x))) = "away_team") : HEAD(y)) AS away
RETURN dayName,
EXTRACT(team in home: team.name) AS homeOpponents,
EXTRACT(team in away: team.name) AS awayOpponents
ORDER BY dayName
----
Here we take a slightly different approach where we make use of functions that we can apply to a matching path. We create a collection of tuples where +++<cite>+++LAST(p)+++</cite>+++ matches the opponent node and +++<cite>+++HEAD(RELS(p))+++</cite>+++ matches the 'home_team' or 'away_team' relationship accordingly.
We then filter the collection to find the times that we played at home and away. This is done by taking the second value from the tuple and then calling +++<cite>+++TYPE+++</cite>+++ on it which either returns 'home_team' or 'away_team'. We then extract the first value from the tuple which is the opponent node.
In the last part of the query we extract the name from the opponent nodes. | null | null | [
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] | neo4jcypher-with-collect-extract | https://markhneedham.com/blog/2013/03/20/neo4jcypher-with-collect-extract |
false | 2013-03-18 23:19:36 | neo4j/cypher: SQL style GROUP BY WITH LIMIT query | [
"neo4j",
"cypher"
] | [
"neo4j"
] | A few weeks ago I wrote a blog post where I described how we could http://www.markhneedham.com/blog/2013/02/17/neo4jcypher-sql-style-group-by-functionality/[construct a SQL GROUP BY style query] in http://docs.neo4j.org/chunked/milestone/cypher-query-lang.html[cypher] and last week I wanted to write a similar query but with what I think would be a LIMIT clause in SQL.
I wanted to find the maximum number of goals that players had scored in a match for a specific team and started off with the following query to find all the matches that players had scored in:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH team-[:home_team|away_team]-game-[:scored_in]-player-[:played]-stats-[:for]-team,
stats-[:in]-game
RETURN DISTINCT player.name, stats.goals, game.name
----
We find all the matches where Manchester United were playing and then get a list of the players who scored for them in those games:
[source,text]
----
+--------------------------------------------------------------------------------+
| player.name | stats.goals | game.name |
+--------------------------------------------------------------------------------+
| "Javier Hernández" | 1 | "Manchester United vs Wigan Athletic" |
| "Robin Van Persie" | 1 | "Manchester United vs Sunderland" |
| "Danny Welbeck" | 1 | "Manchester United vs Stoke City" |
| "Rafael" | 1 | "Queens Park Rangers vs Manchester United" |
| "Wayne Rooney" | 1 | "Manchester United vs Norwich City" |
| "Shinji Kagawa" | 1 | "Manchester United vs Fulham" |
| "Shinji Kagawa" | 3 | "Manchester United vs Norwich City" |
...
+--------------------------------------------------------------------------------+
50 rows
----
Our next step would be to only return the unique combinations of players and goals:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH team-[:home_team|away_team]-game-[:scored_in]-player-[:played]-stats-[:for]-team,
stats-[:in]-game
RETURN DISTINCT player.name, stats.goals
----
[source,text]
----
+-----------------------------------+
| player.name | stats.goals |
+-----------------------------------+
| "Nemanja Vidic" | 1 |
| "Shinji Kagawa" | 1 |
| "Danny Welbeck" | 1 |
| "Darren Fletcher" | 1 |
| "Wayne Rooney" | 2 |
| "Javier Hernández" | 1 |
| "Nani" | 1 |
| "Tom Cleverley" | 1 |
| "Robin Van Persie" | 2 |
| "Shinji Kagawa" | 3 |
...
| "Robin Van Persie" | 3 |
+-----------------------------------+
21 rows
----
Since we only want to return each player once along with their maximum value from the 'stats.goals' column all we have to do now is make use of the +++<cite>+++http://docs.neo4j.org/chunked/milestone/query-aggregation.html#aggregation-max[MAX]+++</cite>+++ function:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH team-[:home_team|away_team]-game-[:scored_in]-player-[:played]-stats-[:for]-team,
stats-[:in]-game
RETURN DISTINCT player.name, MAX(stats.goals) AS goals
ORDER BY goals DESC
----
[source,text]
----
+-----------------------------+
| player.name | goals |
+-----------------------------+
| "Robin Van Persie" | 3 |
| "Shinji Kagawa" | 3 |
| "Javier Hernández" | 2 |
| "Wayne Rooney" | 2 |
| "Paul Scholes" | 1 |
| "Ryan Giggs" | 1 |
| "Tom Cleverley" | 1 |
...
| "Rafael" | 1 |
| "Darren Fletcher" | 1 |
| "Nani" | 1 |
+-----------------------------+
16 rows
---- | null | null | [
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] | neo4jcypher-sql-style-group-by-with-limit-query | https://markhneedham.com/blog/2013/03/18/neo4jcypher-sql-style-group-by-with-limit-query |
false | 2013-03-29 10:51:37 | Editing config files on a server & Ctrl-Z | [
"software-development"
] | [
"Software Development"
] | "A couple of weeks ago https://twitter.com/timrgoodwin[Tim] and I were spinning up a new service on (...TRUNCATED) | null | null | [0.020787741988897324,-0.020870553329586983,-0.008831940591335297,0.03842626139521599,0.102838501334(...TRUNCATED) | [-0.10668612271547318,-0.028614232316613197,-0.011191360652446747,-0.04260871931910515,0.06387036293(...TRUNCATED) | [-0.04834061488509178,0.037395745515823364,-0.001488151028752327,0.001364432042464614,0.006659605540(...TRUNCATED) | editing-config-files-on-a-server-ctrl-z | https://markhneedham.com/blog/2013/03/29/editing-config-files-on-a-server-ctrl-z |
false | 2013-03-16 11:58:42 | A quick and dirty way of testing the performance of a service | [
"software-development"
] | [
"Software Development"
] | "We had a power outage in our data centre yesterday and once it had recovered https://twitter.com/ja(...TRUNCATED) | null | null | [0.030247362330555916,-0.01343638077378273,0.01028284803032875,0.045743465423583984,0.05697846785187(...TRUNCATED) | [-0.08546430617570877,-0.04089677333831787,-0.024446990340948105,-0.008896862156689167,0.06583327800(...TRUNCATED) | [-0.009783712215721607,0.06838808208703995,-0.021387062966823578,0.025298725813627243,0.007329901680(...TRUNCATED) | a-quick-and-dirty-way-of-testing-the-performance-of-a-service | "https://markhneedham.com/blog/2013/03/16/a-quick-and-dirty-way-of-testing-the-performance-of-a-serv(...TRUNCATED) |
false | 2013-03-17 20:21:10 | clojure/Java Interop: The doto macro | [
"java",
"clojure",
"interop"
] | [
"Clojure"
] | "I recently wrote about some http://www.markhneedham.com/blog/2013/03/17/clojurejava-interop-importi(...TRUNCATED) | null | null | [0.002471019048243761,-0.013959275558590889,-0.03309272229671478,-0.005049073603004217,0.07075443118(...TRUNCATED) | [-0.06643670052289963,-0.04220634326338768,-0.009614154696464539,-0.029233591631054878,0.03872268646(...TRUNCATED) | [0.029796916991472244,0.04653238132596016,0.010706035420298576,-0.0004499733913689852,-0.00636274600(...TRUNCATED) | clojurejava-interop-the-doto-macro | https://markhneedham.com/blog/2013/03/17/clojurejava-interop-the-doto-macro |
false | 2013-03-17 18:56:36 | clojure/Java Interop - Importing neo4j spatial data | [
"java",
"clojure",
"neo4j"
] | [
"neo4j"
] | "I wrote a post about a week ago where I described how I'd http://www.markhneedham.com/blog/2013/03/(...TRUNCATED) | null | null | [0.010771064087748528,-0.011268312111496925,-0.02168768085539341,0.020929010584950447,0.083746537566(...TRUNCATED) | [-0.015485635958611965,-0.0224214568734169,0.009397018700838089,-0.05304528772830963,0.0953090637922(...TRUNCATED) | [0.07055427134037018,0.042397722601890564,-0.020967237651348114,-0.005194726400077343,0.014124209992(...TRUNCATED) | clojurejava-interop-importing-neo4j-spatial-data | https://markhneedham.com/blog/2013/03/17/clojurejava-interop-importing-neo4j-spatial-data |
false | 2013-03-17 14:00:35 | Understanding what lsof socket/port aliases refer to | [
"software-development",
"devops-2"
] | [
"DevOps"
] | "Earlier in the week we wanted to check which ports were being listened on and by what processes whi(...TRUNCATED) | null | null | [-0.037673283368349075,-0.003555653151124716,-0.009796075522899628,0.009113416075706482,0.0915163159(...TRUNCATED) | [-0.06968486309051514,-0.055897656828165054,-0.01805156283080578,-0.06910508126020432,0.055130138993(...TRUNCATED) | [0.02144784666597843,-0.002786268014460802,0.013498280197381973,-0.04672441631555557,0.0017769109690(...TRUNCATED) | understanding-what-lsof-socketport-aliases-refer-to | https://markhneedham.com/blog/2013/03/17/understanding-what-lsof-socketport-aliases-refer-to |
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