1.1 million comments from Hacker News: small data full-text / analytics test
Published: 2022-05-30 14:15:27


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/a96oo453hilkrrif8rys.jpeg)

## Intro
In this test we use the data collection of 1.1M [Hacker News](https://news.ycombinator.com/) curated comments with numeric fields from https://zenodo.org/record/45901. In the modern world 1 million of documents can be considered a very small data set which, however, can be typical for many applications: blogs and news sites, online stores, job, automotive and real estate sites and so on. It’s typical for such applications to have:

- not very long textual data in one or multiple fields
- and a number of attributes

## Data collection
The source of the data collection is https://zenodo.org/record/45901.

The record structure is:

```
"properties": {
   "story_id": {"type": "integer"},
   "story_text": {"type": "text"},
   "story_author": {"type": "text", "fields": {"raw": {"type":"keyword"}}},
   "comment_id": {"type": "integer"},
   "comment_text": {"type": "text"},
   "comment_author": {"type": "text", "fields": {"raw": {"type":"keyword"}}},
   "comment_ranking": {"type": "integer"},
   "author_comment_count": {"type": "integer"},
   "story_comment_count": {"type": "integer"}
}
```
Databases
So far we have made this test available for 4 databases:

- [MySQL](https://www.mysql.com/) - “world’s most popular” open source OLTP database,
- [Percona Server for MySQL](https://www.percona.com/software/mysql-database/percona-server) - free, fully compatible, enhanced and open source drop-in replacement for any MySQL database,
- [Clickhouse](https://github.com/ClickHouse/ClickHouse) - a powerful OLAP database,
- [Elasticsearch](https://github.com/elastic/elasticsearch) - general purpose “search and analytics engine”,
- [Manticore Search](https://github.com/manticoresoftware/manticoresearch/) - “database for search”, Elasticsearch alternative.

In this test we make **as little changes to database default settings as possible** to not give either of them an unfair advantage. Testing at max tuning is no less important, but it's a subject for another benchmark. Here we want to understand what latency a regular non-experienced user can get after just installing a database and running it with its default settings. But to make it fair to compare one with another we still had to change a few settings:

- MySQL and Percona Server for MySQL: [no tuning](https://github.com/db-benchmarks/db-benchmarks/blob/main/tests/hn_small/mysql/init) , just C`REATE TABLE ..., FULLTEXT(story_text,story_author,comment_text,comment_author)) `and standard [mysql docker image](https://github.com/db-benchmarks/db-benchmarks/blob/main/docker-compose.yml) .
- Clickhouse: [no tuning](https://github.com/db-benchmarks/db-benchmarks/blob/main/tests/hn/ch/init) , just `CREATE TABLE ... ENGINE = MergeTree() ORDER BY id SETTINGS index_granularity = 8192` and standard [clickhouse-server docker image](https://github.com/db-benchmarks/db-benchmarks/blob/main/docker-compose.yml).
- Elasticsearch: also [no tuning](https://github.com/db-benchmarks/db-benchmarks/tree/main/tests/hn_small/es/logstash) except for `bootstrap.memory_lock=true` since as said on https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html#_disable_swapping it needs to be done for performance. The [docker image](https://github.com/db-benchmarks/db-benchmarks/blob/main/docker-compose.yml) is also from the vendor.
- [Manticore Search](https://github.com/db-benchmarks/db-benchmarks/blob/main/tests/hn/manticore/generate_manticore_config.php):

 - `min_infix_len = 2 `since in Elasticsearch by default you can do infix full-text search and it would be unfair advantage if Manticore was running w/o infixes (that increase response time).
 - and as for the others we use [their own docker image](https://github.com/db-benchmarks/db-benchmarks/blob/main/dockers/manticore) + their columnar library (but it’s not used in this test).

## About caches

We've also configured the databases to not use any internal caches. Why this is important:
1. In this benchmark, we conduct an **accurate latency measurement** to find out what response time users can expect if they run one of the tested queries at a random moment, not after running the same query many times consequently.
2. Any cache is a shortcut to low latency. As written in [Wikipedia](https://en.wikipedia.org/wiki/Cache_(computing)) "cache stores data so that future requests for that data can be served faster". But caches are different, they can be divided into 2 main groups:
   - 👌 those that just cache raw data stored on disk. For example many databases use `mmap()` to map the data stored on disk to memory, access it easily and let the operating system take care about the rest (reading it from disk when there's free memory, removing it from memory when it's needed for something more important etc). This is ok in terms of performance testing, because we let **each** database leverage the benefit of using the OS page cache (or its internal similar cache that just reads data from disk)
     **That's exactly what we do in this benchmark.**
   - ❗ those that are used to save results of previous calculations. And it's fine in many cases, but in terms of this benchmark letting database enable such a cache is a bad idea, because:
     - it breaks proper measuring: instead of measuring calculation time you start measuring how long it takes to find a value by a key in memory. It's not something we want to do in this test (but it's interesting in general and we'll perhaps do it in the future and publish some article "Benchmark of caches").
     - even if they save not a full result of a particular query, but results of its sub-calculations it's not good, because it breaks the idea of the test - "what response time users can expect if they run one of the tested queries at a random moment".
     - some databases have such a cache (it's usually called "query cache"), others don't so if we don't disable database internal caches we'll give an unfair advantage to those having that.

     So we do everything to make sure none of the database does this kind of caching.

What exactly we do to achieve that:

- Clickhouse:
 - `SYSTEM DROP MARK CACHE`, `SYSTEM DROP UNCOMPRESSED CACHE`, `SYSTEM DROP COMPILED EXPRESSION CACHE` [before testing each new query](https://github.com/db-benchmarks/db-benchmarks/blob/main/plugins/clickhouse.php) (not each attempt of the same query).
- Elasticsearch:
 - "index.queries.cache.enabled": false in its configuration
 - /_cache/clear?request=true&query=true&fielddata=true [before testing each new query](https://github.com/db-benchmarks/db-benchmarks/blob/main/plugins/elasticsearch.php)(not each attempt of the same query).
- Manticore Search (in configuration file):
 - `qcache_max_bytes = 0`
 - `docstore_cache_size = 0`
- Operating system:
 - we do `echo 3 > /proc/sys/vm/drop_caches; sync` before each **NEW** query (**NOT** each attempt). I.e. for each new query we:
    - stop database
    - drop OS cache
    - start it back
    - make the very first cold query and measure its time
    - and make tens more attempts (up to 100 or until the coefficient of variation is low enough to consider the test results high quality)

## Queries
The query set consists of both full-text and analytical (filtering, sorting, grouping, aggregating) queries:


```
[
"select count(*) from hn_small",
"select count(*) from hn_small where comment_ranking=100",
"select count(*) from hn_small where comment_ranking=500",
"select count(*) from hn_small where comment_ranking > 300 and comment_ranking < 500",
"select story_author, count(*) from hn_small group by story_author order by count(*) desc limit 20",
"select story_author, avg(comment_ranking) avg from hn_small group by story_author order by avg desc limit 20",
"select comment_ranking, count(*) from hn_small group by comment_ranking order by count(*) desc limit 20",
"select comment_ranking, avg(author_comment_count) avg from hn_small group by comment_ranking order by avg desc, comment_ranking desc limit 20",
"select comment_ranking, avg(author_comment_count+story_comment_count) avg from hn_small group by comment_ranking order by avg desc, comment_ranking desc limit 20",
"select comment_ranking, avg(author_comment_count+story_comment_count) avg from hn_small where comment_ranking < 10 group by comment_ranking order by avg desc, comment_ranking desc limit 20",
{
    "manticoresearch": "select comment_ranking, avg(author_comment_count) avg from hn_small where match('google') group by comment_ranking order by avg desc, comment_ranking desc limit 20",
    "clickhouse": "select comment_ranking, avg(author_comment_count) avg from hn_small where (match(story_text, '(?i)\\Wgoogle\\W') or match(story_author,'(?i)\\Wgoogle\\W') or match(comment_text, '(?i)\\Wgoogle\\W') or match(comment_author, '(?i)\\Wgoogle\\W')) group by comment_ranking order by avg desc, comment_ranking desc limit 20",
    "elasticsearch": "select comment_ranking, avg(author_comment_count) avg from hn_small where query('google') group by comment_ranking order by avg desc, comment_ranking desc limit 20",
    "mysql": "select comment_ranking, avg(author_comment_count) avg from hn_small where match(story_text,story_author,comment_text,comment_author) against ('google') group by comment_ranking order by avg desc, comment_ranking desc limit 20"
},
{
    "manticoresearch": "select comment_ranking, avg(author_comment_count) avg from hn_small where match('google') and comment_ranking > 200 group by comment_ranking order by avg desc, comment_ranking desc limit 20",
    "clickhouse":"select comment_ranking, avg(author_comment_count) avg from hn_small where (match(story_text, '(?i)\\Wgoogle\\W') or match(story_author,'(?i)\\Wgoogle\\W') or match(comment_text, '(?i)\\Wgoogle\\W') or match(comment_author, '(?i)\\Wgoogle\\W')) and comment_ranking > 200 group by comment_ranking order by avg desc, comment_ranking desc limit 20",
    "elasticsearch":"select comment_ranking, avg(author_comment_count) avg from hn_small where query('google') and comment_ranking > 200 group by comment_ranking order by avg desc, comment_ranking desc limit 20",
    "mysql":"select comment_ranking, avg(author_comment_count) avg from hn_small where match(story_text,story_author,comment_text,comment_author) against ('google') and comment_ranking > 200 group by comment_ranking order by avg desc, comment_ranking desc limit 20"
},
{
    "manticoresearch": "select comment_ranking, avg(author_comment_count+story_comment_count) avg from hn_small where match('google') and comment_ranking > 200 group by comment_ranking order by avg desc, comment_ranking desc limit 20",
    "clickhouse": "select comment_ranking, avg(author_comment_count+story_comment_count) avg from hn_small where (match(story_text, '(?i)\\Wgoogle\\W') or match(story_author,'(?i)\\Wgoogle\\W') or match(comment_text, '(?i)\\Wgoogle\\W') or match(comment_author, '(?i)\\Wgoogle\\W')) and comment_ranking > 200 group by comment_ranking order by avg desc, comment_ranking desc limit 20",
    "elasticsearch": "select comment_ranking, avg(author_comment_count+story_comment_count) avg from hn_small where query('google') and comment_ranking > 200 group by comment_ranking order by avg desc, comment_ranking desc limit 20",
    "mysql": "select comment_ranking, avg(author_comment_count+story_comment_count) avg from hn_small where match(story_text,story_author,comment_text,comment_author) against ('google') and comment_ranking > 200 group by comment_ranking order by avg desc, comment_ranking desc limit 20"
},
{
    "manticoresearch": "select * from hn_small where match('abc') limit 20",
    "clickhouse": "select * from hn_small where (match(story_text, '(?i)\\Wabc\\W') or match(story_author,'(?i)\\Wabc\\W') or match(comment_text, '(?i)\\Wabc\\W') or match(comment_author, '(?i)\\Wabc\\W')) limit 20",
    "elasticsearch": "select * from hn_small where query('abc') limit 20",
    "mysql": "select * from hn_small where match(story_text,story_author,comment_text,comment_author) against ('google') limit 20"
},
{
    "manticoresearch": "select * from hn_small where match('abc -google') limit 20",
    "clickhouse": "select * from hn_small where (match(story_text, '(?i)\\Wabc\\W') or match(story_author,'(?i)\\Wabc\\W') or match(comment_text, '(?i)\\Wabc\\W') or match(comment_author, '(?i)\\Wabc\\W')) and not (match(story_text, '(?i)\\Wgoogle\\W') or match(story_author,'(?i)\\Wgoogle\\W') or match(comment_text, '(?i)\\Wgoogle\\W') or match(comment_author, '(?i)\\Wgoogle\\W')) limit 20",
    "elasticsearch": "select * from hn_small where query('abc !google') limit 20",
    "mysql": "select * from hn_small where match(story_text,story_author,comment_text,comment_author) against ('abc -google') limit 20"
},
{
    "manticoresearch": "select * from hn_small where match('\"elon musk\"') limit 20",
    "clickhouse": "select * from hn_small where (match(story_text, '(?i)\\Welon\\Wmusk\\W') or match(story_author,'(?i)\\Welon\\Wmusk\\W') or match(comment_text, '(?i)\\Welon\\Wmusk\\W') or match(comment_author, '(?i)\\Welon\\Wmusk\\W')) limit 20",
    "elasticsearch": "select * from hn_small where query('\\\"elon musk\\\"') limit 20",
    "mysql": "select * from hn_small where match(story_text,story_author,comment_text,comment_author) against ('\"elon musk\"') limit 20"
},
{
    "manticoresearch": "select * from hn_small where match('abc') order by comment_ranking asc limit 20",
    "clickhouse": "select * from hn_small where (match(story_text, '(?i)\\Wabc\\W') or match(story_author,'(?i)\\Wabc\\W') or match(comment_text, '(?i)\\Wabc\\W') or match(comment_author, '(?i)\\Wabc\\W')) order by comment_ranking asc limit 20",
    "elasticsearch": "select * from hn_small where query('abc') order by comment_ranking asc limit 20",
    "mysql": "select * from hn_small where match(story_text,story_author,comment_text,comment_author) against ('abc') order by comment_ranking asc limit 20"
},
{
    "manticoresearch": "select * from hn_small where match('abc') order by comment_ranking asc, story_id desc limit 20",
    "clickhouse": "select * from hn_small where (match(story_text, '(?i)\\Wabc\\W') or match(story_author,'(?i)\\Wabc\\W') or match(comment_text, '(?i)\\Wabc\\W') or match(comment_author, '(?i)\\Wabc\\W')) order by comment_ranking asc, story_id desc limit 20",
    "elasticsearch": "select * from hn_small where query('abc') order by comment_ranking asc, story_id desc limit 20",
    "mysql": "select * from hn_small where match(story_text,story_author,comment_text,comment_author) against ('abc') order by comment_ranking asc, story_id desc limit 20"
},
{
    "manticoresearch": "select count(*) from hn_small where match('google') and comment_ranking > 200",
    "clickhouse": "select count(*) from hn_small where (match(story_text, '(?i)\\Wgoogle\\W') or match(story_author,'(?i)\\Wgoogle\\W') or match(comment_text, '(?i)\\Wgoogle\\W') or match(comment_author, '(?i)\\Wgoogle\\W')) and comment_ranking > 200",
    "elasticsearch": "select count(*) from hn_small where query('google') and comment_ranking > 200",
    "mysql": "select count(*) from hn_small where match(story_text,story_author,comment_text,comment_author) against ('google') and comment_ranking > 200"
},
{
    "manticoresearch": "select story_id from hn_small where match('me') order by comment_ranking asc limit 20",
    "clickhouse": "select story_id from hn_small where (match(story_text, '(?i)\\Wme\\W') or match(story_author,'(?i)\\Wme\\W') or match(comment_text, '(?i)\\Wme\\W') or match(comment_author, '(?i)\\Wme\\W')) order by comment_ranking asc limit 20",
    "elasticsearch": "select story_id from hn_small where query('me') order by comment_ranking asc limit 20",
    "mysql": "select story_id from hn_small where match(story_text,story_author,comment_text,comment_author) against ('me') order by comment_ranking asc limit 20"
},
{
    "manticoresearch": "select story_id, comment_id, comment_ranking, author_comment_count, story_comment_count, story_author, comment_author from hn_small where match('abc') limit 20",
    "clickhouse": "select story_id, comment_id, comment_ranking, author_comment_count, story_comment_count, story_author, comment_author from hn_small where (match(story_text, '(?i)\\Wabc\\W') or match(story_author,'(?i)\\Wabc\\W') or match(comment_text, '(?i)\\Wabc\\W') or match(comment_author, '(?i)\\Wabc\\W')) limit 20",
    "elasticsearch": "select story_id, comment_id, comment_ranking, author_comment_count, story_comment_count, story_author, comment_author from hn_small where query('abc') limit 20",
    "mysql": "select story_id, comment_id, comment_ranking, author_comment_count, story_comment_count, story_author, comment_author from hn_small where match(story_text,story_author,comment_text,comment_author) against ('abc') limit 20"
},
"select * from hn_small order by comment_ranking asc limit 20",
"select * from hn_small order by comment_ranking desc limit 20",
"select * from hn_small order by comment_ranking asc, story_id asc limit 20",
"select comment_ranking from hn_small order by comment_ranking asc limit 20",
"select comment_ranking, story_text from hn_small order by comment_ranking asc limit 20",
"select count(*) from hn_small where comment_ranking in (100,200)",
"select story_id from hn_small order by comment_ranking asc, author_comment_count asc, story_comment_count asc, comment_id asc limit 20"
]
```

## Results
You can find all the results on the [results page](https://db-benchmarks.com/?cache=fast_avg&engines=mysql_tuned&tests=hn&memory=1024&queries=0%2C1%2C2%2C3%2C6%2C7%2C8%2C9%2C12%2C13%2C15%2C16%2C17%2C18%2C19%2C20%2C21%2C23%2C24%2C25%2C26) by selecting “Test: hn_small”.

Remember that **the only high quality metric is “Fast avg” since it guarantees low [coefficient of variation](https://en.wikipedia.org/wiki/Coefficient_of_variation) and high queries count conducted for each query.** The other 2 (“Fastest” and “Slowest”) are provided with no guarantee since:

- **Slowest** - is a single attempt result, in most cases the very first coldest query. Even though we purge OS cache before each cold query it can’t be considered stable. So it can be used for informational purposes only (even though many benchmark authors publish such results without any disclaimer).
- **Fastest** - just the very fastest result, it should be in most cases similar to the “Fast avg” metric, but can be more volatile from run to run.
Remember the tests including the results are 100% transparent as well as everything in this project, so:

- you can use the [test framework](https://github.com/db-benchmarks/db-benchmarks) to learn how they were made
- and find raw test results in the [results](https://github.com/db-benchmarks/db-benchmarks/tree/main/results) directory.
**Unlike other less transparent and less objective benchmarks we are not making any conclusions, we are just leaving screenshots of the results here:**

**All competitors at once**


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/zymqlnxv2ti89c2vumx6.png)



**MySQL vs Clickhouse**


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/9jlpcdxg6ncpyqttt8lc.png)

**MySQL vs Elasitcsearch**


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/6rm37mhecukneth6v3fa.png)

**MySQL vs Manticore Search**


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/z9uv07ykxgue4gq50jed.png)

**MySQL vs Percona Server**


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/qlgqnhlugkyp72utl4i9.png)

**Clickhouse vs Elasticsearch**


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/r4dsp1jtfxqjwvjjdnjh.png)

**Manticore Search vs Elasticsearch**


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/h1jq7q43m44crjd2aaqv.png)

**Manticore Search vs Clickhouse**


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/t9ad0bxukkiya77dmv3z.png)

## Disclaimer
The author of this test and the test framework is a member of [Manticore Search](https://manticoresearch.com/) core team and the test was initially made to compare Manticore Search with Elasticsearch, but as shown above and can be verified in the [open source code](https://github.com/db-benchmarks/db-benchmarks) and by running the same test yourself Manticore Search wasn’t given any unfair advantage, so the test can be considered unprejudiced. However, if something is missing or wrong (i.e. non-objective) in the test feel free to make a pull request or an issue on [Github](https://github.com/db-benchmarks/db-benchmarks). Your take is appreciated! Thank you for spending your time reading this!