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Column Store Database Benchmarks: MariaDB ColumnStore vs. Clickhouse vs. Apache Spark

 | March 17, 2017 |  Posted In: Apache Spark, Big Data, Column Store Database, MySQL

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This blog shares some column store database benchmark results, and compares the query performance of MariaDB ColumnStore v. 1.0.7 (based on InfiniDB), Clickhouse and Apache Spark.

I’ve already written about ClickHouse (Column Store database).

The purpose of the benchmark is to see how these three solutions work on a single big server, with many CPU cores and large amounts of RAM. Both systems are massively parallel (MPP) database systems, so they should use many cores for SELECT queries.

For the benchmarks, I chose three datasets:

  1. Wikipedia page Counts, loaded full with the year 2008, ~26 billion rows
  2. Query analytics data from Percona Monitoring and Management
  3. Online shop orders

This blog post shares the results for the Wikipedia page counts (same queries as for the Clickhouse benchmark). In the following posts I will use other datasets to compare the performance.

Databases, Versions and Storage Engines Tested

  • MariaDB ColumnStore v. 1.0.7, ColumnStore storage engine
  • Yandex ClickHouse v. 1.1.54164, MergeTree storage engine
  • Apache Spark v. 2.1.0, Parquet files and ORC files

Although all of the above solutions can run in a “cluster” mode (with multiple nodes), I’ve only used one server.

Hardware

This time I’m using newer and faster hardware:

  • CPU: physical = 2, cores = 32, virtual = 64, hyperthreading = yes
  • RAM: 256Gb
  • Disk: Samsung SSD 960 PRO 1TB, NVMe card

Data Sizes

I’ve loaded the above data into Clickhouse, ColumnStore and MySQL (for MySQL the data included a primary key; Wikistat was not loaded to MySQL due to the size). MySQL tables are InnoDB with a primary key.

Dataset Size (GB) Column Store Clickhouse MySQL Spark / Parquet Spark / ORC file
Wikistat 374.24 Gb 211.3 Gb n/a (> 2 Tb) 395 Gb 273 Gb
Query metrics 61.23 Gb 28.35 Gb 520 Gb
Store Orders 9.3 Gb 4.01 Gb 46.55 Gb

 

Query Performance

Wikipedia page counts queries

Test type (warm) Spark Clickhouse ColumnStore
Query 1: count(*) 5.37 2.14 30.77
Query 2: group by month 205.75 16.36 259.09
Query 3: top 100 wiki pages by hits (group by path) 750.35 171.22 1640.7

Test type (cold) Spark Clickhouse ColumnStore
Query 1: count(*) 21.93 8.01 139.01
Query 2: group by month 217.88 16.65 420.77
Query 3: top 100 wiki pages by hits (group by path) 887.434 182.56 1703.19


Partitioning and Primary Keys

All of the solutions have the ability to take advantage of data “partitioning,” and only scan needed rows.

Clickhouse has “primary keys” (for the MergeTree storage engine) and scans only the needed chunks of data (similar to partition “pruning” in MySQL). No changes to SQL or table definitions is needed when working with Clickhouse.

Clickhouse example:

As we can see here, ClickHouse has processed ~two billion rows for one month of data, and ~23 billion rows for ten months of data. Queries that only select one month of data are much faster.

For ColumnStore we need to re-write the SQL query and use “between ‘2008-01-01’ and 2008-01-10′” so it can take advantage of partition elimination (as long as the data is loaded in approximate time order). When using functions (i.e., year(dt) or month(dt)), the current implementation does not use this optimization. (This is similar to MySQL, in that if the WHERE clause has month(dt) or any other functions, MySQL can’t use an index on the dt field.)

ColumnStore example:

Apache Spark does have partitioning however. It requires the use of partitioning with parquet format in the table definition. Without declaring partitions, even the modified query (“select count(*), month(date) as mon from wikistat where date between ‘2008-01-01’ and ‘2008-01-31’ group by mon order by mon”) will have to scan all the data.

The following table and graph shows the performance of the updated query:

Test type / updated query Spark Clickhouse ColumnStore
group by month, one month, updated syntax 205.75 0.93 12.46
group by month, ten months, updated syntax 205.75 8.84 170.81

 

Working with Large Datasets

With 1Tb uncompressed data, doing a “GROUP BY” requires lots of memory to store the intermediate results (unlike MySQL, ColumnStore, Clickhouse and Apache Spark use hash tables to store groups by “buckets”). For example, this query requires a very large hash table:

As “path” is actually a URL (without the hostname), it takes a lot of memory to store the intermediate results (hash table) for GROUP BY.

MariaDB ColumnStore does not allow us to “spill” data on disk for now (only disk-based joins are implemented). If you need to GROUP BY on a large text field, you can decrease the disk block cache setting in Columnstore.xml (i.e., set disk cache to 10% of RAM) to make room for an intermediate GROUP BY:

In addition, as the query has an ORDER BY, we need to increase max_length_for_sort_data in MySQL:

SQL Support

SQL Spark* Clickhouse ColumnStore
INSERT … VALUES ✅ yes ✅ yes ✅ yes
INSERT SELECT / BULK INSERT ✅ yes ✅ yes ✅ yes
UPDATE ❌ no ❌ no ✅ yes
DELETE ❌ no ❌ no ✅ yes
ALTER … ADD/DROP/MODIFY COLUMN ❌ no ✅ yes ✅ yes
ALTER … change paritions ✅ yes ✅ yes ✅ yes
SELECT with WINDOW functions ✅ yes ❌ no ✅ yes

 

*Spark does not support UPDATE/DELETE. However, Hive supports ACID transactions with UPDATE and DELETE statements. BEGIN, COMMIT, and ROLLBACK are not yet supported (only the ORC file format is supported).

ColumnStore is the only database out of the three that supports a full set of DML and DDL (almost all of the MySQL’s implementation of SQL is supported).

Comparing ColumnStore to Clickhouse and Apache Spark

 Solution  Advantages  Disadvantages
MariaDB ColumnStore
  • MySQL frontend (make it easy to migrate from MySQL)
  • UPDATE and DELETE are supported
  • Window functions support
  • Select queries are slower
  • No replication from normal MySQL server (planned for the future versions)
  • No support for GROUP BY on disk
Yandex ClickHouse
  • Fastest performance
  • Better compression
  • Primary keys
  • Disk-based GROUP BY, etc.
  • No MySQL protocol support
Apache Spark
  • Flexible storage options
  • Machine learning integration (i.e., pyspark ML libraries run inside spark nodes)
  • No MySQL protocol support
  • Slower select queries (compared to ClickHouse)


Conclusion

Yandex ClickHouse is an absolute winner in this benchmark: it shows both better performance (>10x) and better compression than
MariaDB ColumnStore and Apache Spark. If you are looking for the best performance and compression, ClickHouse looks very good.

At the same time, ColumnStore provides a MySQL endpoint (MySQL protocol and syntax), so it is a good option if you are migrating from MySQL. Right now, it can’t replicate directly from MySQL but if this option is available in the future we can attach a ColumnStore replication slave to any MySQL master and use the slave for reporting queries (i.e., BI or data science teams can use a ColumnStore database, which is updated very close to realtime).

Table Structure and List of Queries

Table structure (MySQL / Columnstore version):

Query 1:

Query 2a (full scan):

Query 2b (for partitioning test)

Query 3:

 

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Alexander Rubin

Alexander joined Percona in 2013. Alexander worked with MySQL since 2000 as DBA and Application Developer. Before joining Percona he was doing MySQL consulting as a principal consultant for over 7 years (started with MySQL AB in 2006, then Sun Microsystems and then Oracle). He helped many customers design large, scalable and highly available MySQL systems and optimize MySQL performance. Alexander also helped customers design Big Data stores with Apache Hadoop and related technologies.

9 Comments

  • I’ve been looking into different platforms to do analytics and this blog post makes me want to reconsider Clickhouse. What I don’t like about it it’s that apart of Yandex almost no one else is using it yet compared to hadoop based alternatives or MariaDB that I could easily get support in case I would have issues with them.

    Also it would be really cool to see a performance comparison over multiple nodes to compare how well this different systems scale over a cluster.

    • Hello Luis,

      as far as we can see, more than a hundred companies use ClickHouse. To make sure of this, simply join ClickHouse telegram chat or Google group. There you can ask any questions. The community and ClickHouse team responds promptly to them.

      If you still need a support service, please leave your contacts at clickhouse-feedback@yandex-team.ru

    • It is gathering popularity quickly here in Russia. For instance, we were switching to Spark from our legacy statistical system but immediately dumped everything we did after the clickhouse was released:

      1) It is turned to be much quicker
      2) The fact it is server greatly benifits us: free input source split. With spark you either creates a table with many columns which bad for readability and insert statement can be really long, thus error prone. Or parse these sources several times and this can be overly expensive at times. Not a problem with clickhouse.
      3) With clickhouse you don’t just have naturally distributed log parsing. You naturally have continuous data, second by second, minute by minute, day by day available in the single source. With Spark you will struggle with http://stackoverflow.com/questions/38793170/appending-to-orc-file.
      4) Clickhouse gives free to use realtime access to collected data. This is really useful in many circumstances. It is a great time saver sometimes.
      5) It is fast as I said. Hadoop is slow to the extent you could need several hosts just to discover you match the speed of relational operations over GNU utils (awk, grep, sort, join) on the single host. Or rather not quite up to that speed. Hadoop is just too slow.

      • Good to see that is getting traction, I couldn’t find many information about people using it but maybe if I would search on yandex I would get better information.

  • Thank you for very informative article.

    It would be nice if the comparison also included the difficulty of installation, data loading and tuning.

    There is no any mention about tuning. Does it mean that the databases were used “out of the box” with default settings?

    Also, how well MariaDB ColumnStore, ClickHouse and Apache Spark are supported online,
    I mean by Internet users? Could you find answers to your problems on the Internet?

  • Clickhouse has no Update or Delete functionality. It is still super fast, but lack of Update/Delete is a serious limitation for many users.

  • I sure hope that Percona can bring ClickHouse into the MySQL protocol so that percona toolkit will work with it, as well as the PMM. Very interesting. (sure wish there was Window functions support as I now have a postgres instance for that!!!?? and sore miss percona toolkit)

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