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A Look at MyRocks Performance

 | April 30, 2018 |  Posted In: Benchmarks, Insight for DBAs, MyRocks, MySQL


In this blog post, I’ll look at MyRocks performance through some benchmark testing.

As the MyRocks storage engine (based on the RocksDB key-value store ) is now available as part of Percona Server for MySQL 5.7, I wanted to take a look at how it performs on a relatively high-end server and SSD storage. I wanted to check how it performs for different amounts of available memory for the given database size. This is similar to the benchmark I published a while ago for InnoDB (

In this case, I plan to use a sysbench-tpcc benchmark ( and I will execute it for both MyRocks and InnoDB. We’ll use InnoDB as a baseline.

For the benchmark, I will use 100 TPC-C warehouses, with a set of 10 tables (to shift the bottleneck from row contention). This should give roughly 90GB of data size (when loaded into InnoDB) and is a roughly equivalent to 1000 warehouses data size.

To vary the memory size, I will change innodb_buffer_pool_size from 5GB to 100GB for InnoDB, and rocksdb_block_cache_size for MyRocks.

For MyRocks we will use LZ4 as the default compression on disk. The data size in the MyRocks storage engine is 21GB. Interesting to note, that in MyRocks uncompressed size is 70GB on the storage.

For both engines, I did not use FOREIGN KEYS, as MyRocks does not support it at the moment.

MyRocks does not support SELECT .. FOR UPDATE statements in REPEATABLE-READ mode in the Percona Server for MySQL implementation. However, “SELECT .. FOR UPDATE” is used in this benchmark. So I had to use READ-COMMITTED mode, which is supported.

The most important setting I used was to enable binary logs, for the following reasons:

  1. Any serious production uses binary logs
  2. With disabled binary logs, MyRocks is affected by a suboptimal transaction coordinator

I used the following settings for binary logs:

  • binlog_format = ‘ROW’
  • binlog_row_image=minimal
  • sync_binlog=10000 (I am not using 0, as this causes serious stalls during binary log rotations, when the  content of binary log is flushed to storage all at once)

While I am not a full expert in MyRocks tuning yet, I used recommendations from this page: The Facebook-MyRocks engineering team also provided me input on the best settings for MyRocks.

Let’s review the results for different memory sizes.

This first chart shows throughput jitter. This helps to understand the distribution of throughput results. Throughput is measured every 1 second, and on the chart I show all measurements after 2000 seconds of a run (the total length of each run is 3600 seconds). So I show the last 1600 seconds of each run (to remove warm-up phases):

MyRocks Performance

To better quantify results, let’s take a look at them on a boxplot. The quickest way to understand boxplots is to take a look at the middle line. It represents a median of measurements (see more at

MyRocks Performance 2

Before we jump to the summary of results, let’s take a look at a variation of the throughput for both InnoDB and MyRocks. We will zoom to a 1-second resolution chart for 100 GB of allocated memory:

MyRocks Performance 3

We can see that there is a lot of variation with periodical 1-second performance drops with MyRocks. At this moment, I do not know what causes these drops.

So let’s take a look at the average throughput for each engine for different memory settings (the results are in tps, and more is better):

Memory, GB InnoDB MyRocks
5 849.0664 4205.714
10 1321.9 4298.217
20 1808.236 4333.424
30 2275.403 4394.413
40 2968.101 4459.578
50 3867.625 4503.215
60 4756.551 4571.163
70 5527.853 4576.867
80 5984.642 4616.538
90 5949.249 4620.87
100 5961.2 4599.143


This is where MyRocks behaves differently from InnoDB. InnoDB benefits greatly from additional memory, up to the size of working dataset. After that, there is no reason to add more memory.

At the same time, interestingly MyRocks does not benefit much from additional memory.

Basically, MyRocks performs as expected for a write-optimized engine. You can refer to my article How Three Fundamental Data Structures Impact Storage and Retrieval for more details. 

In conclusion, InnoDB performs better (compared to itself) when the working dataset fits (or almost fits) into available memory, while MyRocks can operate (and outperform InnoDB) on small memory sizes.

IO and CPU usage

It is worth looking at resource utilization for each engine. I took vmstat measurements for each run so that we can analyze IO and CPU usage.

First, let’s review writes per second (in KB/sec). Please keep in mind that these writes include binary log writes too, not just writes from the storage engine.

Memory, GB InnoDB MyRocks
5 244754.4 87401.54
10 290602.5 89874.55
20 311726 93387.05
30 313851.7 93429.92
40 316890.6 94044.94
50 318404.5 96602.42
60 276341.5 94898.08
70 217726.9 97015.82
80 184805.3 96231.51
90 187185.1 96193.6
100 184867.5 97998.26


We can also calculate how many writes per transaction each storage engine performs:

MyRocks Performance 4

This chart shows the essential difference between InnoDB and MyRocks. MyRocks, being a write-optimized engine, uses a constant amount of writes per transaction.

For InnoDB, the amount of writes greatly depends on the memory size. The less memory we have, the more writes it has to perform.

What about reads?

The following table shows reads in KB per second.

Memory, GB InnoDB MyRocks
5 218343.1 171957.77
10 171634.7 146229.82
20 148395.3 125007.81
30 146829.1 110106.87
40 144707 97887.6
50 132858.1 87035.38
60 98371.2 77562.45
70 42532.15 71830.09
80 3479.852 66702.02
90 3811.371 64240.41
100 1998.137 62894.54


We can translate this to the number of reads per transaction:

MyRocks Performance 5

This shows MyRocks’ read-amplification. The allocation of more memory helps to decrease IO reads, but not as much as for InnoDB.

CPU usage

Let’s also review CPU usage for each storage engine. Let’s start with InnoDB:

MyRocks Performance 6

The chart shows that for 5GB memory size, InnoDB spends most of its time in IO waits (green area), and the CPU usage (blue area) increases with more memory.

This is the same chart for MyRocks:

MyRocks Performance 7

In tabular form:

Memory, GB engine us sys wa id
5 InnoDB 8 2 57 33
5 MyRocks 56 11 18 15
10 InnoDB 12 3 57 28
10 MyRocks 57 11 18 13
20 InnoDB 16 4 55 25
20 MyRocks 58 11 19 11
30 InnoDB 20 5 50 25
30 MyRocks 59 11 19 10
40 InnoDB 26 7 44 24
40 MyRocks 60 11 20 9
50 InnoDB 35 8 38 19
50 MyRocks 60 11 21 7
60 InnoDB 43 10 36 10
60 MyRocks 61 11 22 6
70 InnoDB 51 12 34 4
70 MyRocks 61 11 23 5
80 InnoDB 55 12 31 1
80 MyRocks 61 11 23 5
90 InnoDB 55 12 32 1
90 MyRocks 61 11 23 4
100 InnoDB 55 12 32 1
100 MyRocks 61 11 24 4


We can see that MyRocks uses a lot of CPU (in us+sys state) no matter how much memory is allocated. This leads to the conclusion that MyRocks performance is limited more by CPU performance than by available memory.

MyRocks directory size

As MyRocks writes all changes and compacts SST files down the road, it would be interesting to see how the data directory size changes during the benchmark so we can estimate our storage needs. Here is a chart of datadirectory size:

MyRocks Performance 8

We can see that datadirectory goes from 20GB at the start, to 31GB during the benchmark. It is interesting to observe the data growing until compaction shrinks it.


In conclusion, I can say that MyRocks performance increases as the ratio of dataset size to memory increases, outperforming InnoDB by almost five times in the case of 5GB memory allocation. Throughput variation is something to be concerned about, but I hope this gets improved in the future.

MyRocks does not require a lot of memory and shows constant write IO, while using most of the CPU resources.

I think this potentially makes MyRocks a great choice for cloud database instances, where both memory and IO can cost a lot. MyRocks deployments would make it cheaper to deploy in the cloud.

I will follow up with further cloud-oriented benchmarks.


Raw results, scripts and config

My goal is to provide fully repeatable benchmarks. To this end, I’m  sharing all the scripts and settings I used in the following GitHub repo:

MyRocks settings

InnoDB settings

Hardware spec

Supermicro server:

  • CPU:
    • Intel(R) Xeon(R) CPU E5-2683 v3 @ 2.00GHz
    • 2 sockets / 28 cores / 56 threads
  • Memory: 256GB of RAM
  • Storage: SAMSUNG  SM863 1.9TB Enterprise SSD
  • Filesystem: ext4
  • Percona-Server-5.7.21-20
  • OS: Ubuntu 16.04.4, kernel 4.13.0-36-generic
Vadim Tkachenko

Vadim Tkachenko co-founded Percona in 2006 and serves as its Chief Technology Officer. Vadim leads Percona Labs, which focuses on technology research and performance evaluations of Percona’s and third-party products. Percona Labs designs no-gimmick tests of hardware, filesystems, storage engines, and databases that surpass the standard performance and functionality scenario benchmarks. Vadim’s expertise in LAMP performance and multi-threaded programming help optimize MySQL and InnoDB internals to take full advantage of modern hardware. Oracle Corporation and its predecessors have incorporated Vadim’s source code patches into the mainstream MySQL and InnoDB products. He also co-authored the book High Performance MySQL: Optimization, Backups, and Replication 3rd Edition.


  • Your innodb_buffer_pool_size is twice the maximum amount of memory (100G) you used for your benchmark. Wouldn’t double the amount of ram for the buffer pool cause MySQL to go into swap?

    Was that number static throughout the tests or did you tweak it based on the amount of memory available?

  • Hi Vadim,

    Your 2010 benchmark on RAM vs. fast SSD is my favourite benchmark ever 🙂 . Thank you for running it again in 2018 with variance included.

    I would be curious to see 8.0 results for InnoDB… there were some redo log enhancements that made it in shortly before GA.

  • Great article. The throughput heatmap is very interesting and a good way to visualize the higher variance of rocksDB.

    One hidden danger with LSM (rocksDB) is that you may not be catching all the more extreme edgecases of compactions with a test that only lasts for 1 hr.
    As the data set on disk grows larger and data gets compacted to lower and lower levels, there can definitely be cases when a larger compaction can run after 6, 12 or even 24 hours of running the system, and it can introduce bigger variance in throughput, while those background compactions are running. In some cases the throughput drops can even last for many minutes, which can definitely cause some unexpected issues for a database that is serving live low latency traffic.

    • With fast storage 1 hour is probably long enough to see steady state behavior.

      RocksDB supports leveled and tiered compaction but the default is leveled. While there might be long running compaction with tiered, that doesn’t happen with level as each compaction step consumes ~11 SST files which should be a few hundred MB of data.

    • Zviad,

      This is where tuning may help. With the most default settings I saw stalls even during 1 hour runs.
      With changes to settings (you can see the config in the post) I was able to mitigate compaction stalls

  • Vadim, I would also suggest running the test which uses buffered-IO with MyRocks. Read-ahead in RocksDB is currently not supported when directIO is used and depending on the query plan that may be a big deal for some of the queries.

    • Ovais,

      That correct and it might be beneficial for some workloads. In this particular test I tried buffered-IO and I saw better results with O_DIRECT than with buffered-IO .

  • Thank you very much for the interesting comparison. I have a question regarding performance with low page buffer (Inno) and block cache size(MyRocks). For InnoDB, buffer pool is the main consumer of memory storing both data and indexes. However for RocksDB, additional memory is allocated for bloom filters and for index blocks.
    How large was this memory area in your experiments?

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