Sep
26
2018
--

Scaling IO-Bound Workloads for MySQL in the Cloud – part 2

Rplot07-innodb-iops

This post is a followup to my previous article https://www.percona.com/blog/2018/08/29/scaling-io-bound-workloads-mysql-cloud/

In this instance, I want to show the data in different dimensions, primarily to answer questions around how throughput scales with increasing IOPS.

A recap: for the test I use Amazon instances and Amazon gp2 and io1 volumes. In addition to the original post, I also tested two gpl2 volumes combined in software RAID0. I did this for the following reason: Amazon cap the single gp2 volume throughput to 160MB/sec, and as we will see from the charts, this limits InnoDB performance.

Also, a reminder from the previous post: we can increase gp2 IOPS by increasing volume size (to the top limit 10000 IOPS), and for io1 we can increase IOPS by paying per additional IOPS.

Scaling with InnoDB

So for the first result, let’s see how InnoDB scales with increasing IOPS.

There are a few interesting observations here: InnoDB scales linearly with additional IOPS, but it faces a throughput limit that Amazon applies to volumes.

So besides considering IOPS, we should take into account the maximal throughout of volumes.

In the second chart we compare InnoDB performance vs the cost of volumes:

It’s interesting to see here the slope for gp2 volumes is steeper than for io1 volumes. This means we can get a bigger increase in InnoDB performance per dollar using gp2 volumes, but only until we reach the IOPS and throughput limits that are applied to gp2 volumes.

Scaling with MyRocks

And here’s the similar chart but for MyRocks:

Here we can also see that MyRocks scales linearly, showing identical results on gp2 and io1 volumes. This means that running on gp2 will be cheaper. Also, there is no plateau in throughput, as we saw for InnoDB, which means that MyRocks uses less IO throughput.

And the chart for the cost of running MyRocks:

This charts also shows that it is cheaper to run on gp2 volume but only while it provides enough IOPS. I assume that using two gp2 volumes would allow me to double the throughput. (I did not run the test for MyRocks using two volumes)

Conclusions

  • Both MyRocks and InnoDB can scale (linearly) with additional IOPS on gp2 and io1 Amazon volumes.
  • Take into account that IOPS is not the only factor to consider. There is also throughput limit, which affects InnoDB results, so for further scaling you might need to use multiple volumes.

The post Scaling IO-Bound Workloads for MySQL in the Cloud – part 2 appeared first on Percona Database Performance Blog.

Aug
01
2018
--

Saving With MyRocks in The Cloud

The main focus of a previous blog post was the performance of MyRocks when using fast SSD devices. However, I figured that MyRocks would be beneficial for use in cloud workloads, where storage is either slow or expensive.

In that earlier post, we demonstrated the benefits of MyRocks, especially for heavy IO workloads. Meanwhile, Mark wrote in his blog that the CPU overhead in MyRocks might be significant for CPU-bound workloads, but this should not be the issue for IO-bound workloads.

In the cloud the cost of resources is a major consideration. Let’s review the annual cost for the processing and storage resources.

 Resource cost/year, $   IO cost $/year   Total $/year 
c5.9xlarge  7881    7881
1TB io1 5000 IOPS  1500  3900    5400
1TB io1 10000 IOPS  1500  7800    9300
1TB io1 15000 IOPS  1500  11700  13200
1TB io1 20000 IOPS  1500  15600  17100
1TB io1 30000 IOPS  1500  23400  24900
3.4TB GP2 (10000 IOPS)  4800    4800

 

The scenario

The server version is Percona Server 5.7.22

For instances, I used c5.9xlarge instances. The reason for c5 was that it provides high performance Nitro virtualization: Brendan Gregg describes this in his blog post. The rationale for 9xlarge instances was to be able to utilize io1 volumes with a 30000 IOPS throughput – smaller instances will cap io1 throughput at a lower level.

I also used huge gp2 volumes: 3400GB, as this volume provides guaranteed 10000 IOPS even if we do not use io1 volumes. This is a cheaper alternative to io1 volumes to achieve 10000 IOPS.

For the workload I used sysbench-tpcc 5000W (50 tables * 100W), which for InnoDB gave about 471GB in storage used space.

For the cache I used 27GB and 54G buffer size, so the workload is IO-heavy.

I wanted to compare how InnoDB and RocksDB performed under this scenario.

If you are curious I prepared my terraform+ansible deployment files here: https://github.com/vadimtk/terraform-ansible-percona

Before jumping to the results, I should note that for MyRocks I used LZ4 compression for all levels, which in its final size is 91GB. That is five times less than InnoDB size. This alone provides operational benefits—for example to copy InnoDB files (471GB) from a backup volume takes longer than 1 hour, while it is much faster (five times) for MyRocks.

The benchmark results

So let’s review the results.

InnoDB versus MyRocks throughput in the cloud

Or presenting average throughput in a tabular form:

cachesize IOPS engine avg TPS
27 5000 innodb 132.66
27 5000 rocksdb 481.03
27 10000 innodb 285.93
27 10000 rocksdb 1224.14
27 10000gp2 innodb 227.19
27 10000gp2 rocksdb 1268.89
27 15000 innodb 436.04
27 15000 rocksdb 1839.66
27 20000 innodb 584.69
27 20000 rocksdb 2336.94
27 30000 innodb 753.86
27 30000 rocksdb 2508.97
54 5000 innodb 197.51
54 5000 rocksdb 667.63
54 10000 innodb 433.99
54 10000 rocksdb 1600.01
54 10000gp2 innodb 326.12
54 10000gp2 rocksdb 1559.98
54 15000 innodb 661.34
54 15000 rocksdb 2176.83
54 20000 innodb 888.74
54 20000 rocksdb 2506.22
54 30000 innodb 1097.31
54 30000 rocksdb 2690.91

 

We can see that MyRocks outperformed InnoDB in every single combination, but it is also important to note the following:

MyRocks on io1 5000 IOPS showed the performance that InnoDB showed in io1 15000 IOPS.

That means that InnoDB requires three times more in storage throughput. If we take a look at the storage cost, it corresponds to three times more expensive storage. Given that MyRocks requires less storage, it is possible to save even more on storage capacity.

On the most economical storage (3400GB gp2, which will provide 10000 IOPS) MyRocks showed 4.7 times better throughput.

For the 30000 IOPS storage, MyRocks was still better by 2.45 times.

However it is worth noting that MyRocks showed a greater variance in throughput during the runs. Let’s review the charts with 1 sec resolution for GP2 and io1 30000 IOPS storage:Throughput 1 sec resolution for GP2 and io1 30000 IOPS storage MyROCKS versus InnoDB

Such variance might be problematic for workloads that require stable throughput and where periodical slowdowns are unacceptable.

Conclusion

MyRocks is suitable and beneficial not only for fast SSD, but also for cloud deployments. By requiring less IOPS, MyRocks can provide better performance and save on the storage costs.

However, before evaluating MyRocks, make sure that your workload is IO-bound i.e. the working set is much bigger than available memory. For CPU-intensive workloads (where the working set fits into memory), MyRocks will be less beneficial or even perform worse than InnoDB (as described in the blog post A Look at MyRocks Performance)

 

 

 

The post Saving With MyRocks in The Cloud appeared first on Percona Database Performance Blog.

Powered by WordPress | Theme: Aeros 2.0 by TheBuckmaker.com