Since my first post on MySQL transaction descriptors optimization introduced in Percona Server 5.5.30-30.2 and a followup by Dimitri Kravchuk, we have received a large number of questions on why the benchmark results in both posts look rather different. We were curious as well, so we tried to answer that question by retrying benchmarks on various combinations of hardware and dataset sizes, including the ones that are as close as possible to Dimitri’s environment. To put a long story short, the results are fairly consistent with our original post across all test combinations. …

The Micron P320h SSD is an SLC-based PCIe solid-state storage device which claims to provide the highest read throughput of any server-grade SSD, and at Micron’s request, I recently took some time to put the card through its paces, and the numbers are indeed quite impressive.

For reference, the benchmarks for this device were performed primarily on a Dell R720 with 192GB of RAM and two Xeon E5-2660 processors that yield a total of 32 virtual cores. This is the same machine which was used in my previous benchmark run. A small handful of additional tests were also performed using the Cisco UCS C250. The operating system in use was CentOS 6.3, and for the sysbench fileIO tests, the EXT4 filesystem was used. The card itself is the 700GB model.

So let’s take a look at the data.

With the sysbench fileIO test in asynchronous …

In addition to the problem with trx_list scan we discussed in Friday’s post, there is another issue in InnoDB transaction processing that notably affects MySQL performance – for every transaction InnoDB creates a read view and allocates memory for this structure from heap. The problem is that the heap for that allocation is destroyed on each commit and thus the read view memory is reallocated on the next transaction.

There are two aspects of this problem:

1) memory allocation is an costly operation and if memory …

One major problem in terms of MySQL performance that still stands in the way of InnoDB scalability is the trx_list scan on consistent read view creation. It was originally reported as a part of MySQL bug #49169 and can be described as follows. Whenever a connection wants to create a consistent read, it has to make a snapshot of the transaction states to determine which transactions are seen in the view later. To this end, InnoDB scans trx_list (i.e. the list of currently open transactions) and copies IDs of transactions that have not yet been committed at the current point in time, and thus should not be visible in the consistent read. For the REPEATABLE_READ isolation level, the snapshot is created on the first SELECT …

A few weeks ago I wrote about “MySQL Performance at High Concurrency” and why it is important, which was followed up by Vadim’s post on ThreadPool in Percona Server providing some great illustration on the topic. This time I want to target an opposite question: why MySQL performance at low concurrency is important for you.

I decided to write about this topic as a number of recent blog posts and articles look at MySQL performance starting with certain concurrency as the low point. For example, …

Facebook is a major user of MySQL and has pushed the performance limits of the technology. Their MySQL experts have deep, hands on knowledge of the technology. I’m pleased to welcome Mark Callaghan, Software Engineer for Database Infrastructure at Facebook, back again this year to the Percona Live MySQL Conference & Expo to share his expertise. Mark was a keynote speaker at last year’s conference and will appear this year with a group of Facebook MySQL experts:

• Domas Mituzas, …

I’m pleased to announce that Oracle is sending some of their top technical people to speak at the Percona Live MySQL Conference and Expo. The conference takes place April 22-25, 2013 at the Santa Clara Convention Center and Hyatt Santa Clara.

Tomas Ulin, VP, MySQL Engineering for Oracle, will present an invited keynote talk on “Driving MySQL Innovation” during the Tuesday morning opening keynotes. With the recent release of MySQL 5.6, conference attendees will hear about the latest developments of this major MySQL release.

In addition to Tomas, Oracle MySQL technologists will also lead three breakout …

Last time I wrote about memory allocators and how they can affect MySQL performance in general. This time I would like to explore this topic from a bit different angle: What impact does the number of processor cores have on different memory allocators and what difference we will see in MySQL performance in this scenario?

Let me share a conclusion first: If you have a server with more than 8 cores you should use something different than the default glibc memory allocator.
We recommend jemalloc or tcmalloc.

In my test I will use Dell R720 box(spec), Centos 6.3, upcoming Percona Server 5.5.30 and 3 allocators – stock glibc 2.13, jemalloc-3.1.0, the latest tcmalloc from svn repo. …

Investigating MySQL Replication Latency in Percona XtraDB Cluster

I was curious to check how Percona XtraDB Cluster behaves when it comes to MySQL replication latency — or better yet, call it data propagation latency. It was interesting to see whenever I can get stale data reads from other cluster nodes after write performed to some specific node. To test it I wrote quite a simple script (you can find it in the end of the post) which connects to one node in the cluster, performs an update and then immediately does the read from second node. If the data has been already propagated — good, if not we’ll continue to retry reads until it finally propagates, and then measure the latency. This is used to see whenever application …

Based on a lot of surprising comments about my MySQL 5.5 vs 5.6 performance post I decided to perform deeper investigation to see where my results could go possibly wrong. I had set up everything to be as simple as possible to get maximally repeatable results. I did Read Only ran which is typically a lot more repeatable (though also less relevant for production like workload). I had done number of iterations for benchmark run and I used dedicated physical hardware box so external environment impact often causing problems in Virtualized environments can be eliminated. Still I found there could be large variance between the runs.

I set up the benchmarks run to go over night in the loop, doing the benchmark run for 5 runs when restarting MySQL server and repeating the run. I did it on 2 identical boxes to eliminate faulty hardware as …