Solid State Drive (SSD) have made it big and have made their way not only in desktop computing but also in mission-critical servers. SSDs have proved to be a break-through in IO performance and leave HDD far far behind in terms of Random IO performance. Random IO is what most of the database administrators would be concerned about as that is 90% of the IO pattern visible on database servers like MySQL. I have found Intel 520-series and Intel 910-series to be quite popular and they do give very good numbers in terms of Random IOPS. However, its not just performance that you should be concerned about, failure predictions and health gauges are also very important, as loss of data is a big NO-NO. There is a great deal of misconception about the endurance level of SSD, as its mostly compared to rotating disks even when measuring endurance levels, however, there is a big difference in how both SSD and HDD work, and that has a direct impact on the endurance level of SSD.

Once MySQL is deployed inside a datacenter environment (i.e. forms a cloud ;-), major feature in it becomes replication. It is used to maintain hot copies, standby copies, read-only copies, invalidate external systems, replicate to external systems, etc. If this functionality is broken, datacenter is broken – components are not synchronized anymore, invalidations not done, data not consistent.

From performance perspective, replication not working properly results in unusable slaves so load cannot be spread. This results in higher load on other machines, including master (especially on master, if environment needs stronger consistency guarantees).

Judging on replication importance in MySQL deployments, it should attract performance engineering as much as InnoDB and other critical pieces. Though slave replication performance is being increased in 5.6, master side is not (well,

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Vadim and others have pointed at the index->lock problems before, but I think they didn’t good job enough at pointing out how bad it can get (the actual problematic was hidden somewhere as some odd edge case). What ‘index lock’ means is generally the fact that InnoDB has table-level locking which will kill performance on big tables miserably.

InnoDB is a huge pie of layers, that have various locking behaviors, and are layered on top of each other, and are structured nicely as subdirectories in your innodb_plugin directory. Low level storage interfaces are done via os/ routines, then on top of that there’s some file space manager, fsp/, which allocates space for btr/ to live in, where individual page/ entities live, with multiple row/ pieces.

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Warning, this may be kernel version specific, albeit this kernel is used by many database systems

Lately I’ve been working on getting more memory used by InnoDB buffer pool – besides obvious things like InnoDB memory tax there were seemingly external factors that were pushing out MySQL into swap (even with swappiness=0). We were working a lot on getting low hanging fruits like scripts that use too much memory, but they seem to be all somewhat gone, but MySQL has way too much memory pressure from outside.

I grabbed my uncache utility to assist with the investigation and started uncaching various bits on two systems, one that had larger buffer pool (60G), which was already being sent to swap, and a conservatively allocated (55G) machine, both 72G boxes. Initial

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We quite often say, that benchmark performance is usually different from real world performance – so performance engineering usually has to cover both – benchmarks allow to understand sustained performance bottlenecks, and real world analysis usually concentrates on something what would be considered ‘exceptional’ and not important in benchmarks – stalls of various kind. They are extremely important, as the state when our performance is lowest is the state of performance we provide to our platform users.

On a machine that is doing 5000qps, stalling for 100ms means that 500 queries were not served as fast as they could, or even hit application timeouts or exceptional MySQL conditions (like 1023 transaction limit). Of course, stalling for a second means 5000 queries were not served in time…

We have

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At Kscope this year, I attended a half day in-depth session entitled Data Warehousing Performance Best Practices, given by Maria Colgan of Oracle. In that session, there was a section on how to determine I/O throughput for a system, because in data warehousing I/O per second (iops) is less important than I/O throughput (how much actual data goes through, not just how many reads/writes).

The section contained an Oracle-specific in-database tool, and a standalone tool that can be used on many operating systems, regardless of whether or not a database exists:

If Oracle is installed, run DBMS_RESOURCE_MANAGER.CALIBRATE_IO:

SET SERVEROUTPUT ON
DECLARE

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A while ago I started a project which will be heavily IO-bound on the MySQL Server, the testmachine allocated for this had a DAS with 15 disks (although I only used 14) connected via external SAS (standard 3Gb/s half-duplex or 6Gb/s full-duplex on two ports).

I used sysbench for the tests, both fileio and oltp, although these results will be based on the fileio results. The setup with the disks in RAID10 (7 raid 1 sets, then striping over them) and later RAID50 (2 raid 5 sets with 7 disks each, then striping over that), the latter yielding better results.

Let’s take a look at 1,2,4,8,16 and 128 concurrent clients, with different IO schedulers, although using XFS.
The config for the raid controller was write-back, cached access, advanced readahead. 512MB battery backed cache on the controller.

Also, I tested both sequential reading (SEQRD in

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2.6.29 was released. I don’t usually write about linux kernel releases, thats what Slashdot is for :), but this one introduces write barriers in LVM, as well as ext4 with write barriers enabled by default. If you run this kernel and forget to turn off barrier support at filesystems (like XFS, nobarrier), you will see nasty performance slowdowns (recent post about it). Beware.

My favorite Linux tool in DB work is ‘iostat -x’ (and I really really want to see whenever I’m doing any kind of performance analysis), yet I had to learn its limitations and properties. For example, I took 1s snapshot from a slightly overloaded 16-disk database box:

avg-cpu:  %user   %nice %system %iowait  %steal   %idle
           8.12    0.00    2.57   21.65    0.00   67.66

Device:  rrqm/s   wrqm/s     r/s     w/s   rsec/s   wsec/s \
sda     7684.00    19.00 2420.00  498.00 81848.00  5287.00 \

        avgrq-sz avgqu-sz   await  svctm  %util
           29.86    32.99   11.17   0.34 100.00

I pasted this somewhere on IRC, and got “doesn’t look too healthy” and that it is disk-bound. Now, to understand if it really is, one has to understand what iostat tells here.

First line of numbers shows that we’ve got plenty of CPU resources (thats

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