Over a million developers have joined DZone.

Why %util Number from Iostat is Meaningless for MySQL Capacity Planning

DZone's Guide to

Why %util Number from Iostat is Meaningless for MySQL Capacity Planning

· Performance Zone
Free Resource

Evolve your approach to Application Performance Monitoring by adopting five best practices that are outlined and explored in this e-book, brought to you in partnership with BMC.

Earlier this month I wrote about vmstat iowait cpu numbers, and some of the comments I got were advertising the use of util% as reported by the iostat tool instead. I find this number even more useless for MySQL performance tuning and capacity planning.

Now let me start by saying this is a really tricky and deceptive number. Many DBAs who report instances of their systems having a very busy IO subsystem said the util% in vmstat was above 99% and therefore they believe this number is a good indicator of an overloaded IO subsystem.

Indeed – when your IO subsystem is busy, up to its full capacity, the utilization should be very close to 100%. However, it is perfectly possible for the IO subsystem and MySQL with it to have plenty more capacity than when utilization is showing 100% – as I will show in an example.

Before that though lets see what the iostat manual page has to say on this topic – from this main page we can read:


Percentage of CPU time during which I/O requests were issued to the device (bandwidth utilization for the device). Device saturation occurs when this value is close to 100% for devices serving requests serially. But for devices serving requests in parallel, such as RAID arrays and modern SSDs, this number does not reflect their performance limits.

Which says right here that the number is useless for pretty much any production database server that is likely to be running RAID, Flash/SSD, SAN or cloud storage (such as EBS) capable of handling multiple requests in parallel.

Let’s look at the following illustration. I will run sysbench on a system with a rather slow storage data size larger than buffer pool and uniform distribution to put pressure on the IO subsystem. I will use a read-only benchmark here as it keeps things more simple…

sysbench –num-threads=1 –max-requests=0 –max-time=6000000 –report-interval=10 –test=oltp –oltp-read-only=on –db-driver=mysql –oltp-table-size=100000000 –oltp-dist-type=uniform –init-rng=on –mysql-user=root –mysql-password= run

I’m seeing some 9 transactions per second, while disk utilization from iostat is at nearly 96%:

[ 80s] threads: 1, tps: 9.30, reads/s: 130.20, writes/s: 0.00 response time: 171.82ms (95%)
[ 90s] threads: 1, tps: 9.20, reads/s: 128.80, writes/s: 0.00 response time: 157.72ms (95%)
[ 100s] threads: 1, tps: 9.00, reads/s: 126.00, writes/s: 0.00 response time: 215.38ms (95%)
[ 110s] threads: 1, tps: 9.30, reads/s: 130.20, writes/s: 0.00 response time: 141.39ms (95%)

Device: rrqm/s wrqm/s r/s w/s rsec/s wsec/s avgrq-sz avgqu-sz await svctm %util
dm-0 0.00 0.00 127.90 0.70 4070.40 28.00 31.87 1.01 7.83 7.52 96.68

This makes a lot of sense – with read single thread read workload the drive should be only used getting data needed by the query, which will not be 100% as there is some extra time needed to process the query on the MySQL side as well as passing the result set back to sysbench.

So 96% utilization; 9 transactions per second, this is a close to full-system capacity with less than 5% of device time to spare, right?

Let’s run a benchmark with more concurrency – 4 threads at the time; we’ll see…

[ 110s] threads: 4, tps: 21.10, reads/s: 295.40, writes/s: 0.00 response time: 312.09ms (95%)
[ 120s] threads: 4, tps: 22.00, reads/s: 308.00, writes/s: 0.00 response time: 297.05ms (95%)
[ 130s] threads: 4, tps: 22.40, reads/s: 313.60, writes/s: 0.00 response time: 335.34ms (95%)

Device: rrqm/s wrqm/s r/s w/s rsec/s wsec/s avgrq-sz avgqu-sz await svctm %util
dm-0 0.00 0.00 295.40 0.90 9372.80 35.20 31.75 4.06 13.69 3.38 100.01

So we’re seeing 100% utilization now, but what is interesting – we’re able to reclaim much more than less than 5% which was left if we look at utilization – throughput of the system increased about 2.5x

Finally let’s do the test with 64 threads – this is more concurrency than exists at storage level which is conventional hard drives in RAID on this system…

[ 70s] threads: 64, tps: 42.90, reads/s: 600.60, writes/s: 0.00 response time: 2516.43ms (95%)
[ 80s] threads: 64, tps: 42.40, reads/s: 593.60, writes/s: 0.00 response time: 2613.15ms (95%)
[ 90s] threads: 64, tps: 44.80, reads/s: 627.20, writes/s: 0.00 response time: 2404.49ms (95%)

Device: rrqm/s wrqm/s r/s w/s rsec/s wsec/s avgrq-sz avgqu-sz await svctm %util
dm-0 0.00 0.00 601.20 0.80 19065.60 33.60 31.73 65.98 108.72 1.66 100.00

In this case we’re getting 4.5x of throughput compared to single thread and 100% utilization. We’re also getting almost double throughput of the run with 4 thread where 100% utilization was reported. This makes sense – there are 4 drives which can work in parallel and with many outstanding requests they are able to optimize their seeks better hence giving a bit more than 4x.

So what have we so ? The system which was 96% capacity but which could have driven still to provide 4.5x throughput – so it had plenty of extra IO capacity. More powerful storage might have significantly more ability to run requests in parallel so it is quite possible to see 10x or more room after utilization% starts to be reported close to 100%

So if utilization% is not very helpful what can we use to understand our database IO capacity better ? First lets look at the performance reported from those sysbench runs. If we look at 95% response time you can see 1 thread and 4 threads had relatively close 95% time growing just from 150ms to 250-300ms. This is the number I really like to look at- if system is able to respond to the queries with response time not significantly higher than it has with concurrency of 1 it is not overloaded. I like using 3x as multiplier – ie when 95% spikes to be more than 3x of the single concurrency the system might be getting to the overload.

With 64 threads the 95% response time is 15-20x of the one we see with single thread so it is surely overloaded.

Do we have anything reported by iostat which we can use in a similar way? It turns out we do! Check out the “await” column which tells us how much the requester had to wait for the IO request to be serviced. With single concurrency it is 7.8ms which is what this drives can do for random IO and is as good as it gets. With 4 threads it is 13.7ms – less than double of best possible, so also good enough… with concurrency of 64 it is however 108ms which is over 10x of what this device could produce with no waiting and which is surely sign of overload.

A couple words of caution. First, do not look at svctm which is not designed with parallel processing in mind. You can see in our case it actually gets better with high concurrency while really database had to wait a lot longer for requests submitted. Second, iostat mixes together reads and writes in single statistics which specifically for databases and especially on RAID with BBU might be like mixing apples and oranges together – writes should go to writeback cache and be acknowledged essentially instantly while reads only complete when actual data can be delivered. The tool pt-diskstats from Percona Tookit breaks them apart and so can be much more for storage tuning for database workload. Some of the recent operating systems also ship with sysstat/iostat which breaks out await to r_await and w_await which is much more useful.

Final note – I used a read-only workload on purpose – when it comes to writes things can be even more complicated – MySQL buffer pool can be more efficient with more intensive writes plus group commit might be able to commit a lot of transactions with single disk write. Still, the same base logic will apply.

Summary: The take away is simple – util% only shows if a device has at least one operation to deal with or is completely busy, which does not reflect actual utilization for a majority of modern IO subsystems. So you may have a lot of storage IO capacity left even when utilization is close to 100%.

Learn tips and best practices for optimizing your capacity management strategy with the Market Guide for Capacity Management, brought to you in partnership with BMC.


Published at DZone with permission of Peter Zaitsev, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.


Dev Resources & Solutions Straight to Your Inbox

Thanks for subscribing!

Awesome! Check your inbox to verify your email so you can start receiving the latest in tech news and resources.


{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}