This is a time-honored topic, and there’s no shortage of articles on the topic on this blog. I wanted to write a post trying to condense and clarify those posts, as it has taken me a while to really understand this relationship.

Some basic facts

• Most of …

This post is being constantly updated as we find out more useful information on Momentum tuning. Last update: 2012-05-05.

About 2 months ago I’ve joined LivingSocial technical operations team and one of my first tasks there was to figure out a way to make our MTAs perform better and deliver faster. We use a really great product called Momentum MTA (former Ecelerity) and it is really fast, but it is always good to be able to squeeze as much performance as possible so I’ve started looking for a ways to make our system faster.

While working on it I’ve created a set of scripts to integrate Momentum with Graphite for all kinds of crazy stats graphing, those scripts will be opensourced soon, but for now I’ve decided to share a few tips about performance-related changes …

There are about a gazillion FAQs and HOWTOs out there that talk about XFS configuration, RAID IO alignment, and mount point options.  I wanted to try to put some of that information together in a condensed and simplified format that will work for the majority of use cases.  This is not meant to cover every single tuning option, but rather to cover the important bases in a simple and easy to understand way.

Let’s say you have a server with standard hardware RAID setup running conventional HDDs.

RAID setup

For the sake of simplicity you create one single RAID logical volume that covers all your available drives.  This is the easiest setup to configure and maintain and is the best choice for operability in the majority of normal configurations.  Are there ways to squeeze more performance out of a server by dividing the logical volumes: perhaps, but it requires a lot of fiddling and custom tuning to …

Performance schema performance tuning One of the most important question users ask before deciding to use the performance schema is: what is the overhead ? The underlying concern of course is to make sure deploying the performance schema does not negatively impact production ("First, do no harm").
The question is simple, and yet the answer is not so simple, as it depends on so many things.
As seen in some benchmarks done already, the overhead when using the performance schema can vary a lot, and in some cases really causes degradations.
A lot of time has been spent investigating the root causes (note the plural form) for the performance schema overhead in general, so I think it is a good time to share current findings.

Server workload Analysing performances, and testing the effects of different code or configuration changes, can hardly be done in a vacuum. That's where benchmarks come in.
The problem with …

By popular request, here’s the PDF of the slides of this talk as presented in January 2011 in brisbane; it’s fairly self-explanatory. Note that it’s not really extensive “tuning”, it just fixes up a few things that are usually “wrong” in default installs, creating a more sane baseline. If you want to get to optimal correctness and more performance, other things do need to be done as well.

Now that flash storage is becoming more popular, IO alignment question keeps popping up more often than it used to when all we had were rotating hard disk drives. I think the reason is very simple – when systems only had one bearing hard disk drive (HDD) as in RAID1 or one disk drive at all, you couldn’t really have misaligned IO because HDDs operate in 512-byte sectors and that’s also the smallest amount of disk IO that systems can do. NAND flash on the other hand can have a page size of 512-bytes, 2kbytes or 4kbytes (and often you don’t know what size it is really) so the IO alignment question becomes more relevant.

It was and still is, however, relevant with HDD RAID storage – technology we have been using for many years – when there’s striping like in RAID0, 5, 6 or any variation of them (5+0, 1+0, 1+0+0 etc.). While IO inside the RAID is perfectly aligned to disk sectors (again due to the fact operations are done in …

Listening to the OurSQL podcast: Repli-cans and Repli-can’ts got me thinking, what are the issues with MySQL replication that Sarah and Sheeri didn’t have the time to include in their episode. Here’s my list:
Replication Capacity Index This is a concept introduced by Percona in last year’s post: Estimating Replication Capacity which I revisited briefly during my presentation at this year’s MySQL Users Conference. Why is this important? Very …

“The least expensive query is the query you never run.”

Data access is expensive for your application. It often requires CPU, network and disk access, all of which can take a lot of time. Using less computing resources, particularly in the cloud, results in decreased overall operational costs, so caches provide real value by avoiding using those resources. You need an efficient and reliable cache in order to achieve the desired result. Your end users also care about response times because this affects their work productivity or their enjoyment of your service. This post describes some of the most common cache methods for MySQL.

Popular cache methods

The MySQL query cache

When the query cache is enabled, MySQL examines each query to see if the contents have been stored in the query cache. If the results have been cached they are used instead of actually running the query.. This improves the response time …

innodb_buffer_pool_instances is, perhaps, the single most important tuning parameter in MySQL 5.5. And the default of 1 severely caps MySQL performance on any recent hardware. A more reasonable default is called for, e.g. (innodb_buffer_pool_size in GB + number of CPUs)/2.
This observation is coming from my recent experience of "first run" tuning of MySQL 5.5 on several installations: this is the first thing that was changed on all of them.

It’s always good to have some equations for reference when you are tuning a MySQL server. How else will you know what to set your buffer sizes to after all? If you have some that I’ve missed… add a comment!

Per-Thread Buffer memory utilization (read_buffer_size + read_rnd_buffer_size + sort_buffer_size + thread_stack + join_buffer_size + binlog_cache_size) * max_connections Global Buffer memory utilization innodb_buffer_pool_size + innodb_additional_mem_pool_size + innodb_log_buffer_size + key_buffer_size + query_cache_size Threads and Connections thread_cache miss rate = Threads_created / Connections connection ratio = (max_used_connections*100)/ max_connections threads_per_second = threads_created / uptime Key Buffer key_buffer_free = (key_blocks_unused * key_cache_block_size) / (key_buffer_size * 100) key_cache_miss_rate = key_read_requests / key_reads …