MySQL Performance Blog

I will co-present in webinar on Performance Challenges and Solutions for IO Bound Workloads in MySQL. My part of the presentation will be speaking about why switching from CPU bound workload to IO bound is such an important event, how to prepare to it as well as how to keep your application performance good as the data growths.

The Brian’s portion of webinar will focus on the Schooner offering as example of flash based appliance - one of solutions I mention in my presentation.

It should be interesting whenever you’re interested in Schooner appliance offerings, flash or scaling MySQL in General.

You can follow this url to register.

I wanted to share a little rule of thumb I sometimes use to decide which columns should come first in an index. This is not specific to MySQL, it’s generally applicable to any database server with b-tree indexes. And there are a bunch of subtleties, but I will also ignore those for the sake of simplicity.

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I vaguely recall a couple of blog posts recently asking something like “what’s the formula to compute mysqld’s worst-case maximum memory usage?” Various formulas are in wide use, but none of them is fully correct. Here’s why: you can’t write an equation for it.

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These days I’m working with a customer who has an application based entirely on stored routines on MySQL side. Even though I haven’t worked much with stored procedures, I though it’s going to be a piece of cake. In the end - it was, but there’s a catch.

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When your goal is to optimize application performance it is very important to understand what goal do you really have. If you do not have a good understanding of the goal your performance optimization effort may well still bring its results but you may waste a lot of time before you reach same results as you would reach much sooner with focused approach.

The time is critical for many performance optimization tasks not only because of labor associated expenses but also because of the suffering - slow web site means your marketing budget is wasted, customer not completing purchases, users are leaving to competitors, all of this making the time truly critical matter.
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Our customers or prospective customers often ask us how we do a performance audit (it’s our most popular service). I thought I should write a blog post that will both answer their question, so I can just reply “read all about it at this URL” and share our methodology with readers a little bit. This fits well with our philosophy of openness. It also shocks people sometimes — “you’re giving away the golden goose!” Not really. What you hire us for is our experience, not a recipe that anyone can follow.

A full performance audit is far more extensive than this article can cover, and might wander into Apache, networking config, caching layers, etc. Wherever the problem is, we’ll track it down. I won’t talk about that. That’s not because I want to keep secrets from you. To the contrary, I’d love to share it all with you. But that’s a huge job; it will take many pages, and I’m not going to write that much.

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Peter wrote a post a while ago about choosing a good InnoDB log file size.  Not to pick on Peter, but the post actually kind of talks about a lot of things and then doesn’t tell you how to choose a good log file size!  So I thought I’d clarify it a little.

The basic point is that your log file needs to be big enough to let InnoDB optimize its I/O, but not so big that recovery takes a long time.  That much Peter covered really well.  But how do you choose that size? I’ll show you a rule of thumb that works pretty well.

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Do you know that there are two limits about dirty (modified but not flushed to disk) blocks of InnoDB buffer pool? One is the limit of “amount”. The other is the limit of “age”.

– limit of “amount” –

As you know, buffer pool of InnoDB works as write-back cache of its datafiles. If the buffer pool is filled by dirty blocks, InnoDB cannot allocate new blocks without flushing the dirty blocks and the performance would get worse. This is the limit of dirty block “amount”. We can avoid this limit by setting ‘innodb_max_dirty_pages_pct’ smaller or setting the larger buffer pool size. We might be never at a loss about the limit.

The another limit we should understand is limit of dirty block “age”.

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We have written before about the importance of using numeric types as keys, but maybe you’ve inherited a schema that you can’t change or have chosen string types as keys for a specific reason. Either way, the character sets used on joined columns can have a significant impact on the performance of your queries.

Take the following example, using the InnoDB storage engine:
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When optimizing queries for customers, the first thing I do with a slow query is figure out what it’s trying to do. You can’t fully optimize a query unless you know how to consider alternative ways to write it, and you can’t do that unless you know what the query “means.” I frequently run into a situation where I’m forced to stop and ask the developers what they were trying to do with COUNT(). This is database-agnostic, not related to MySQL.

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