MySQL Performance Blog

There are some tools we commonly use doing performance review and optimization and we often ask each other where that particular stuff is located on the web or what is exactly name of the command what does that.

Initially I thought creating internal Percona Wiki page, but thought there is no reason this information should not be public instead.

So now you can find our favorite MySQL Performance Review Tools on the site.

MySQL Server does not require you to specify name of the index if you're running ALTER TABLE statement - it is optional. Though what might be good practical reasons to specify the key name or omit ?

Things what you should be looking at is how MySQL names indexes automatically as well as what maintaining the indexes.

Lets first speak about naming. If you do not specify index name MySQL will name index by the first column of index created, if there is such index already it will add numeric prefix to it, for example:

Much has been written about the new InnoDB pluggable storage engine, which Innobase released at the MySQL conference last month.

Sometimes due to programming error or due to very complex query you can get your PHP script running too long, well after user stopped waiting for the page to render and went browsing other sites.
Looking at Server-Status I've seen scripts executing for hours sometimes which is obviously the problem - they take Apache Slot, MySQL Connection and other resources.

I had discussion today who was thinking Apache would kill the script after "Timeout" specified in Apache configuration is reached - this was not my experience so I decided to run couple of tests to check it.

Quite frequently I would log in to customers system and find MySQL using too much memory. I would look at memory consumed by Innodb (it is often higher than innodb_buffer_pool_size) substract memory used by other global buffers such as query_cache_size and key_buffer and will in many cases see some mysterous memory which I can’t really explain. It can be several Gigabytes accounting for over 50% of memory usage of MySQL in some cases, though typically it is much smaller fraction.

Looking at Kaj’s Blog Annoucement MySQL has pulled back on the plans to release portions of the servers as Open Source only.

I am extremely happy to hear these news ! This is good for MySQL as a company, MySQL customers and MySQL users.

I’m hoping Community feedback was serious contributer to this decision, though I know there were a lot of Internal discussions as well. In any case this sends a great message to community - Speak up and you may be heard.

In the previous post I mentioned a way I use to preload Clustered Index (data) for Innodb tables. Though I thought this topic would benefit from a bit more information.

Sun is aggressively pushing T2000 as Scalable MySQL Platforms, and indeed it is Scalable in terms of high concurrency workloads - it is able to execute a lot of concurrent threads and so speed gain from 1 thread to say 32 thread will be significant.

But thing a lot of people miss is - Being Scalable is Not Enough - you need to scale from reasonable base to claim the good performance, and this is where T2000 performs subpar in many cases.

If you’ve missed our presentations on MySQL Users Conference you can catch up now by taking a look at the slides, which are now published in presentations section of our company web site. You can also find a lot of old presentations in the same location.

Enjoy !

Entry posted by peter | No comment

In my post on estimating query completion time, I wrote about how I measured the performance on a join between a few tables in a typical star schema data warehousing scenario.

In short, a query that could take several days to run with one join order takes an hour with another, and the optimizer chose the poorer of the two join orders. Why is one join order so much slower than the other, and why did the optimizer not choose the faster one? That's what this post is about.

I read very nice post by Matt today and it has many good insights though I can’t say I agree on all points.