MySQL, like a lot of other software, has many knobs you can tweak. Most of these knobs may affect behaviour, but more importantly most affect the memory usage of the server, so getting these settings right is very important.

Most of MySQL’s memory is really used just as a cache, in one form or another, information that otherwise is on disk. So ensuring you have as large a cache as possible is important. However, making these memory sizes too large will trigger the server to start swapping and possibly can cause it to crash or cause the kernel to kill the process when it runs out of memory.  So that’s something we want to avoid.

Certain settings affect memory allocation on a per connection/thread basis, being bounded by thread_cache_size and max_connections.  If you configure for the worst behaviour (max_connections) you may end up not actually using all the memory you have available, memory which normally could be …

MySQL has two confusingly identical by the first look variables myisam_sort_buffer_size and sort_buffer_size. Thing is that those two confusingly similar variables has absolutely different meanings.

sort_buffer_size is a per-connection variable and do not belongs to any specific storage engine. It doesn’t matter do you use MyISAM or InnoDB – MySQL will allocate sort_buffer_size for every sort (required most of the times for ORDER BY and GROUP BY queries) so increasing it’s value might help speeding up those queries however I would not recommend to change it from the default value unless you are absolutely sure about all the drawbacks. Value for out-of-the-box MySQL-5.1.41 installation on Ubuntu is 2Mb and it’s recommended to keep it that way.

On …

TokuDB® v5.2, the latest version of Tokutek’s flagship storage engine for MySQL and MariaDB, is now available.

This version offers performance enhancements over previous releases, especially for multi-client scale up and point queries, and extends the cases where ALTER TABLE is non-blocking, in particular adding Hot Column Rename.

TokuDB v5.2 maintains all our established advantages: fast trickle load, fast bulk load, fast range queries through clustering indexes, hot schema changes, great compression, no fragmentation, and full MySQL compatibility for ease of installation. See our benchmark page for details.

Multi-client workloads

In TokuDB v5.2, we have reworked our locking scheme to better support multi-client workloads, and as always, we have focused on large databases. How did we do? Let’s check out some …

Disk I/O is still a common source of performance issues, despite modern cloud environments, modern file systems and huge amounts of main memory serving as file system cache. Understanding how well that cache is working is a key task while investigating disk I/O issues. In this post, I’ll show the activity of the ZFS file system Adaptive Replacement Cache (ARC).

There are often more statistics available than you realize (or have been documented), which may certainly be true with the ARC. Apart from showing these statistics, I’ll also show how to extend observability using dynamic tracing (DTrace). These tracing techniques are also applicable to any kernel subsystem. This is an advanced topic, where I’ll sometimes dip into kernel code.

Architecture

For background on the ZFS ARC, see the paper ARC: A Self-Tuning, Low Overhead …

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 …

“I’ve installed MySQL and it doesn’t work fast enough for me”. MySQL server is heart of database driven application (if it uses MySQL as database of course!) and any slowness related to running queries is affecting all application layers.

MySQL server tuning and query slowness hunting are always step by step process and without knowing all the data (SHOW GLOBAL VARIABLES, SHOW GLOBAL STATUS, SHOW TABLE STATUS LIKE ‘tablename’, EXPLAIN details for slow query is just some of the required information) it would be generally a blind guess. But there are still few things which is related to newly installed MySQL server.

If you are using stock MySQL you might need to check memory pool size which MySQL used to load index data to avoid slow IO requests and increase queries speed. Connect to MySQL and fire two queries:

SHOW VARIABLES LIKE 'key_buffer_size';
SHOW VARIABLES LIKE …

This guide will get you up and running with how to identify the bottleneck queries using the excellent tool pt-query-digest. You will learn how to use and analyze the output returned by pt-query-digest. You will also learn some differences between slow query logging in various MySQL versions. Later on in the post I will also show you how to make use of the extra diagnostic data available with Percona Server.

The post Profiling your slow queries using pt-query-digest and some love from Percona Server appeared first on ovais.tariq.

This week, we continue our discussion about MySQL and its forks. We discuss the Percona server and MariaDB.

Percona Live comes to Washington, DC on Wednesday, January 11th, 2012 at the Walter E. Washington Convention Center. There is a 50% discount for students, faculty and staff of educational organizations, and a 35% discount for government employees. http://www.percona.com/live/dc-2012/

Percona Server
Percona software

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Continues from Improving InnoDB memory usage.

Here are some numbers from the fixups described in the above article:

The workload consists of 10 partitioned tables, each one containing 1000 partitions. This means 10’000 InnoDB tables. We truncate the tables, then restart mysqld and run:

1. INSERT a single row into each of the 10 tables
2. SELECT * from each table
3. FLUSH TABLES (this causes the tables to be closed and reopened on the next run)
4. wait for 10 seconds

we repeat the above steps 10 times. Here is the total memory consumption by mysqld with 1GB InnoDB buffer pool during the workload:

In the fixed case (green line) you can clearly see the peaks when the commands 1. – 4. are run …

Last month we did a few improvements in InnoDB memory usage. We solved a challenging issue about how InnoDB uses memory in certain places of the code.

The symptom of the issue was that under a certain workloads the memory used by InnoDB kept growing infinitely, until OOM killer kicked in. It looked like a memory leak, but Valgrind wasn’t reporting any leaks and the issue was not reproducible on FreeBSD – it only happened on Linux (see Bug#57480). Especially the latest fact lead us to think that there is something in the InnoDB memory usage pattern that reveals a nasty side of the otherwise good-natured Linux’s memory manager.

It turned out to be an interesting …