I recently gave a presentation at the New York Effective MySQL Meetup on the new features of, and some of the compelling reasons to upgrade to MySQL 5.5. There are also a number of new MySQL variables that can have a dramatic effect on performance in a highly transactional environment, innodb_buffer_pool_instances and innodb_purge_threads are just two to consider.

For more information on all the new variables, status, reserved words and benchmarks of new features you can Download Presentation Slides.

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There are a number of significant new features in MySQL 5.5 including semi-synchronous replication, SIGNAL and RESIGNAL, the PERFORMANCE_SCHEMA, additional STATUS variables, new partitioning options, different default storage engine, better UTF8 support and removal of deprecated functions just to list key considerations.

However some of the performance improvements are worth the investment of time. For a high concurrency InnoDB environment one new configuration alone can provide a …

This week we post th' audio from th' closin' keynote o' Percona Live by Harrison Fisk o' Facebook, "MySQL at Facebook, Current and Future", I'll warrant ye. There is no ear candy this week.

On July 12th at 9 AM PST I will be giving a webinar about performance implications for Hibernate and Connector/J. If you cannot attend at this time, a recorded session will be available soon after the webinar.

MySQL is not only about LAMP, and a lot of people use it from Java apps. For some of those, Hibernate is the persistent framework of choice. This webinar will discuss the performance implications of using Hiberate to manage persistence with a MySQL backend, and also some more broad implications for Connector/J  that will apply for anyone using MySQL from Java.

Topics will include:
* Very brief overview of ORMs and Hibernate
* Fetch strategies
* Lazyness
* Manually written SQL
* Concurrency
* Concurrency-related config options for Connector/J

You may register …

Many people consider this information in the MySQL Query Execution Plan (QEP) to indicate that the referenced table is using an index. It actually means that ONLY the index is used. This can for larger and more frequent queries provide a significant boost.

In a recent example, adding an index dropped a query from 190ms to 6ms. However adding a better index dropped that 6ms query to 1.2ms. When executed 100s/1000s of times per second these millisecond improvements can have a huge benefit in greater scalability. While people often tune slow running queries, in a well tuned system shaving milliseconds of queries, in this example making 6ms query 80% better is a far greater improvement.

You can get a detailed explanation of how to identify, create and verify covering indexes from my Percona Live presentation Improving performance …

In the first part of this article I have showed how I align IO, now I want to share results of the benchmark that I have been running to see how much benefit can we get from a proper IO alignment on a 4-disk RAID1+0 with 64k stripe element. I haven’t been running any benchmarks in a while so be careful with my results and forgiving to my mistakes

The environment

Here is the summary of the system I have been running this on (for brevity I have removed some irrelevant information):

# Aspersa System Summary Report ##############################
    Platform | Linux
     Release | Ubuntu 10.04.2 LTS (lucid)
      Kernel | 2.6.32-31-server
Architecture | CPU = 64-bit, OS = 64-bit
# Processor …

The most basic and most oft-repeated task that a DBA has to accomplish is to look at slow logs and filter out queries that are suboptimal, that consume lots of unnecessary resources and that hence slow down the database server. This post looks at why and how VIEWs can help against such suboptimal operations.

There are multiple metrics that are really useful for read workload analysis, that should all be tracked and looked at in performance-critical environments.

The most commonly used is of course Questions (or ‘Queries’, ‘COM_Select’) – this is probably primary finger-pointing metric that can be used in communication with different departments (“why did your qps go up by 30%?”) – it doesn’t always reveal actual cost, it can be increase of actual request rates, it can be new feature, it can be fat fingers error somewhere in the code or improperly handled cache failure.

Another important to note is Connections – MySQL’s costly bottleneck. Though most of users won’t be approaching ~10k/s area – at that point connection pooling starts actually making sense – it is worth to check for other reasons, such as “maybe we connect when we shouldn’t”, or needlessly reconnect, or …

Some time ago I wrote about MySQL 5.5 semi-synchronous replication. Since then, I have wanted to benchmark the overhead of semi-synchronous replication with a decent server. Now the occasion presented itself, thanks to some related business that I had to benchmark, and thus I did a few simple runs with and without semi-synchronous replication enabled, to see the impact of this feature on performance. If you haven't read the article on semi-synchronous replication, the bottom line is that, with this feature enabled, the master waits until at least one slave has acknowledged receipt for the data before returning a positive result to the client. This means that for each commit there are two network calls between master and slave. My gut feeling was that this feature would be costly in terms of query response time, although I was not prepared to …

Shard-Query is an open source tool kit which helps improve the performance of queries against a MySQL database by distributing the work over multiple machines and/or multiple cores. This is similar to the divide and conquer approach that Hive takes in combination with Hadoop. Shard-Query applies a clever approach to parallelism which allows it to significantly improve the performance of queries by spreading the work over all available compute resources. In this test, Shard-Query averages a nearly 6x (max over 10x) improvement over the baseline, as shown in the following graph:

One significant advantage of Shard-Query over Hive is that it works with existing MySQL data sets and queries. Another advantage is that it works with all MySQL …