Performance Schema is used extensively both internally and within the MySQL community, and I expect even more usage with the new SYS Schema and the Performance Schema enhancements in 5.7. Performance Schema is the single best tool available for monitoring MySQL Server internals and execution details at a lower level. Having said that, we are also no stranger to the fact that any monitoring tool comes with an additional cost to performance. Hence It has always been an important question to find out just how much it costs us when Performance …

When optimizing queries and investigating performance issues, MySQL comes with built in support for profiling queries aka

SET profiling = 1;

 . This is already awesome and simple to use, but why the PERFORMANCE_SCHEMA alternative?

Because profiling will be removed soon (already deprecated on MySQL 5.6 ad 5.7); the built-in profiling capability can only be enabled per session. This means that you cannot capture profiling information for queries running from other connections. If you are using Percona Server, the profiling option for log_slow_verbosity is a nice alternative, unfortunately, not everyone is using Percona Server.

Now, for a quick demo: I execute a …

I recently hosted a webinar introducing MySQL Cluster and then looking into what’s new in the latest version (MySQL Cluster 7.4) in some more detail. The replay of the MySQL Cluster 7.4 webinar is now available here. Alternatively if just want to skim through the charts then scroll down.

Abstract

MySQL Cluster powers the subscriber databases of major communication services providers as well as next generation web, cloud, social and mobile applications. It is designed to deliver:

• Real-time, in-memory performance for both OLTP and analytics workloads
• Linear scale-out for both reads and writes
• 99.999% High Availability
• Transparent, cross-shard transactions …

The MySQL 5.6.4 release introduced support for fractional values within the temporal datatypes: TIME, DATETIME, and TIMESTAMP. Hence the storage requirement and encoding differs for them in comparison to older (5.5 and earlier) temporal datatypes. The storage format for the temporal datatypes in the old format are not space efficient either, and recreating tables having both the new and old formats can be a long and tedious process. For these reasons, we wanted to make it easier for users to identify precisely which tables, if any, need to be upgraded.

In my previous blog post, where we looked at the process of upgrading old MySQL-5.5 format temporals to the MySQL-5.6 format, there was the …

A couple of days ago I wrote about one of my forays into MDX land (Retrieving denormalized tabular results with MDX). The topic of that post was how to write MDX so as to retrieve the kind of flat, tabular results one gets from SQL queries. An essential point of that solution was the MDX Ancestor() function.

I stumbled upon the topic of my previous blogpost while I was researching something else entirely. Creating flat tables and looking up individual ancestors is actually a rather specific application of a much more general solution I found initially. Pivot tables and the "Show Parents" functionalityGUI OLAP tools typically offer a pivot table query interface. They let you drag and drop measures and dimension items, like …

Data inconsistencies in replication environments are a pretty common. There are lots of posts that explain how to fix those using pt-table-checksum and pt-table-sync. Usually we only care about the data but from time to time we receive this question in support:

How can I check the table definition consistency between servers?

Replication also allow us to have different table definition between master and slaves. For example, there are some cases that you need some indexes on slaves for querying purposes but are not really needed on the master. There are some other cases where those differences are just a mistake that needs to be fixed.

…

The slides for my Improving Performance With Better Indexes presentation at Percona Live 2015 MySQL Conference and Expo are now available.

In this presentation I discuss how to identify, review and analyze SQL statements in order to create better indexes for your queries. This includes understanding the EXPLAIN syntax and how to create and identify covering and partial column indexes.

Download PDF Presentation

This presentation is based on the work with a customer showing the 95% improvement of a key 15 table join query running 15,000 QPS in a 25 billion SQL statements per day …

It is my pleasure to announce that Percona has acquired Tokutek and will take over development and support for TokuDB® and TokuMX™ as well as the revolutionary Fractal Tree® indexing technology that enables those products to deliver improved performance, reliability and compression for modern Big Data applications.

At Percona we have been working with the Tokutek team since 2009, helping to improve performance and scalability. The TokuDB storage engine has been available for Percona Server for about a year, so joining forces is quite a natural step for us.

Fractal Tree indexing technology—developed by years of data science research at MIT, Stony Brook University and Rutgers University—is the new generation data structure which, for many workloads, leapfrogs traditional B-tree technology which was invented in 1972 (over 40 years ago!).  It is also often …

If you haven’t already heard, on the Tuesday morning of the 2015 Percona Live MySQL Conference and Expo it was announced that Tokutek is now part of the Percona family.  This means TokuDB® for MySQL, and TokuMX™ for MongoDB are Percona products now; and that the Tokutek  team is now part of the Percona team.

Percona’s well-deserved reputation for unparalleled customer service and support in the MySQL market makes them the perfect home for Tokutek’s ground-breaking products.  And with the Tokutek acquisition, Percona can expand and extend their activities and offerings into the MongoDB market.

This is a win/win for NoSQL and MySQL fans alike.

More About Tokutek

Tokutek is the company that productized a new and revolutionary form of database indexing designed specifically for modern, Big Data applications.  Based on data science research on new methods for high-performance data …

It is my pleasure to announce that Percona has acquired Tokutek and will take over development and support for TokuDB® and TokuMX™ as well as the revolutionary Fractal Tree® indexing technology that enables those products to deliver improved performance, reliability and compression for modern Big Data applications.

At Percona we have been working with the Tokutek team since 2009, helping to improve performance and scalability. The TokuDB storage engine has been available for Percona Server for about a year, so joining forces is quite a natural step for us.

Fractal Tree indexing technology—developed by years of data science research at MIT, Stony Brook University and Rutgers University—is the new generation data structure which, for many workloads, leapfrogs traditional B-tree technology which was invented in 1972 (over 40 years ago!).  It is also often superior to LSM indexing, especially for mixed workloads.

…