November 22, 2014

Multi-Valued INSERTs, AUTO_INCREMENT & Percona XtraDB Cluster

A common migration path from standalone MySQL/Percona Server to a Percona XtraDB Cluster (PXC) environment involves some measure of time where one node in the new cluster has been configured as a slave of the production master that the cluster is slated to replace. In this way, the new cluster acts as a slave of […]

Tools and tips for analysis of MySQL’s Slow Query Log

MySQL has a nice feature, slow query log, which allows you to log all queries that exceed a predefined about of time to execute. Peter Zaitsev first wrote about this back in 2006 – there have been a few other posts here on the MySQL Performance Blog since then (check this and this, too) but […]

PERFORMANCE_SCHEMA vs Slow Query Log

A couple of weeks ago, shortly after Vadim wrote about Percona Cloud Tools and using Slow Query Log to capture the data, Mark Leith asked why don’t we just use Performance Schema instead? This is an interesting question and I think it deserves its own blog post to talk about. First, I would say main […]

Using per-query variable statements in Percona Server

Percona Server has implemented per-query variable statement support in version 5.6.14-62.0. This feature provides the ability to set variable values only for a certain query, after execution of which the previous values will be restored. Per-query variable values can be set up with the following command:

Example: If we want to increase the sort_buffer_size […]

MySQL Query Patterns, Optimized – Webinar questions followup

On Friday I gave a presentation on “MySQL Query Patterns, Optimized” for Percona MySQL Webinars.  If you missed it, you can still register to view the recording and my slides. Thanks to everyone who attended, and especially to folks who asked the great questions.  I answered as many as we had time for  during the session, but here […]

Identifying the load with the help of pt-query-digest and Percona Server

Overview Profiling, analyzing and then fixing queries is likely the most oft-repeated part of a job of a DBA and one that keeps evolving, as new features are added to the application new queries pop up that need to be analyzed and fixed. And there are not too many tools out there that can make […]

The case for getting rid of duplicate “sets”

The most useful feature of the relational database is that it allows us to easily process data in sets, which can be much faster than processing it serially. When the relational database was first implemented, write-ahead-logging and other technologies did not exist. This made it difficult to implement the database in a way that matched […]

Distributed Set Processing with Shard-Query

Can Shard-Query scale to 20 nodes? Peter asked this question in comments to to my previous Shard-Query benchmark. Actually he asked if it could scale to 50, but testing 20 was all I could due to to EC2 and time limits. I think the results at 20 nodes are very useful to understand the performance: […]

Shard-Query EC2 images available

Infobright and InnoDB AMI images are now available There are now demonstration AMI images for Shard-Query. Each image comes pre-loaded with the data used in the previous Shard-Query blog post. The data in the each image is split into 20 “shards”. This blog post will refer to an EC2 instances as a node from here […]

Flexviews – part 3 – improving query performance using materialized views

Combating “data drift” In my first post in this series, I described materialized views (MVs). An MV is essentially a cached result set at one point in time. The contents of the MV will become incorrect (out of sync) when the underlying data changes. This loss of synchronization is sometimes called drift. This is conceptually […]