May 22, 2013

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 [...]

Fishing with dynamite, brought to you by the randgen and dbqp

I tend to speak highly of the random query generator as a testing tool and thought I would share a story that shows how it can really shine. At our recent dev team meeting, we spent approximately 30 minutes of hack time to produce test cases for 3 rather hard to duplicate bugs. Of course, [...]

SELECT UNION Results INTO OUTFILE

Here’s a quick tip I know some of us has overlooked at some point. When doing SELECT … UNION SELECT, where do you put the the INTO OUTFILE clause? On the first SELECT, on the last or somewhere else? The manual has the answer here, to quote: Only the last SELECT statement can use INTO [...]

Using any general purpose computer as a special purpose SIMD computer

Often times, from a computing perspective, one must run a function on a large amount of input. Often times, the same function must be run on many pieces of input, and this is a very expensive process unless the work can be done in parallel. Shard-Query introduces set based processing, which on the surface appears [...]

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 turbo charges Infobright community edition (ICE)

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 [...]

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 [...]

Moving Subtrees in Closure Table Hierarchies

Many software developers find they need to store hierarchical data, such as threaded comments, personnel org charts, or nested bill-of-materials. Sometimes it’s tricky to do this in SQL and still run efficient queries against the data. I’ll be presenting a webinar for Percona on February 28 at 9am PST. I’ll describe several solutions for storing [...]

Moving from MyISAM to Innodb or XtraDB. Basics

I do not know if it is because we’re hosting a free webinar on migrating MyISAM to Innodb or some other reason but recently I see a lot of questions about migration from MyISAM to Innodb. Webinar will cover the process in a lot more details though I would like to go over basics in [...]

Shard-Query adds parallelism to queries

Preamble: On performance, workload and scalability: MySQL has always been focused on OLTP workloads. In fact, both Percona Server and MySQL 5.5.7rc have numerous performance improvements which benefit workloads that have high concurrency. Typical OLTP workloads feature numerous clients (perhaps hundreds or thousands) each reading and writing small chunks of data. The recent improvements to [...]