April 20, 2014

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 […]

MySQL Limitations Part 3: Subqueries

This is the third in a series on what’s seriously limiting MySQL in certain circumstances (links: part 1, 2). This post is about subqueries, which in some cases execute outside-in instead of inside-out as users expect.

EXPLAIN EXTENDED can tell you all kinds of interesting things

While many people are familiar with the MySQL EXPLAIN command, fewer people are familiar with “extended explain” which was added in MySQL 4.1 EXPLAIN EXTENDED can show you what the MySQL optimizer does to your query. You might not know this, but MySQL can dramatically change your query before it actually executes it. This process […]

A workaround for the performance problems of TEMPTABLE views

MySQL supports two different algorithms for views: the MERGE algorithm and the TEMPTABLE algorithm. These two algorithms differ greatly. A view which uses the MERGE algorithm can merge filter conditions into the view query itself. This has significant performance advantages over TEMPTABLE views. A view which uses the TEMPTABLE algorithm will have to compute the […]

Missing Data – rows used to generate result set

As Baron writes it is not the number of rows returned by the query but number of rows accessed by the query will most likely be defining query performance. Of course not all row accessed are created equal (such as full table scan row accesses may be much faster than random index lookups row accesses […]

The MySQL optimizer, the OS cache, and sequential versus random I/O

In my post on estimating query completion time, I wrote about how I measured the performance on a join between a few tables in a typical star schema data warehousing scenario. In short, a query that could take several days to run with one join order takes an hour with another, and the optimizer chose […]

Heikki Tuuri Innodb answers – Part I

Its almost a month since I promised Heikki Tuuri to answer Innodb Questions. Heikki is a busy man so I got answers to only some of the questions but as people still poking me about this I decided to publish the answers I have so far. Plus we may get some interesting follow up questions […]