November 26, 2014

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 […]

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 […]

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 […]

Debugging problems with row based replication

MySQL 5.1 introduces row based binary logging. In fact, the default binary logging format in GA versions of MySQL 5.1 is ‘MIXED’ STATEMENT*;   The binlog_format  variable can still be changed per sessions which means it is possible that some of your binary log entries will be written in a row-based fashion instead of the […]

Multi Column indexes vs Index Merge

The mistake I commonly see among MySQL users is how indexes are created. Quite commonly people just index individual columns as they are referenced in where clause thinking this is the optimal indexing strategy. For example if I would have something like AGE=18 AND STATE=’CA’ they would create 2 separate indexes on AGE and STATE […]

3 ways MySQL uses indexes

I often see people confuse different ways MySQL can use indexing, getting wrong ideas on what query performance they should expect. There are 3 main ways how MySQL can use the indexes for query execution, which are not mutually exclusive, in fact some queries will use indexes for all 3 purposes listed here.

UNION vs UNION ALL Performance

When I was comparing performance of UNION vs MySQL 5.0 index merge algorithm Sinisa pointed out I should be using UNION ALL instead of simple UNION in my benchmarks, and he was right. Numbers would be different but it should not change general point of having optimization of moving LIMIT inside of union clause being […]

COUNT(*) for Innodb Tables

I guess note number one about MyISAM to Innodb migration is warning what Innodb is very slow in COUNT(*) queries. The part which I often however see omitted is fact it only applies to COUNT(*) queries without WHERE clause. So if you have query like SELECT COUNT(*) FROM USER It will be much faster for […]

ORDER BY … LIMIT Performance Optimization

Suboptimal ORDER BY implementation, especially together with LIMIT is often the cause of MySQL Performance problems. Here is what you need to know about ORDER BY … LIMIT optimization to avoid these problems ORDER BY with LIMIT is most common use of ORDER BY in interactive applications with large data sets being sorted. On many […]