April 18, 2014

MySQL server memory usage troubleshooting tips

There are many blog posts already written on topics related to “MySQL server memory usage,” but nevertheless there are some who still get confused when troubleshooting issues associated with memory usage for MySQL. As a Percona support engineer, I’m seeing many issues regularly related to heavy server loads – OR OOM killer got invoked and […]

How to find MySQL queries worth optimizing ?

One question I often get is how one can find out queries which should be optimized. By looking at pt-query-digest report it is easy to find slow queries or queries which cause the large portion of the load on the system but how do we know whenever there is any possibility to make this query […]

MySQL Indexing Best Practices: Webinar Questions Followup

I had a lot of questions on my MySQL Indexing: Best Practices Webinar (both recording and slides are available now) We had lots of questions. I did not have time to answer some and others are better answered in writing anyway. Q: One developer on our team wants to replace longish (25-30) indexed varchars with […]

Testing MySQL column stores

Recently I had the opportunity to do some testing on a large data set against two MySQL column-store storage engines. I’d like to note that this effort was sponsored by Infobright, but this analysis reflects my independent testing from an objective viewpoint. I performed two different types of testing. The first focused on core functionality […]

Using flow control functions for performance monitoring queries

I’m not big fan on flow control functions like IF or CASE used in MySQL Queries as they are often abused used to create queries which are poorly readable as well as can hardly be optimized well by MySQL Optimizer. One way I find IF statement very useful is computing multiple aggregates over different set […]

How rows_sent can be more than rows_examined?

When looking at queries that are candidates for optimization I often recommend that people look at rows_sent and rows_examined values as available in the slow query log (as well as some other places). If rows_examined is by far larger than rows_sent, say 100 larger, then the query is a great candidate for optimization. Optimization could […]

Flexviews is a working scalable database transactional memory example

http://Flexvie.ws fully implements a method for creating materialized views for MySQL data sets. The tool is for MySQL, but the methods are database agnostic. A materialized view is an analogue of software transactional memory. You can think of this as database transactional memory, or as database state distributed over time, but in an easy way […]

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: […]

The two even more fundamental performance metrics

In a recent blog post, I wrote about four fundamental metrics for system performance analysis. These are throughput, residence time, “weighted time” (the sum of all residence times in the observation period — the terminology is mine for lack of a better name), and concurrency. I derived all of these metrics from two “even more […]

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 […]