April 17, 2014

Increasing slow query performance with the parallel query execution

MySQL and Scaling-up (using more powerful hardware) was always a hot topic. Originally MySQL did not scale well with multiple CPUs; there were times when InnoDB performed poorer with more  CPU cores than with less CPU cores. MySQL 5.6 can scale significantly better; however there is still 1 big limitation: 1 SQL query will eventually use only […]

Schema Design in MongoDB vs Schema Design in MySQL

For people used to relational databases, using NoSQL solutions such as MongoDB brings interesting challenges. One of them is schema design: while in the relational world, normalization is a good way to start, how should we design our collections when creating a new MongoDB application? Let’s see with a simple example how we would create […]

MySQL 5.6 vs MySQL 5.5 and the Star Schema Benchmark

So far most of the benchmarks posted about MySQL 5.6 use the sysbench OLTP workload.  I wanted to test a set of queries which, unlike sysbench, utilize joins.  I also wanted an easily reproducible set of data which is more rich than the simple sysbench table.  The Star Schema Benchmark (SSB) seems ideal for this. […]

How to convert MySQL’s SHOW PROFILES into a real profile

SHOW PROFILES shows how much time MySQL spends in various phases of query execution, but it isn’t a full-featured profile. By that, I mean that it doesn’t show similar phases aggregated together, doesn’t sort them by worst-first, and doesn’t show the relative amount of time consumed. I’ll profile the “nicer_but_slower_film_list” included with the Sakila sample […]

Solving INFORMATION_SCHEMA slowness

Many of us find INFORMATION_SCHEMA painfully slow to work it when it comes to retrieving table meta data. Many people resort to using file system tools instead to find for example how much space innodb tables are using and things like it. Besides being just slow accessing information_schema can often impact server performance dramatically. The […]

Color code your performance numbers

When analyzing how good or bad response time is it is not handy to look at the averages, min or max times – something what is easily computed using built in aggregate functions. We most likely would like to see some percentile numbers – 95 percentile or 99 percentile. The problem is computing these in […]

Extending Index for Innodb tables can hurt performance in a surprising way

One schema optimization we often do is extending index when there are queries which can use more key part. Typically this is safe operation, unless index length increases dramatically queries which can use index can also use prefix of the new index are they ? It turns there are special cases when this is not […]

Analyzing air traffic performance with InfoBright and MonetDB

Accidentally me and Baron played with InfoBright (see http://www.mysqlperformanceblog.com/2009/09/29/quick-comparison-of-myisam-infobright-and-monetdb/) this week. And following Baron’s example I also run the same load against MonetDB. Reading comments to Baron’s post I tied to load the same data to LucidDB, but I was not successful in this. I tried to analyze a bigger dataset and I took public […]

Goal driven performance optimization

When your goal is to optimize application performance it is very important to understand what goal do you really have. If you do not have a good understanding of the goal your performance optimization effort may well still bring its results but you may waste a lot of time before you reach same results as […]

Computing 95 percentile in MySQL

When doing performance analyzes you often would want to see 95 percentile, 99 percentile and similar values. The “average” is the evil of performance optimization and often as helpful as “average patient temperature in the hospital”. Lets set you have 10000 page views or queries and have average response time of 1 second. What does […]