October 23, 2014

Modeling MySQL Capacity by Measuring Resource Consumptions

There are many angles you can look at the system to predict in performance, the model baron has published for example is good for measuring scalability of the system as concurrency growths. In many cases however we’re facing a need to answer a question how much load a given system can handle when load is […]

MySQL-Memcached or NOSQL Tokyo Tyrant – part 1

All to often people force themselves into using a database like MySQL with no thought into whether if its the best solution to there problem. Why?  Because their other applications use it, so why not the new application?  Over the past couple of months I have been doing a ton of work for clients who […]

Analyzing air traffic performance with InfoBright and MonetDB

Accidentally me and Baron played with InfoBright (see http://www.percona.com/blog/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 […]

High-Performance Click Analysis with MySQL

We have a lot of customers who do click analysis, site analytics, search engine marketing, online advertising, user behavior analysis, and many similar types of work.  The first thing these have in common is that they’re generally some kind of loggable event. The next characteristic of a lot of these systems (real or planned) is […]

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 […]

How adding another table to JOIN can improve performance ?

JOINs are expensive and it most typical the fewer tables (for the same database) you join the better performance you will get. As for any rules there are however exceptions The one I’m speaking about comes from the issue with MySQL optimizer stopping using further index key parts as soon as there is a range […]

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 […]

MySQL File System Fragmentation Benchmarks

Few days ago I wrote about testing writing to many files and seeing how this affects sequential read performance. I was very interested to see how it shows itself with real tables so I’ve got the script and ran tests for MyISAM and Innodb tables on ext3 filesystem. Here is what I found:

MySQL EXPLAIN limits and errors.

Running EXPLAIN for problematic queries is very powerful tool for MySQL Performance optimization. If you’ve been using this tool a lot you probably noticed it is not always provide adequate information. Here is list of things you may wish to watch out. EXPLAIN can be wrong – this does not happen very often but it […]