Overview Profiling, analyzing and then fixing queries is likely the most oft-repeated part of a job of a DBA and one that keeps evolving, as new features are added to the application new queries pop up that need to be analyzed and fixed. And there are not too many tools out there that can make [...]
Identifying the load with the help of pt-query-digest and Percona Server
December 29, 2011 By 3 Comments
Flexviews – part 3 – improving query performance using materialized views
April 4, 2011 By 20 Comments
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 [...]
Modeling MySQL Capacity by Measuring Resource Consumptions
March 8, 2011 By 1 Comment
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 [...]
UDF -vs- MySQL Stored Function
September 6, 2010 By 8 Comments
Few days ago I was working on a case where we needed to modify a lot of data before pushing it to sphinx – MySQL did not have a function to do the thing so I thought I’ll write MySQL Stored Function and we’ll be good to go. It worked! But not so well really [...]
Estimating Replication Capacity
July 20, 2010 By 4 Comments
It is easy for MySQL replication to become bottleneck when Master server is not seriously loaded and the more cores and hard drives the get the larger the difference becomes, as long as replication remains single thread process. At the same time it is a lot easier to optimize your system when your replication runs [...]
Joining on range? Wrong!
May 17, 2010 By 10 Comments
The problem I am going to describe is likely to be around since the very beginning of MySQL, however unless you carefully analyse and profile your queries, it might easily go unnoticed. I used it as one of the examples in our talk given at phpDay.it conference last week to demonstrate some pitfalls one may [...]
InnoDB: look after fragmentation
November 5, 2009 By 12 Comments
One problem made me puzzled for couple hours, but it was really interesting to figure out what’s going on. So let me introduce problem at first. The table is
1 2 3 4 5 6 7 8 | CREATE TABLE `c` ( `tracker_id` int(10) unsigned NOT NULL, `username` char(20) character set latin1 collate latin1_bin NOT NULL, `time_id` date NOT NULL, `block_id` int(10) unsigned default NULL, PRIMARY KEY (`tracker_id`,`username`,`time_id`), KEY `block_id` (`block_id`) ) ENGINE=InnoDB |
Table has 11864696 rows and takes Data_length: 698,351,616 bytes on disk The problem is that after restoring table from mysqldump, the query that scans data [...]
Air traffic queries in LucidDB
October 26, 2009 By 20 Comments
After my first post Analyzing air traffic performance with InfoBright and MonetDB where I was not able to finish task with LucidDB, John Sichi contacted me with help to setup. You can see instruction how to load data on LucidDB Wiki page You can find the description of benchmark in original post, there I will [...]
Analyzing air traffic performance with InfoBright and MonetDB
October 2, 2009 By 50 Comments
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 [...]
Just do the math!
July 6, 2009 By 4 Comments
One of the most typical reasons for performance and scalability problems I encounter is simply failing to do the math. And these are typically bad one because it often leads to implementing architectures which are not up for job they are intended to solve. Let me start with example to make it clear. Lets say [...]
