In addition to being practical, …

There are two PRIMARY directions to sort data in SQL: Ascending (ASC) and Descending DESC.
When these two sort definitions are put together in a single statement a filesort is produced.

Why do we want to avoid filesorts?

Filesorts are bad. 1st they tickle a thread based buffer called sort_buffer_size. Additionally filesorts reads the data twice, unless max_length_for_sort_data limit is reached and as a result the Filesort runs slower to reduce disk I/O. If you want filesorts to run faster at the expense of the disk increase the default max_length_for_sort_data. You can read the filesort algorithm here.

So, here is an example

 CREATE TABLE `ABCD` (
  `A` int(10) unsigned NOT NULL default '0',
  `B` int(10) unsigned NOT NULL default '0',
  `C` int(10) unsigned NOT NULL …

Assume you have a table with about 300 000 rows, and an indexed column ‘col1′ with only 9 distinct values. Now you got a query like ’select * from t1 where col1 = const’. The questions are

- when the index is faster to full table scan and vice versa?
- does MySQL use the optimal plan by default?

These questions became very relevant now that QOT got server access and is able to gather various table metrics including selectivity. Besides index selectivity the threshold value obviously depends on the storage engine used, so for me it is also interesting to see how our PBXT engine compares to others in this aspect. Namely to InnoDB - an engine with similar transactional properties and MyISAM - a very fast engine for read-only scenarios.

For the test I took the …

SELECT Field0[,Field1,Field2,...] FROM TABLE PROCEDURE ANALYSE() is a nice tool to find out more about your table’s columns. Still, it could be improved in a lot of ways, and the stored procedure below is a starting point. It makes use of procedure analyse (though with ‘SELECT * FROM’), and modifies it’s output to include the […]

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At the January 2009 Boston User Group I presented a session on the new partitioning feature in MySQL 5.1. I go through how to define partitions, how partitioning makes queries faster, the different types of partitioning and when to use each type, and the restrictions and limitations of partitioning.

The slides are available at http://www.technocation.org/files/doc/2009_01_Partitioning.pdf. The 380.6 Mb .mov movie (1 hr 16 min) can be played directly in your browser at http://technocation.org/node/671/play or downloaded at http://technocation.org/node/671/download.

Notes:
The partitioning part of the MySQL Manual is at: …

You might be wondering what's been happening with MySQL performance since Sun arrived on the scene. The good news is that we haven't been idle. There's been general recognition that MySQL could benefit from some performance and scalability enhancements, and Sun assembled a cross-organizational team immediately after the acquisition to get started on it. We've enjoyed excellent cooperation between the engineers from both organizations.

This kind of effort is not new for Sun - we've been working with proprietary database companies on performance for years, with source code for each of the major databases on site to help the process. In this case, the fact that the MySQL engineers are working for the same company certainly simplifies a lot of things.

If you'd like to get some insight into what's been happening, a video has just …

You might be wondering what's been happening with MySQL performance since Sun arrived on the scene. The good news is that we haven't been idle. There's been general recognition that MySQL could benefit from some performance and scalability enhancements, and Sun assembled a cross-organizational team immediately after the acquisition to get started on it. We've enjoyed excellent cooperation between the engineers from both organizations.

This kind of effort is not new for Sun - we've been working with proprietary database companies on performance for years, with source code for each of the major databases on site to help the process. In this case, the fact that the MySQL engineers are working for the same company certainly simplifies a lot of things.

If you'd like to get some insight into what's been happening, a video has just …

You might be wondering what's been happening with MySQL performance since Sun arrived on the scene. The good news is that we haven't been idle. There's been general recognition that MySQL could benefit from some performance and scalability enhancements, and Sun assembled a cross-organizational team immediately after the acquisition to get started on it. We've enjoyed excellent cooperation between the engineers from both organizations.

This kind of effort is not new for Sun - we've been working with proprietary database companies on performance for years, with source code for each of the major databases on site to help the process. In this case, the fact that the MySQL engineers are working for the same company certainly simplifies a lot of things.

If you'd like to get some insight into what's been happening, a video has just …

Over the last few weeks I have been doing some work on improving the concurrency performance of PBXT. The last Alpha version (1.0.03) has quite a few problems in this area.

Most of the problems have been with r/w lock and mutex contention but, I soon discovered that MySQL has some serious problems of it's own. In fact, I had to remove some of the bottlenecks in MySQL in order to continue the optimization of PBXT.

The result for simple SELECT performance is shown in the graph below.

Here you can see that the gain is over 60% for 32 or more concurrent threads. Both results show the performance with the newly optimized version of PBXT. The test is running on a 2.16 MHz dual core processor, so I expect an even greater improvement on 4 or 8 cores. The query I ran for this test is …

First some of the things that you need to use and understand

Explain Syntax

Order by Optimization

Group by Optimization

Update: Updated errors.

Now some details that are usually missed. GROUP BY does sorting unless you tell mysql not to. GROUP BY has two optimization methods, loose index scan, and tight index scan.

Loose index scan, scans the entire table index, while tight index scan uses some sort of constraint. For large datasets that are accessed often and require some sort of group by, tight index scans are better.


So how to pick columns to create …