Have you wanted to compress only certain types of columns in a table while leaving other columns uncompressed? While working on a customer case this week I saw an interesting problem where a table had many heavily utilized TEXT fields with some read queries exceeding 500MB (!!), and stored in a 100GB table. In this [...]
Improved InnoDB fast index creation
One of the serious limitations in the fast index creation feature introduced in the InnoDB plugin is that it only works when indexes are explicitly created using ALTER TABLE or CREATE INDEX. Peter has already blogged about it before, here I’ll just briefly reiterate other cases that might benefit from that feature: when ALTER TABLE [...]
A recovery trivia or how to recover from a lost ibdata1 file
A few day ago, a customer came to Percona needing to recover data. Basically, while doing a transfer from one SAN to another, something went wrong and they lost the ibdata1 file, where all the table meta-data is stored. Fortunately, they were running with innodb_file_per_table so the data itself was available. What they could provide [...]
Connecting orphaned .ibd files
There are two ways InnoDB can organize tablespaces. First is when all data, indexes and system buffers are stored in a single tablespace. This is typicaly one or several ibdata files. A well known innodb_file_per_table option brings the second one. Tables and system areas are split into different files. Usually system tablespace is located in [...]
Shard-Query EC2 images available
Infobright and InnoDB AMI images are now available There are now demonstration AMI images for Shard-Query. Each image comes pre-loaded with the data used in the previous Shard-Query blog post. The data in the each image is split into 20 “shards”. This blog post will refer to an EC2 instances as a node from here [...]
Recover BLOB fields
For a long time long types like BLOB, TEXT were not supported by Percona InnoDB Recovery Tool. The reason consists in a special way InnoDB stores BLOBs. An InnoDB table is stored in a clustered index called PRIMARY. It must exist even if a user hasn’t defined the primary index. The PRIMARY index pages are [...]
A workaround for the performance problems of TEMPTABLE views
MySQL supports two different algorithms for views: the MERGE algorithm and the TEMPTABLE algorithm. These two algorithms differ greatly. A view which uses the MERGE algorithm can merge filter conditions into the view query itself. This has significant performance advantages over TEMPTABLE views. A view which uses the TEMPTABLE algorithm will have to compute the [...]
Joining on range? Wrong!
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 [...]
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 [...]
Multi Column indexes vs Index Merge
The mistake I commonly see among MySQL users is how indexes are created. Quite commonly people just index individual columns as they are referenced in where clause thinking this is the optimal indexing strategy. For example if I would have something like AGE=18 AND STATE=’CA’ they would create 2 separate indexes on AGE and STATE [...]

