Many people use mysqldump –single-transaction to get consistent backup for their Innodb tables without making database read only. In most cases it works, but did you know there are some cases when you can get table entirely missing from the backup if you use this technique ? The problem comes from the fact how MySQL’s [...]
Faster Point In Time Recovery with LVM2 Snaphots and Binary Logs
LVM snapshots is one powerful way of taking a consistent backup of your MySQL databases – but did you know that you can now restore directly from a snapshot (and binary logs for point in time recovery) in case of that ‘Oops’ moment? Let me show you quickly how. This howto assumes that you already [...]
STOP: DELETE IGNORE on Tables with Foreign Keys Can Break Replication
DELETE IGNORE suppresses errors and downgrades them as warnings, if you are not aware how IGNORE behaves on tables with FOREIGN KEYs, you could be in for a surprise. Let’s take a table with data as example, column c1 on table t2 references column c1 on table t1 – both columns have identical set of rows for [...]
Identifying the load with the help of pt-query-digest and Percona Server
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 [...]
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 [...]
When EXPLAIN estimates can go wrong!
I have been working with a few customer cases and one interesting case popped up. The customer was facing a peculiar problem where the rows column in the EXPLAIN output of the query was totally off. The actual number of rows was 18 times more than the number of rows reported by MySQL in the [...]
Distributed Set Processing with Shard-Query
Can Shard-Query scale to 20 nodes? Peter asked this question in comments to to my previous Shard-Query benchmark. Actually he asked if it could scale to 50, but testing 20 was all I could due to to EC2 and time limits. I think the results at 20 nodes are very useful to understand the performance: [...]
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 [...]
Flexviews – part 3 – improving query performance using materialized views
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 [...]
How well does your table fits in innodb buffer pool ?
Understanding how well your tables and indexes fit to buffer pool are often very helpful to understand why some queries are IO bound and others not – it may be because the tables and indexes they are accessing are not in cache, for example being washed away by other queries. MySQL Server does not provide [...]

