November 21, 2014

Limited disk space? Compact backups with Percona Xtrabackup 2.1

One very interesting feature, “Compact Backup,” is introduced in Percona XtraBackup 2.1. You can run “compact backups” with the  –compact option, which is very useful for those who have limited disk space to keep the MySQL database backup. Now let’s first understand how it works. When we are using –compact option with Innobackupex, it will omit the […]

Benchmarking Percona Server TokuDB vs InnoDB

After compiling Percona Server with TokuDB, of course I wanted to compare InnoDB performance vs TokuDB. I have a particular workload I’m interested in testing – it is an insert-intensive workload (which is TokuDB’s strong suit) with some roll-up aggregation, which should produce updates in-place (I will use INSERT .. ON DUPLICATE KEY UPDATE statements […]

How to recover table structure from InnoDB dictionary

To recover a dropped or corrupt table with Percona Data Recovery Tool for InnoDB you need two things: media with records(ibdata1, *.ibd, disk image, etc.) and a table structure. Indeed, there is no information about the table structure in an InnoDB page. Normally we either recover the structure from .frm files or take it from […]

Edge-case behavior of INSERT…ODKU

A few weeks back, I was working on a customer issue wherein they were observing database performance that dropped through the floor (to the point of an outage) roughly every 4 weeks or so. Nothing special about the environment, the hardware, or the queries; really, the majority of the database was a single table with […]

Logging Deadlock errors

The principal source of information for InnoDB diagnostics is the output of SHOW ENGINE INNODB STATUS but there are some sections that are not very useful. For example, LATEST DETECTED DEADLOCK only shows, as the name implies, the latest error detected. If you have 100 deadlocks per minute you will be able to see only […]

The case for getting rid of duplicate “sets”

The most useful feature of the relational database is that it allows us to easily process data in sets, which can be much faster than processing it serially. When the relational database was first implemented, write-ahead-logging and other technologies did not exist. This made it difficult to implement the database in a way that matched […]

Using any general purpose computer as a special purpose SIMD computer

Often times, from a computing perspective, one must run a function on a large amount of input. Often times, the same function must be run on many pieces of input, and this is a very expensive process unless the work can be done in parallel. Shard-Query introduces set based processing, which on the surface appears […]

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 […]

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 […]

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 […]