September 16, 2014

Checking the subset sum set problem with set processing

Hi, Here is an easy way to run the subset sum check from SQL, which you can then distribute with Shard-Query:

Notice there is no 16 in the list. We did not pass the check. There are enough 15s though. The distinct value count for each item in the output set, must at least […]

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 […]

MySQL Partitioning – can save you or kill you

I wanted for a while to write about using MySQL Partitioning for Performance Optimization and I just got a relevant customer case to illustrate it. First you need to understand how partitions work internally. Partitions are on the low level are separate table. This means when you’re doing lookup by partitioned key you will look […]

Getting History of Table Sizes in MySQL

One data point which is very helpful but surprisingly few people have is the history of the table sizes. Projection of data growth is very important component for capacity planning and simply watching the growth of space used on partition is not very helpful. Now as MySQL 5.0+ has information schema collecting and keeping this […]

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 […]

Statistics of InnoDB tables and indexes available in xtrabackup

If you ever wondered how big is that or another index in InnoDB … you had to calculate it yourself by multiplying size of row (which I should add is harder in the case of a VARCHAR – since you need to estimate average length) on count of records. And it still would be quite […]

Beware of MySQL Data Truncation

Here is nice gotcha which I’ve seen many times and which can cause just a minefield for many reasons. Lets say you had a system storing articles and you use article_id as unsigned int. As the time goes and you see you may get over 4 billions of articles you change the type for article_id […]

Testing InnoDB “Barracuda” format with compression

New features of InnoDB – compression format and fast index creation sound so promising so I spent some time to research time and sizes on data we have on our production. The schema of one of shards is