May 22, 2013

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 [...]

The two even more fundamental performance metrics

In a recent blog post, I wrote about four fundamental metrics for system performance analysis. These are throughput, residence time, “weighted time” (the sum of all residence times in the observation period — the terminology is mine for lack of a better name), and concurrency. I derived all of these metrics from two “even more [...]

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 [...]

Using Flexviews – part two, change data capture

In my previous post I introduced materialized view concepts. This post begins with an introduction to change data capture technology and describes some of the ways in which it can be leveraged for your benefit. This is followed by a description of FlexCDC, the change data capture tool included with Flexviews. It continues with an [...]

Shard-Query adds parallelism to queries

Preamble: On performance, workload and scalability: MySQL has always been focused on OLTP workloads. In fact, both Percona Server and MySQL 5.5.7rc have numerous performance improvements which benefit workloads that have high concurrency. Typical OLTP workloads feature numerous clients (perhaps hundreds or thousands) each reading and writing small chunks of data. The recent improvements to [...]

Why you should ignore MySQL’s key cache hit ratio

I have not caused a fist fight in a while, so it’s time to take off the gloves. I claim that somewhere around of 99% of advice about tuning MySQL’s key cache hit ratio is wrong, even when you hear it from experts. There are two major problems with the key buffer hit ratio, and [...]

Index lock and adaptive search – next two biggest InnoDB problems

Running many benchmarks on fast storage (FusionIO, SSDs) and multi-cores CPUs system I constantly face two contention problems. So I suspect it’s going to be next biggest issues to make InnoDB scaling on high-end system. This is also reason why in benchmarks I posted previously CPU usage is only about 50%, leaving other 50% in [...]

Getting around optimizer limitations with an IN() list

There was a discussion on LinkedIn one month ago that caught my eye: Database search by “within x number of miles” radius? Anyone out there created a zipcode database and created a “search within x numer of miles” function ? Thankful for any tips you can throw my way.. J A few people commented that [...]

Upgrading MySQL

Upgrading MySQL Server is a very interesting task as you can approach it with so much different “depth”. For some this is 15 minutes job for others it is many month projects. Why is that ? Performing MySQL upgrade two things should normally worry you. It is Regressions – functionality regressions when what you’ve been [...]