May 23, 2013

MySQL performance: Impact of memory allocators (Part 2)

Last time I wrote about memory allocators and how they can affect MySQL performance in general. This time I would like to explore this topic from a bit different angle: What impact does the number of processor cores have on different memory allocators and what difference we will see in MySQL performance in this scenario? [...]

Troubleshooting MySQL Memory Usage

One of the most painful troubleshooting tasks with MySQL is troubleshooting memory usage. The problem usually starts like this – you have configured MySQL to use reasonable global buffers, such as innodb_buffer_size, key_buffer_size etc, you have reasonable amount of connections but yet MySQL takes much more memory than you would expect, causing swapping or other [...]

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 [...]

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 [...]

Shard-Query turbo charges Infobright community edition (ICE)

Shard-Query is an open source tool kit which helps improve the performance of queries against a MySQL database by distributing the work over multiple machines and/or multiple cores. This is similar to the divide and conquer approach that Hive takes in combination with Hadoop. Shard-Query applies a clever approach to parallelism which allows it to [...]

How InnoDB handles REDO logging

Xaprb (Baron) recently blogged about how InnoDB performs a checkpoint , I thought it might be useful to explain another important mechanism that affects both response time and throughput – The transaction log.

The Doom of Multiple Storage Engines

One of the big “Selling Points” of MySQL is support for Multiple Storage engines, and from the glance view it is indeed great to provide users with same top level SQL interface allowing them to store their data many different way. As nice as it sounds the in theory this benefit comes at very significant [...]

How fast is FLUSH TABLES WITH READ LOCK?

A week or so ago at the MySQL conference, I visited one of the backup vendors in the Expo Hall. I started to chat with them about their MySQL backup product. One of the representatives told me that their backup product uses FLUSH TABLES WITH READ LOCK, which he admitted takes a global lock on [...]

Is your MySQL Server Loaded ?

So you’re running the benchmark/stress test – how do you tell if MySQL server is really loaded ? This looks like the trivial question but in fact, especially when workload consists of simple queries I see the load generation and network really putting a lot less load on MySQL than expected. For example you may [...]