April 17, 2014

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 […]

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 […]

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 don’t want to shard.

Note: This blog post is part 1 of 4 on building our training workshop.

The Percona training workshop will not cover sharding. If you follow our blog, you’ll notice we don’t talk much about the subject; in some cases it makes sense, but in many we’ve seen that it causes architectures to be prematurely complicated.

So let me state it: You don’t want to shard.

Optimize everything else first, and then if performance still isn’t good enough, it’s time to take a very bitter medicine. The reason you need to shard basically comes down to one of these two reasons

Impossible – possible, moving InnoDB tables between servers

This is probably the feature I missed most from early days when I started to use InnoDB instead of MyISAM. Since that I figured out how to survive without it, but this is first question I hear from customers who migrated from MyISAM to InnoDB – can I just copy .ibd files from one server […]

Researching your MySQL table sizes

I posted a simple INFORMATION_SCHEMA query to find largest tables last month and it got a good response. Today I needed little modifications to that query to look into few more aspects of data sizes so here it goes:

Sharding and Time Base Partitioning

For large number of online applications once you implemented proper sharding you can consider your scaling problems solved – by getting more and more hardware you can grow. As I recently wrote it however does not mean it is the most optimal way by itself to do things. The “classical” sharding involves partitioning by user_id,site_id […]

ScaleArc: Benchmarking with sysbench

ScaleArc recently hired Percona to perform various tests on its database traffic management product. This post is the outcome of the benchmarks carried out by Uday Sawant (ScaleArc) and myself. You can also download the report directly as a PDF here. The goal of these benchmarks is to identify the potential overhead of the ScaleArc […]