In a month, the 24th of October, Johan Andersson (severalnines.com) and I will be giving a full day tutorial on NDB cluster which will include both presentations and hands-on. Be ready for a fast ramp-up on NDB! Among items covered: – Achitecture – Installation – Loading data – Administration (common procedures) – Node recovery – [...]
MySQL performance on EC2/EBS versus RDS
A while ago I started a series of posts showing benchmark results on Amazon EC2 servers with RAID’ed EBS volumes and MySQL, versus RDS machines. For reasons that won’t add anything to this discussion, I got sidetracked, and then time passed, and I no longer think it’s a good idea to publish those blog posts [...]
Aligning IO on a hard disk RAID – the Benchmarks
In the first part of this article I have showed how I align IO, now I want to share results of the benchmark that I have been running to see how much benefit can we get from a proper IO alignment on a 4-disk RAID1+0 with 64k stripe element. I haven’t been running any benchmarks [...]
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:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | CREATE TABLE `the list` ( `id` bigint(20) NOT NULL AUTO_INCREMENT, `val` bigint(20) NOT NULL DEFAULT '0', PRIMARY KEY (`id`), KEY `id` (`id`) ) ENGINE=MyISAM; SELECT val as `val`, COUNT(DISTINCT (id)) as `cd` FROM test.data as d WHERE val in (-2,-3,-10,15,15,16) GROUP BY val; +-----+----------+----------+ | val | cd | CNT | +-----+----------+----------+ | -10 | 1 | 1 | | -3 | 1 | 1 | | -2 | 1 | 1 | | 15 | 35417088 | 35417088 | +-----+----------+----------+ 5 rows in set (40.20 sec) |
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 [...]
Finding an optimal balance of I/O, CPU, and RAM for MySQL
For a long time I’ve wanted to know how MySQL scales as you add more memory to the server. Vadim recently benchmarked the effects of increasing memory and CPU core count. He looked for a balance between utilizing the hardware as much as possible, limiting the system complexity, and lowering the price-to-performance ratio. The outcome [...]
Distributed set processing performance analysis with ICE 3.5.2pl1 at 20 nodes.
Demonstrating distributed set processing performance Shard-Query + ICE scales very well up to at least 20 nodes This post is a detailed performance analysis of what I’ve coined “distributed set processing”. Please also read this post’s “sister post” which describes the distributed set processing technique. Also, remember that Percona can help you get up and [...]
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 [...]

