Over 200 attendees attended last week’s Percona Live NY event. This year we structured event different than in 2011 with Tutorial Day allowing for in-depth 3 hour presentations for those looking to dive deep into specific topics. We also added an Expo Hall which allowed a lot of MySQL Ecosystem participant to meet their prospective [...]
linux.conf.au 2012 roundup
January 24, 2012 By Leave a Comment
I spent last week at linux.conf.au in Ballarat, Victoria (that’s the Victoria in Australia, not wherever else there may be one) which is only a pleasant two hour drive from my home town of Melbourne (Australia, not Florida). I sent an email internally to our experts detailing bits of the conference that may interest them [...]
Is your MySQL Application having Busy IO by Oracle Measures ?
January 8, 2012 By 1 Comment
Preparing Choosing Storage Systems for MySQL talk for Percona Live in Washington,DC I ran into great paper called Sane SAN 2010 by James Morle from Scale Abilities – and Oracle consulting company. It is worth to read for variety of reason yet for this post I wanted to mention what James calls “Busy” Oracle database [...]
MySQL performance on EC2/EBS versus RDS
August 4, 2011 By 11 Comments
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
June 9, 2011 By 13 Comments
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
May 16, 2011 By Leave a Comment
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
May 16, 2011 By 5 Comments
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
May 14, 2011 By 27 Comments
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
May 11, 2011 By 2 Comments
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)
May 6, 2011 By 23 Comments
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 [...]
