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 [...]
Should we give a MySQL Query Cache a second chance ?
April 10, 2011 By 64 Comments
Over last few years I’ve been suggesting more people to disable Query Cache than to enable it. It can cause contention problems as well as stalls and due to coarse invalidation is not as efficient as it could be. These are however mostly due to neglect Query Cache received over almost 10 years, with very [...]
Flexviews – part 3 – improving query performance using materialized views
April 4, 2011 By 20 Comments
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 [...]
Is VoltDB really as scalable as they claim?
February 28, 2011 By 3 Comments
Before I begin, a disclaimer. VoltDB is not a customer, and did not pay Percona or me to investigate VoltDB’s scalability or publish this blog post. More disclaimers at the end. Short version: VoltDB is very scalable; it should scale to 120 partitions, 39 servers, and 1.6 million complex transactions per second at over 300 [...]
The perils of uniform hardware and RAID auto-learn cycles
November 16, 2010 By 16 Comments
Last night a customer had an emergency in selected machines on a large cluster of quite uniform database servers. Some of the servers were slowing down in a very puzzling way over a short time span (a couple of hours). Queries were taking multiple seconds to execute instead of being practically instantaneous. But nothing seemed [...]
On Good Instrumentation
May 31, 2010 By 1 Comment
In so many cases troubleshooting applications I keep thinking how much more efficient things could be going if only there would be a good instrumentation available. Most of applications out there have very little code to help understand what is going on and if it is there it is frequently looking at some metrics which [...]
Beyond great cache hit ratio
May 19, 2010 By 2 Comments
I worked with application recently which has great memcached hit ratio – over 99% but yet still has average page response time over 500ms. Reason ? There are hundreds memcached gets and even though they have some 0.4ms response time they add up to add hundreds of ms to the total response time.
Why you should ignore MySQL’s key cache hit ratio
February 28, 2010 By 28 Comments
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 [...]
