October 24, 2014

Percona Server 5.5.13-20.4 Stable Release

Percona is glad to announce the release of Percona Server 5.5.13-20.4 on July 1st, 2011 (Downloads are available here and from the Percona Software Repositories). Based on MySQL 5.5.13, Percona Server Percona Server 5.5.13-20.4 is now the current stable release in the 5.5 series. All of Percona’s software is open-source and free, all the details of the […]

Performance Schema tables stats

My previous benchmark on Performance Schema was mainly in memory workload and against single tables. Now after adding multi-tables support to sysbench, it is interesting to see what statistic we can get from workload that produces some disk IO. So let’s run sysbench against 100 tables, each 5000000 rows (~1.2G ) and buffer pool 30G. […]

Performance Schema overhead

As continuation of my CPU benchmarks it is interesting to see what is scalability limitation in MySQL 5.6.2, and I am going to check that using PERFORMANCE SCHEMA, but before that let’s estimate what is potential overhead of using PERFORMANCE SCHEMA. So I am going to run the same benchmarks (sysbench read-only and read-write) as […]

Drop table performance

There have been recent discussions about DROP TABLE performance in InnoDB. (You can refer to Peter’s post http://www.percona.com/blog/2011/02/03/performance-problem-with-innodb-and-drop-table/ and these bug reports: http://bugs.mysql.com/bug.php?id=51325 and http://bugs.mysql.com/bug.php?id=56332.) It may not sound that serious, but if your workload often uses DROP TABLE and you have a big buffer pool, it may be a significant issue. This can get […]

Flexviews – part 3 – improving query performance using materialized views

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 […]

MySQL on Amazon RDS part 1: insert performance

Amazon’s Relational Database Service (RDS) is a cloud-hosted MySQL solution. I’ve had some clients hitting performance limitations on standard EC2 servers with EBS volumes (see SSD versus EBS death match), and one of them wanted to evaluate RDS as a replacement. It is built on the same technologies, but the hardware and networking are supposed […]

Death match! EBS versus SSD price, performance, and QoS

Is it a good idea to deploy your database into the cloud? It depends. I have seen it work well many times, and cause trouble at other times. In this blog post I want to examine cloud-based I/O. I/O matters a lot when a) the database’s working set is bigger than the server’s memory, or […]

Write performance on Virident tachIOn card

This is crosspost from http://www.percona.com/blog/. Disclaimer: The benchmarks were done as part of our consulting practice, but this post is totally independent and fully reflects our opinion. One of the biggest problems with solid state drives is that write performance may drop significantly with decreasing free space. I wrote about this before (http://www.percona.com/blog/2010/07/17/ssd-free-space-and-write-performance/), using a […]

A workaround for the performance problems of TEMPTABLE views

MySQL supports two different algorithms for views: the MERGE algorithm and the TEMPTABLE algorithm. These two algorithms differ greatly. A view which uses the MERGE algorithm can merge filter conditions into the view query itself. This has significant performance advantages over TEMPTABLE views. A view which uses the TEMPTABLE algorithm will have to compute the […]

Analyzing air traffic performance with InfoBright and MonetDB

Accidentally me and Baron played with InfoBright (see http://www.percona.com/blog/2009/09/29/quick-comparison-of-myisam-infobright-and-monetdb/) this week. And following Baron’s example I also run the same load against MonetDB. Reading comments to Baron’s post I tied to load the same data to LucidDB, but I was not successful in this. I tried to analyze a bigger dataset and I took public […]