October 31, 2014

PERFORMANCE_SCHEMA vs Slow Query Log

A couple of weeks ago, shortly after Vadim wrote about Percona Cloud Tools and using Slow Query Log to capture the data, Mark Leith asked why don’t we just use Performance Schema instead? This is an interesting question and I think it deserves its own blog post to talk about. First, I would say main […]

Increasing slow query performance with the parallel query execution

MySQL and Scaling-up (using more powerful hardware) was always a hot topic. Originally MySQL did not scale well with multiple CPUs; there were times when InnoDB performed poorer with more  CPU cores than with less CPU cores. MySQL 5.6 can scale significantly better; however there is still 1 big limitation: 1 SQL query will eventually use only […]

More on MySQL transaction descriptors optimization

Since my first post on MySQL transaction descriptors optimization introduced in Percona Server 5.5.30-30.2 and a followup by Dimitri Kravchuk, we have received a large number of questions on why the benchmark results in both posts look rather different. We were curious as well, so we tried to answer that question by retrying benchmarks on […]

MySQL performance: Impact of memory allocators (Part 2)

Last time I wrote about memory allocators and how they can affect MySQL performance in general. This time I would like to explore this topic from a bit different angle: What impact does the number of processor cores have on different memory allocators and what difference we will see in MySQL performance in this scenario? […]

Eventual Consistency in MySQL

We’re told that foreign key constraints are the best way to enforce logical referential integrity (RI) in SQL, preventing rows from becoming orphaned.  But then we learn that the enforcement of foreign keys incurs a significant performance overhead.1,2 MySQL allows us to set FOREIGN_KEY_CHECKS=0 to disable enforcement of RI when the overhead is too high.  But […]

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 […]

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 […]

Using flow control functions for performance monitoring queries

I’m not big fan on flow control functions like IF or CASE used in MySQL Queries as they are often abused used to create queries which are poorly readable as well as can hardly be optimized well by MySQL Optimizer. One way I find IF statement very useful is computing multiple aggregates over different set […]

The MySQL optimizer, the OS cache, and sequential versus random I/O

In my post on estimating query completion time, I wrote about how I measured the performance on a join between a few tables in a typical star schema data warehousing scenario. In short, a query that could take several days to run with one join order takes an hour with another, and the optimizer chose […]