In the last six months, things have gotten much busier in the world of MySQL performance. That is, making MySQL and InnoDB scale faster out of the box. This is a great trend and I hope it keeps going. At this point I’m fighting to find enough time to read about what people are doing; I can’t keep up fast enough to actually understand the improvements. That’s also good.

The blogs that are posting the most news and analysis are MySQL Performance Blog, Mikael Ronstrom’s blog, DimitriK’s blog, and Mark Callaghan’s blog.

Lets get down to how the latest version of Waffle Grid performs.

Starting off simple lets look at the difference between the wafflegrid modes. As mentioned before the LRU mode is the “classic” Waffle Grid setup. A page is put into memcached when the page is removed from the buffer pool via the LRU process. When a page is retrieved from memcached it is expired so its no longer valid. In the New “Non-LRU” mode when a page is read from disk, the page is placed in memcached. When a dirty page is flushed to disk, this page is overwritten in memcached. So how do the different modes perform?

The last 24 months has seen the introduction of Map/Reduce functionality into the data processing arena in various forms.  Map/Reduce is a framework for developing scalable data processing functionality, and was popularized by Google (see this earlier post).

Pure players like Hadoop are starting to find their own niche, helped by organizations such as Cloudera.  However there has been a number of for & against arguments relating to Map/Reduce functionality inside the database.

These arguments are now really serving a moot point.  Customers have recognized value in Map/Reduce prompting some (b)leading edge database vendors to introduce such …

In the last six months, things have gotten much busier in the world of MySQL performance. That is, making MySQL and InnoDB scale faster out of the box. This is a great trend and I hope it keeps going. At this point I’m fighting to find enough time to read about what people are doing; I can’t keep up fast enough to actually understand the improvements. That’s also good. The blogs that are posting the most news and analysis are MySQL Performance Blog, Mikael Ronstrom’s blog, DimitriK’s blog, and Mark Callaghan’s blog.

I’m decently familiar with grep so I can usually make it do what I want. I frequently need to search for instance MySQL source code for certain pattern strings, and this makes life so much easier. But Akash pointed out ack to me, which has the specific tagline “better than grep” (has the domain even) and I reckon it does live up to that. Win! It’s written in pure Perl, very easy to install (doesn’t even use CPAN if you don’t want).

It recurses into subdirs by default, while ignoring stuff like revision control and binary files. You can search specific types of files through a symbolic name rather than by specifying regexes. And it has colour highlighting, and simply uses the familiar Perl regexes for its pattern matching rather than funky subsets of which there are many distinct ones…

You should not try to use Hadoop as a “drop-in” replacement of your current (R)DBMS. That said it is still possible to utilize the power of cluster computing while circumventing its weaknesses when it comes to ad-hoc or real-time queries. We use Hadoop as an on-line system tightly integrated with our application and use it for both, long-running analytical queries and ad-hoc style queries.

In the mindset of a “traditional” database engineer one of the biggest concerns about Hadoop, or MapReduce in conjunction with a distributed file system in general, is the lack of indexes. Set aside that the debate “(R)DBMS vs MapReduce” is most of the time superfluous and sometimes almost leads to religious debates, the absence of a thing like an index is one the biggest hurdles you face when migrating data from a traditional DBMS.
Even …

Performance Tuning is a big and exciting task for any MySQL DBA. Sometimes the most difficult thing to decide in a given scenario is where to "dive in!" There are so many areas to consider application-level, query optimization, hardware optimization, software tweaks, MySQL configuration tuning, etc. The list goes on and on...

Many times when a DBA begins to look at performance tuning for a client, the general assumption is that all of the query traffic coming into the database is essential. Unfortunately, that is not always the case!

Recently, our team was profiling a heavily loaded server running thousands of queries per second. This poor server was really taxed! It's load was consistently approaching 20.0 on the Linux server and increasing week by week at a phenomenal rate. Slaves were regularly falling behind with the heavy traffic.

We knew the obvious place to look was at the queries themselves. We …

I wanted to let everyone know that we are releasing Waffle Grid 0.5 code named Cream. This release fixes the nasty secondary index bug that plagued the butter release. I have been running tests on this code base for about a week straight with no errors. While I think this release is much more stable I would remind everyone this is still not a fully GA release. This release includes the ability to choose the mode of Waffle grid. By setting innodb_memcached_enable to 1, we will push pages to memcached when a disk read is done or when a page write is done, setting this to 2 will enable the classic LRU mode. If you decide to set this to 1 ( non-lru) I would recommend using the standard memcached, as with previous versions the LRU mode works better with our slightly altered memcached ( expire from memcached on get ). I will be posting benchmarks and more details within the next couple of days. Right now you can grab the patch on …

There is an article with a cute title that is gaining popularity:
Do Androids Count Electric Sheep with DB2 or MySQL?
Allegedly, DB2 is demonstrated as incredibly faster than MySQL, with a benchmark that repeats the same COUNT query 100 times.

This is a naive (at best) benchmark that doesn’t tell me nothing about the database potential. But anyway, if you enable the query cache in MySQL, the repetition of 100 queries is at least three times faster than DB2.

Try
set global query_cache_size=1024*1024;
and repeat that test.

Moreover: the table structure doesn’t correspond to the data from the freebase project.

CREATE TABLE `people` (
 `id` int(11) DEFAULT NULL auto_increment PRIMARY KEY,
 `name` varchar(255) NOT …

Always looking at new toolsI've been looking for a better tool to work in a MySQL environment when I want to demo relationships between data models, physical tables, data and SQL code. It has to be a tool that the students can pick up easily without spending a lot of time understanding how it works. It's always an investment to learn another tool. So I've downloaded the MySQL Workbench 5.2