My blogging hasn't been so active lately. Mostly due to that I've been busy on
other things. One of the things that have kept me busy the last few months
is a project which I highly enjoy. I've been performing a benchmark study
of MySQL Cluster using Dolphin SuperSockets. Performance is one of my
favourite topics and a parallel database like MySQL Cluster has a wide array
of performance challenges that makes it very interesting to optimize it.
I will present the results in two webinars on the 30th Nov and 13 dec. The
webinars will also provide some input to the features in Dolphin
SuperSockets and MySQL Cluster that enables high performance and
real-time characteristics. With these changes to MySQL Cluster and using
the Dolphin SuperSockets MySQL Cluster becomes even more adapted for
all types of real-time applications.
Performing this work has been an interesting enterprise in finding out how
to best make use of the Dolphin hardware using MySQL Cluster. I found a
number of interesting ways where 1+1 = 3, meaning I've found optimisations
that can be done in MySQL Cluster that are especially effective if using
Dolphin SuperSockets. So as a result of this some very interesting
achievements have been made.
- A completely interrupt-free execution of ndbd nodes in MySQL Cluster
using Dolphin SuperSockets.
- Real-time features added to MySQL Cluster enabling much faster response
- Possibility to lock threads to CPU's in MySQL Cluster enabling a higher level
of control over the execution environment.
- Possibility to lock pages in main memory removing any risk of swapping
- Possibility to choose between polling and interrupt-driven mechanisms in
The combination of MySQL Cluster and Dolphin SuperSockets becomes a truly
amazing real-time machine. With those added features in place and using
Dolphin SuperSockets I've also seen how MySQL Cluster can take yet another
step on its on-line recovery features. Using those real-time features it is
possible to get node failover times down to around 10 milliseconds.
MySQL Cluster was already before market leading in this respect, with this
feature the gap to the competitors is bound to increase.
Most of the benchmark work have been focused on the DBT2 benchmark. Most
benchmarks I've done in the past have been focused on applications written
directly for the NDB API. So it's been interesting to see what one needs to do
to make the MySQL Server be really fast.
In order to run DBT2 with MySQL Cluster at first I had to adapt the DBT2
- Parallel load of data
- Parallel MySQL Servers while running the benchmark
- Using MySQL Cluster features such as HASH indexes, PARTITIONING and
Disk Data for MySQL Cluster.
I also got tired of remembering all the -i -t -h and so forth in the various
scripts and used more real names for the parameters.
There was also a number of performance bugs in DBT2. DBT2 is implementing
the TPC-C specification and in a number of places the SQL queries were made
such that there was a large number of unnecessary extra record fetches in some
I will soon upload the changes to DBT2 to SourceForge if anyone wants to use
the same benchmark.