As a followup to my MySQL 5.4 Scalability on 64-way CMT Servers blog, I'm posting MySQL 5.4 Sysbench results on the same platform. The tests were carried out using the same basic approach (i.e. turning off entire cores at a time) - see my previous blog for more details.

The Sysbench version used was 0.4.8, and the read-only runs were invoked with the following command:

sysbench --max-time=300 --max-requests=0 --test=oltp --oltp-dist-type=special --oltp-table-size=10000000 \
   --oltp-read-only=on --num-threads=[NO_THREADS] run

The "oltp-read-only=on" parameter was omitted for the read-write tests. The my.cnf file listed in my previous blog was also used unchanged for these tests.

Here is the data presented graphically. Note that the number of vCPUs is the same as the number of active threads up to 64. Active threads beyond …

Today Sun Microsystems announced MySQL 5.4, a release that focuses on performance and scalability. For a long time it's been possible to escape the confines of a single system with MySQL, thanks to scale-out technologies like replication and sharding. But it ought to be possible to scale-up efficiently as well - to fully utilize the CPU resource on a server with a single instance.

MySQL 5.4 takes a stride in that direction. It features a number of performance and scalability fixes, including the justifiably-famous Google SMP patch along with a range of other fixes. And there's plenty more to come in future releases. For specifics about the MySQL 5.4 fixes, check out Mikael Ronstrom's blog.

So how well does MySQL 5.4 scale? To help answer the question I'm going to take a look at some performance data from one of Sun's CMT systems based on the UltraSPARC T2 chip. This …

As a final followup to my MySQL 5.4 Scalability on 64-way CMT Servers blog, I'm posting MySQL 5.4 Sysbench results on a Sun Fire X4270 platform using the Intel x86 Nehalem chip (2 sockets/8 cores/16 threads). All CPUs were turned on during the runs. The my.cnf was the same as described in the previous blog.

The Sysbench version used was 0.4.12, and the read-only runs were invoked with the following command:

sysbench --max-time=300 --max-requests=0 --test=oltp --oltp-dist-type=special --oltp-table-size=10000000 \\
   --oltp-read-only=on --num-threads=[NO_THREADS] run

The "oltp-read-only=on" parameter was omitted for the read-write tests. The my.cnf file listed in my previous blog was also used unchanged for these tests.

Here are the results graphically running on Linux.

The read-only results for MySQL 5.4 show a higher …

As a final followup to my MySQL 5.4 Scalability on 64-way CMT Servers blog, I'm posting MySQL 5.4 Sysbench results on a Sun Fire X4270 platform using the Intel x86 Nehalem chip (2 sockets/8 cores/16 threads). All CPUs were turned on during the runs. The my.cnf was the same as described in the previous blog.

The Sysbench version used was 0.4.12, and the read-only runs were invoked with the following command:

sysbench --max-time=300 --max-requests=0 --test=oltp --oltp-dist-type=special --oltp-table-size=10000000 \\
   --oltp-read-only=on --num-threads=[NO_THREADS] run

The "oltp-read-only=on" parameter was omitted for the read-write tests. The my.cnf file listed in my previous blog was also used unchanged for these tests.

Here are the results graphically running on Linux.

The read-only results for MySQL 5.4 show a higher …

As a followup to my MySQL 5.4 Scalability on 64-way CMT Servers blog, I'm posting MySQL 5.4 Sysbench results on the same platform. The tests were carried out using the same basic approach (i.e. turning off entire cores at a time) - see my previous blog for more details.

The Sysbench version used was 0.4.8, and the read-only runs were invoked with the following command:

sysbench --max-time=300 --max-requests=0 --test=oltp --oltp-dist-type=special --oltp-table-size=10000000 \\
   --oltp-read-only=on --num-threads=[NO_THREADS] run

The "oltp-read-only=on" parameter was omitted for the read-write tests. The my.cnf file listed in my previous blog was also used unchanged for these tests.

Here is the data presented graphically. Note that the number of vCPUs is the same as the number of active threads up to 64. Active threads beyond …

As a followup to my MySQL 5.4 Scalability on 64-way CMT Servers blog, I'm posting MySQL 5.4 Sysbench results on the same platform. The tests were carried out using the same basic approach (i.e. turning off entire cores at a time) - see my previous blog for more details.

The Sysbench version used was 0.4.8, and the read-only runs were invoked with the following command:

sysbench --max-time=300 --max-requests=0 --test=oltp --oltp-dist-type=special --oltp-table-size=10000000 \\
   --oltp-read-only=on --num-threads=[NO_THREADS] run

The "oltp-read-only=on" parameter was omitted for the read-write tests. The my.cnf file listed in my previous blog was also used unchanged for these tests.

Here is the data presented graphically. Note that the number of vCPUs is the same as the number of active threads up to 64. Active threads beyond …

Today Sun Microsystems announced MySQL 5.4, a release that focuses on performance and scalability. For a long time it's been possible to escape the confines of a single system with MySQL, thanks to scale-out technologies like replication and sharding. But it ought to be possible to scale-up efficiently as well - to fully utilize the CPU resource on a server with a single instance.

MySQL 5.4 takes a stride in that direction. It features a number of performance and scalability fixes, including the justifiably-famous Google SMP patch along with a range of other fixes. And there's plenty more to come in future releases. For specifics about the MySQL 5.4 fixes, check out Mikael Ronstrom's blog.

So how well does MySQL 5.4 scale? To help answer the question I'm going to take a look at some performance data from one of Sun's CMT systems based on the UltraSPARC T2 chip. This …

Today Sun Microsystems announced MySQL 5.4, a release that focuses on performance and scalability. For a long time it's been possible to escape the confines of a single system with MySQL, thanks to scale-out technologies like replication and sharding. But it ought to be possible to scale-up efficiently as well - to fully utilize the CPU resource on a server with a single instance.

MySQL 5.4 takes a stride in that direction. It features a number of performance and scalability fixes, including the justifiably-famous Google SMP patch along with a range of other fixes. And there's plenty more to come in future releases. For specifics about the MySQL 5.4 fixes, check out Mikael Ronstrom's blog.

So how well does MySQL 5.4 scale? To help answer the question I'm going to take a look at some performance data from one of Sun's CMT systems based on the UltraSPARC T2 chip. This …

Sounds a bit far fetched (pun intended ;-), but we're doing it. This is not inside of the MySQL server, but rather the overall application design. Let me run you through the logic...

Some key aspects to scaling are: not doing unnecessary queries, and caching what you can. Just a quick baseline. The fastest query is the one you don't do, or the one you've already done before - the latter being caching.

A simple yet brilliant example of this is the Youtube trick where a script reads the relay log, converting updates into appropriate selects and running them so that the InnoDB cache will have the blocks in memory when the slave SQL thread executes the actual update. Maatkit now has a tool for this, so it's publically available. It's not quite predictive, but it's a neat trick anyway that sometimes comes in handy. Search engines use similar tricks.

Extending on this, with certain applications you actually …

Sounds a bit far fetched (pun intended ;-), but we’re doing it. This is not inside of the MySQL server, but rather the overall application design. Let me run you through the logic…

Some key aspects to scaling are: not doing unnecessary queries, and caching what you can. Just a quick baseline. The fastest query is the one you don’t do, or the one you’ve already done before - the latter being caching.

A simple yet brilliant example of this is the Youtube trick where a script reads the relay log, converting updates into appropriate selects and running them so that the InnoDB cache will have the blocks in memory when the slave SQL thread executes the actual update. Maatkit now has a tool for this, so it’s publically available. It’s not quite predictive, but it’s a neat trick anyway that sometimes comes in handy. Search engines use similar tricks.

Extending on this, with certain applications you actually tell …