I've worked on DTrace probes for a while now. It's
a really interesting tool. I've worked on MySQL Cluster
code since 1996 but this is the most advanced tool
I've used to see exactly what's going on inside the
MySQL Server and the data nodes.
I'm still at an early stage of using these DTrace probes
and there is still some work before they are publishable
but one can see very well what's going on inside the
processes in real-time.
My first finding was that I quickly discovered that CPU
percentage that is reported at 1% in prstat in Solaris
actually means that it uses 64% of a CPU thread 1% is the
percentage of the total CPU resources, this is different
to what I'm used to from top.
The benchmark I'm analysing is the same DBT2 I've used in
a fairly long line of analysis on MySQL Cluster performance
over the last 2 years. This benchmark can be downloaded
from www.iclaustron.com/downloads.html. It's a DBT2 based
on version 0.37 with lots of additions to make it work with
running multiple MySQL Server instances as is the case
with MySQL Cluster. Currently I'm running this on Solaris so
there will soon be a new release with some fixes needed to
run this benchmark on Solaris.
Friday, June 06, 2008
Wednesday, March 26, 2008
Visited Hadoop Conference
NOTE: Any comments in this blog entry is based on my personal thoughts after visiting the Hadoop conference and doesn't represent any current plans within MySQL.
I visited the Hadoop conference today which was a very interesting event. The room was filled to its limit, people were even standing up in lack of chairs. Probably around 300 people or so.
It was interesting to see the wide scope of web-scale problems that could be attacked using Hadoop. The major disruptive feature in Hadoop is the MapReduce solution to solving parallel data analysis problems.
One piece that I started thinking of was how one could introduce the MapReduce into SQL. One presentation of HIVE showed an interesting approach of how to solve this problem. I thought a bit on how one could integrate a MapReduce solution in MySQL and there are certainly a lot of problems to solve but I got a few interesting ideas.
The concept of being able to query both business data stored in a database and web-based logs and other type of massive amounts of data is certainly an interesting problem to consider.
In principle what one can add by introducing MapReduce into MySQL is the ability to handle streaming queries (queries that use dataflows as input table(s) and dataflows as output table).
However the actual implementation of Hadoop and HBase still were very much in their infancies so availability and reliability were far away from always on and also performance wasn't yet a focus.
I visited the Hadoop conference today which was a very interesting event. The room was filled to its limit, people were even standing up in lack of chairs. Probably around 300 people or so.
It was interesting to see the wide scope of web-scale problems that could be attacked using Hadoop. The major disruptive feature in Hadoop is the MapReduce solution to solving parallel data analysis problems.
One piece that I started thinking of was how one could introduce the MapReduce into SQL. One presentation of HIVE showed an interesting approach of how to solve this problem. I thought a bit on how one could integrate a MapReduce solution in MySQL and there are certainly a lot of problems to solve but I got a few interesting ideas.
The concept of being able to query both business data stored in a database and web-based logs and other type of massive amounts of data is certainly an interesting problem to consider.
In principle what one can add by introducing MapReduce into MySQL is the ability to handle streaming queries (queries that use dataflows as input table(s) and dataflows as output table).
However the actual implementation of Hadoop and HBase still were very much in their infancies so availability and reliability were far away from always on and also performance wasn't yet a focus.
Tuesday, March 25, 2008
MySQL Architecture Workshop
We had a workshop in Stockholm in early March to discuss what can be done to innovate MySQL in a number of areas. Most of the work here will not be useful code in yet a year or two and a lot longer before it'll be used in Enterprise Ready binaries. Obviously there is no guarantee that this early work will reach production binaries. This work is part of an aim at advancing the MySQL Architecture in the next few years.
One interesting topic we discussed was Pushdown of Query Fragments to Storage Engine.
A Query Fragment is a piece of an SQL query, for example in a 3-way join any join of 2 tables in this query is a Query Fragment, also the full query is a Query Fragment. As part of this interface the storage engine can decide to perform its own optimising using a new interface or it could rely on the MySQL Server to handle this optimisation. If the Storage Engine decides it can handle the Query Fragment and the optimiser decides to use this Query Fragment then the execution of this Query Fragment will be executed using the traditional Storage Engine API as if the Query Fragment was a normal table.
There are many engines that could make use of this new interface. Another interesting use of this interface is to implement parallel query support for the MySQL Server using this new interface. We hope to build a prototype of this sometime this year.
Please provide comments on this development on this blog, the development is in such an early phase that input is very welcome.
One interesting topic we discussed was Pushdown of Query Fragments to Storage Engine.
A Query Fragment is a piece of an SQL query, for example in a 3-way join any join of 2 tables in this query is a Query Fragment, also the full query is a Query Fragment. As part of this interface the storage engine can decide to perform its own optimising using a new interface or it could rely on the MySQL Server to handle this optimisation. If the Storage Engine decides it can handle the Query Fragment and the optimiser decides to use this Query Fragment then the execution of this Query Fragment will be executed using the traditional Storage Engine API as if the Query Fragment was a normal table.
There are many engines that could make use of this new interface. Another interesting use of this interface is to implement parallel query support for the MySQL Server using this new interface. We hope to build a prototype of this sometime this year.
Please provide comments on this development on this blog, the development is in such an early phase that input is very welcome.
Visiting Internal Sun Technology Conference
My first real chance to meet up with my new colleagues at Sun was an internal technology conference at Sun. It was interesting to listen to what's cooking within Sun.
We got a presentation of Data Centers and their impact on the environment and it immediately triggered me to start thinking of how we can interact with power save functions from the MySQL Code. It was also interesting to see slides on how computer architecture is developing, this can be put into thinking about how the MySQL architecture should progress over the next few years.
We got a presentation of Data Centers and their impact on the environment and it immediately triggered me to start thinking of how we can interact with power save functions from the MySQL Code. It was also interesting to see slides on how computer architecture is developing, this can be put into thinking about how the MySQL architecture should progress over the next few years.
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