The Kickfire appliance is designed for business intelligence and analytical workloads, as opposed to OLTP (online transaction processing) environments.  Most of the focus in the MySQL area right now revolves around increasing performance for OLTP type workloads, which makes sense as this is the traditional workload that MySQL has been used for.  In contrast,  Kickfire focuses squarely on analytic environments, delivering high performance execution of analytical and reporting queries .

A MySQL server with fast processors, fast disks (or ssd) and lot of memory will answer many OLTP queries easily.  Kickfire will outperform such a server for typical analytical queries such as aggregation over a large number of rows.

A typical OLTP query might ask “What is the shipping address for this invoice?”.  Contrast this with a typical analytical query, which asks “How much of this item did we sell in all of …

Take a look here:

  Response Time (s)
 Transaction      %    Average :    90th %        Total        Rollbacks      %
------------  -----  ---------------------  -----------  ---------------  -----
    Delivery   3.98      0.211 :     0.266       274829                0   0.00
   New Order  44.78      0.157 :     0.187      3090951            30925   1.00
Order Status   3.99      0.149 :     0.179       275357                0   0.00
     Payment  42.76      0.150 :     0.180      2951361                0   0.00
 Stock Level   3.99      0.152 :     0.182       275564            92070  33.41

50606.82 new-order transactions per minute (NOTPM)
60.5 minute duration
0 total unknown errors
31 second(s) ramping up

If you know what this output is from, and you know what 50K TPM means… your probably curious about these #’s. I am probably tantalizing you right now in fact. But I am not going to tell you more, not yet. So go ahead and guess. Better yet come …

This is an issue that keeps rearing its ugly head over and over again, and since it greatly affects performance, it is most important that DBAs of any DMBS running on Linux come to grips with it. So I decided to do some research and try different settings on my notebook. Here are my findings.

What can you find on the web?

A Wikipedia search for the word swappiness will come up empty (any volunteers out there want to write an article?). A Google search will show some pretty old material—the best article I found is from 2004: Linux: Tuning Swappiness. This article includes a detailed discussion with some interesting remarks by Andrew Morton, a Linux kernel maintainer.

So, what is swappiness?

Towards the end of the email thread quoted in the article, you’ll find this definition (sort of):

> I’ve read the source for where …

Only a little over a week before the User conference and I am still burning the midnight oil to get as much information for my presentations as possible. I thought I would tease you a bit here. What do you get when you put 4 Intel X-25E’s ( Sun branded) SSD’s running RAID10 in a Sun 4450 and run the sysbench fileio test on it?

I am doing the final prep work for my upcoming UC presentation on SSD’s, and I thought I would throw this out their. Recently their has been a great deal of discussion on the write cache on the Intel x-25e and whether you need to disable it to prevent data loss on a power outage. Most disk caches are not protected by a battery backup and are disabled by default on most high end controllers. Who wants to potentially lose 16-64MB of data on an outage? So it seems like it would make sense that you should disable the cache on the Intel drives as well. But their is a problem. Vadim over at the MySQL Performance Blog recently published some benchmarks that show some rather slow results when the disk cache is disabled, in fact I have also noticed a significant slow down in these cases as well. So this leads to the …

Here are some common performance tuning concepts that I frequently run into. Please note that this really is only a basic introduction to performance tuning. For more in-depth tuning, it strongly depends on your systems, data and usage. Server Variables For tuning InnoDB performance, your primary variable is innodb_buffer_pool_size. This is the chunk of memory that InnoDB uses for caching data,

By transparently sitting between client and server on each request, MySQL Proxy offers many possibilities for query manipulation.

Many are explored in the cookbook, and they even include a histogram recipe. Still, I wanted to learn more about the proxy while working on a script that would let me get some stats on the queries executed against a server (or group of servers).

First things first, get a brief glimpse of the lua programming language since that’s what the proxy’s scripts are written in. Alternatively, you can jump straight into the sample scripts, extrapolate what you don’t understand of the syntax by making paralelizations against other …

Yes we are still plugging away working on Waffle Grid, in fact I am testing the heck out of the plugin release this week. Some good news, great news, and bad news to report.

The good news on the testing is using the new Waffle Grid release, I am able to consistently get up close to 15K TPM, that’s up from 3K TPM without WaffleGrid ( 5x increase woohoo! ). This performance boost holds true through several tests, and based on this testing I am working on a set of recommended parameters for getting the most performance out of Waffle. I should have my recommendations for our User Conference presentation.

The great news is I have been able to get close to 20K TPM by disabling the read-ahead! This represents a huge boost in performance, over both the read-ahead enabled and the non-waffle tests. The bad news is in extended tests with the read-ahead disabled the plugin version takes a crap on me and consistently dies about 22-24 minutes …

Spreading the word about Jay’s awesome findings on the libdrizzle benchmark against the original library inherited from MySQL. For those that aren’t familiar with libdrizzle, it is a fresh new (modern implementation) MySQL compatible client library for Drizzle that leverages asynchronous I/O and smarter memory usage founded by Eric Day.

You can read how this library came to life in this thread:

• https://lists.launchpad.net/drizzle-discuss/msg00472.html

As you can see in Jay’s findings with sysbench, libdrizzle outperforms the original library in all concurrency …

I have been benchmarking Waffle Grid using the new Innodb Plugin 1.03 the past couple of days. Let me say the plugin is fast. Which got me thinking, generally when you fix a bottleneck another area becomes a bottleneck… its a vicious cycle really. I figured why not benchmarks several different settings just to see what sort of improvement or detriment we get in Inno. This hopefully will lead to the next place to look for potential performance improvements. For the test I chose a somewhat IO bound setup and a CPU bound setup.

The IO bound setup was a 20W test, 768M buffer pool.
The CPU boud setup was a 20W test, 5GB buffer pool.

I decided to start with the Double Write Buffer. For those who are not familiar with the double write buffer check out the docs or …