Pythian Group

Why a New Utility?

A couple of months back, Tim Procter, Sheeri Cabral and I were discussing about how best to diagnose a MySQL server and/or tune its performance, automating the process as much as possible. The Performance Advisors from MySQL Enterprise do this, but most of our customers don’t have a subscription and Pythian’s collective experience is not necessary reflected by its rules.

In our daily work, we have used Major Heyden’s MySQL Tuner, Mark Leith’s Statpack and our own tools to review a MySQL server configuration parameters. However, all of these tools had limitations in regards of what we wanted to achieve. Our major concern was the need to keep historical data to draw conclusions based on trends and cross check with other time based tools. The conclusion of our conversations was that whatever scripts we would implement, it would need to monitor all configuration and status variables, and their evolution over time.

One day, while looking into slave lag problem, the idea of MySAR popped into my head and a few hours later I was using its first incarnation. I was able to relate the server’s I/O activity peaks with these lags and in turn, discovered that it was caused by a great number of INSERT statements coming in in waves. It was an encouraging outcome for what was nothing more than a proof of concept.

Read the rest of this entry . . .

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?

Read the rest of this entry . . .

Recently we quickly and efficiently resolved a major performance issue with one of our New York clients. In this blog, I will discuss about this performance issue and its solution.

Problem statement

The client’s central database was intermittently freezing because of high CPU usage, and their business severely affected. They had already worked with vendor support and the problem was still unresolved.

Symptoms

Intermittent High Kernel mode CPU usage was the symptom. The server hardware was 4 dual-core CPUs, hyperthreading enabled, with 20GB of RAM, running a Red Hat Linux OS with a 2.6 kernel.

During this database freeze, all CPUs were using kernel mode and the database was almost unusable. Even log-ins and simple SQL such as SELECT * from DUAL; took a few seconds to complete. A review of the AWR report did not help much, as expected, since the problem was outside the database.

Analyzing the situation, collecting system activity reporter (sar) data, we could see that at 08:32 and then at 8:40, CPU usage in kernel mode was almost at 70%. It is also interesting to note that, SADC (sar data collection) also suffered from this CPU spike, since SAR collection at 8:30 completed two minutes later at 8:32, as shown below.

A similar issue repeated at 10:50AM: Read the rest of this entry . . .