Xaprb

This is a cross-post from the MySQL Performance Blog. I thought it would be interesting to users of PostgreSQL, Redis, Memcached, and $system-of-interest as well.

For about the past year I’ve been formulating a series of tools and practices that can provide deep insight into system performance simply by looking at TCP packet headers, and when they arrive and depart from a system. This works for MySQL as well as a lot of other types of systems, because it doesn’t require any of the contents of the packet. Thus, it works without knowledge of what the server and client are conversing about. Packet headers contain only information that’s usually regarded as non-sensitive (IP address, port, TCP flags, etc), so it’s also very easy to get access to this data even in highly secure environments.

I’ve finally written up a paper that shows some of my techniques for detecting problems in a system, which can be an easy way to answer questions such as “is there something we should look into more deeply?” without launching a full-blown analysis project first. It’s available from the white paper section of our website: MySQL Performance Analysis with Percona Toolkit and TCP/IP Network Traffic

I’ll be presenting at the Southern Computer Measurement Group’s meeting on Thursday. I’ll discuss how to extract scalability and performance metrics from TCP/IP packet headers. Registration is inexpensive, but it’s even less if you register by Monday. There is a full schedule of other good talks — it is an all-day meeting.

I’ve been seeing a few occasions where Neil J. Gunther’s Universal Scalability Law doesn’t seem to model all of the important factors in a system as it scales. Models are only models, and they’re not the whole truth, so they never match reality perfectly. But there appear to be a small number of cases where systems can actually scale a bit better than linearly over a portion of the domain, due to what I’ve been calling an “economy of scale.” I believe that the Universal Scalability Law might need a third factor (seriality, coherency, and the new factor, economy of scale). I don’t think that the results I’m seeing can be modeled adequately with only two parameters.

Here are two publicly available cases that appear to demonstrate this phenomenon: Robert Haas’s recent blog post on PostgreSQL, titled Scalability, in Graphical Form, Analyzed and Mikael Ronstrom’s post from May on MySQL (NDB) Cluster, titled Better than Linear Scaling is Possible.

Dr. Ronstrom’s post discusses the mechanics of the phenomenon, and speculates (I’m not sure it’s conclusive) that it is from a combination of partitioning and better use of CPU caches. Now someone needs to do the math to figure out how to include this factor into the equation.

The good thing about the Universal Scalability Law is how simple and applicable it is for many systems. It’s nice that this economy-of-scale factor seems to be unusual and the simpler model remains easy to apply for a large variety of tasks.