With most of the paperwork done I’ve started to make use of my free time in Toronto.  In an effort to improve my Python programming I implemented a cummulative sum estimation based change point detection algorithm in Pyton based on an article by Dr. Wayne A. Taylor.  The changepoint project is available on Google code– all comments are apprecaited.

Here’s my solution for the Talk with Distributed Erlang exercise.

You can run the program by calling the start function, but first you must generate a cookie and start two named Erlang nodes using the following commands:

1. echo -n “dh32d8yhd8″ > erlang.cookie
2. erl -name Node1 -cookie
3. erl -name Node2 -cookie

Introduction
As stock markets open, order management systems are flooded with new orders and it’s crucial that an OMS not miss prices or drop orders during this period.  The purpose of this initial benchmark is to simulate a flood of orders hitting the queue.

Benchmark
The benchmarking script sets up 10 queues, 10 producers and 10 consumers, each producer writes to a single queue and each consumer reads from that queue ie: p1 writes to q1 and c1 reads from q1 as fast at it can.  The time measured is the time it takes a message to travel from p1 to the queue and to be read by c1 and it was calculated by appending the timestamp to the message itself.

I chose to run a series of tests with message size varying from 35 bytes to 400 bytes and the message count varying from 50 messages to 100 messages in an attempt to visualize the broker’s behavior under varying conditions.

The benchmark was performed using a server running 2.8GHz Pentium D with 903464 kB RAM running on Kernel 2.6.18-128.1.10.el5 using RabbitMQ 1.5.5 and a machine with similar specifications as the client—both the producer and consumer run on the same machine.  The network is a typical Fast Ethernet with two switches separating the server and client machines.

Conclusion
Inconclusive! The numbers are very surprising I wasn’t expecting the message transfer times to be greater than .5 seconds under this load.  It’s possible that the consumer may not be able to keep up with flood of messages, if that’s the case than a consumer written in C# is needed.  What is obvious is that more tests  with a constant message size and a varying number of messages  are needed.

The standard deviation is also greater than I expected, so I plan on creating longer running tests and see if the broker falls into a steady state.  Finally, I also want to test with 50 messages per producer using 20 queues/producers instead of 10  and see how RabbitMQ responds.

I’m wondering what sort of performance others are experiencing—any and all feedback is greatly appreciated.

Postscript
I intended on using C# to benchmark RabbitMQ but for want of time  I used a modified version of Colin Surprenant’s bunnypunisher ruby script.  There’s a slightly outdated (and dirty) version of the script available on github.  I will upload the latest version along with the “launcher” tomorrow morning.

Kyle Burton at Asymmetrical View has a fairly comprehensive comparison of RabbitMQ and Qpid.