One of my favorite parts of the IIUG conference is the Advanced Data Tools Fastest DBA contest. This year Lester gave us a real challenge: tune an engine for a TPCC like OLTP benchmark. This alone was a fun challenge but what really put you to the test was the amount of time you had. You had 1 hour from start to finish which included 10 minutes to initialize the engine and 10 minutes to run the benchmark leaving you only 40 minutes to get familiar with the benchmark and do any tuning or index building you wanted to do.
For me the 40 minutes felt like 4 and I didn't think of anything special to do to tune the engine and finished in 4th place but I did manage to claim the fastest Middle-Aged Informix DBA title. The prize for winning this category was a very nice shirt and many jokes at my expense from Tom Girsch and others.
Now that I am back home and have more than 40 minutes to play around with the benchmark I want to see how fast I can get it to run.
The first thing I did is rewrite the Java version in Python. Why would I do this? I don't know, I just like Python and rewriting the original benchmark was a good way for me to understand what the benchmark was doing. During my rewrite I added some logic to capture the execution times of each SQL, the number of times it was executing, the minimum execution time and the maximum execution time so I can see what needs to be improved or how much I've improved things.
Now that I have a good idea of what the benchmark does, I can give a brief overview of what is going on, something I couldn't do a week ago during my 40 minutes at the IIUG conference.
The benchmark simulates a order processing system. The system can take orders, schedule orders for delivery, process payments, check stock levels and check order statusii. There are 50 warehouses that each contain different amounts of the 100000 items in stock. Each warehouse has 10 districts that each have 3000 customers (50 * 10 * 3000 = 1.5 million customers) that can place orders, be delivered items or make payments, etc. from 1 of the 100 terminals that are running in parallel.
When the benchmark starts 100 terminal threads are created and run for 10 minutes continuously picking a transaction type to run.
43% of the time a payment transaction is picked
4% of the time a order status check transaction is picked
4% of the time a delivery transaction is picked
4% of the time a stock level check transaction is picked
45% of the time a new order transaction is picked
Each transaction type consists of multiple SQL statements that select, insert, update and delete and simulates an OLTP environment.
Next time I'll get into the configuration of my test system and share the results of my baseline and then start changing stuff and see if it helps or hurts.