Sysbench has three distribution for random numbers: uniform, special and gaussian. I mostly use uniform and special, and I feel that both do not fully reflect my needs when I run benchmarks. Uniform is stupidly simple: for a table with 1 mln rows, each row gets equal amount of hits. This barely reflects real system, it also does not allow effectively test caching solution, each row can be equally put into cache or removed. That’s why there is special distribution, which is better, but to other extreme – it is skewed to very small percentage of rows, which makes this distribution good to test cache, but it is hard to emulate high IO load.
That’s why I was looking for alternatives, and Zipfian distribution seems decent one. This distribution has a parameter θ (theta), which defines how skewed the distribution is. A physical sense of this parameter, if to apply to database tables, is following: say row 1 accessed N, then row 2 is accessed 2^θ less times, row 3 is accessed 3^θ less, …, row X is accessed X^θ less times.
Say θ=1.1, then if row 1 accessed 1,000,000 times, then row 2 is : 1,000,000/(2^1.1)=466,516 times, row 3: 1,000,000/(2^1.1)=298,652 times, …, row id=10000 : 1,000,000/(10,000^1.1) = 39 times.
Obviously with θ=0 we are getting uniform distribution – each row is accessed equal times ( for row X: 1/(X^0) ).
There is a research that shows that user behavior can be described by this distribution: Zipf, Power-laws, and Pareto – a ranking tutorial
To see distribution on graphs, I took tables with 1mln rows and run row lookup 1 million times.
I implemented Zipf for sysbench, right now it is in a separate tree https://code.launchpad.net/~vadim-tk/sysbench/zipf-distribution, you are welcome to try if it sounds interesting.
I am going to run couple incoming benchmarks with this distribution.
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