MySQL Benchmarking on m3.xlarge and m3.2xlarge Instances
In our last post about MySQL benchmarking, we benchmarked MySQL performance on an m3.large instance using both instance store and different types of Amazon Web Service’s (AWS) Elastic Block Store (EBS) storage.
In this post, we have extended the benchmarks on m3.xlarge and m3.2xlarge. We ran the benchmarks with and without optimizations. We explained the optimization and the rationale behind each of them in a previous post about using Sysbench for benchmarking.
Here is a comparison of the different metrics we measured in Sysbench:
|
Without Optimization |
With Optimization |
|---|---|
|
Transactions Per Second |
Transactions Per Second |
|
Avg. Response Time |
Avg. Response Time |
|
95% Response Time |
95% Response Time |
We noticed a couple of interesting items in the graphs:
1. Even though we see an increase in the transactions per second (TPS) as we move to a more powerful instance type, we also see an increase in the response times. Ideally, the response time would decrease as TPS increases.
The reason behind this can be explained using Little’s Law, which states:
Occupancy = Latency x Throughput
As TPS increases, occupancy increases.
Throughput = Occupancy / Latency
The increase in occupancy leads to increased throughput amidst the increased latency. To better understand the relationship between latency and throughput, read this blog post about Little’s Law.
2. We can see that the benefits of optimization is most evident in larger tables.
We also collected metrics for resource utilization. Here is a summary of resource utilization when the benchmark was run with and without optimizations. We saw a significant differences in disk octets when optimization was applied. We also saw differences in the way memory was consumed.
|
Without Optimization |
With Optimization |
|---|---|
|
CPU |
CPU |
|
Memory |
Memory |
|
Disk Octets |
Disk Octets |
|
Disk Operations |
Disk Operations |
There it is. Benchmarking results of MySQL on m3.xlarge and m3.2xlarge.
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