Exadata is designed to present a well-balanced system between disk scan rates, cell CPU processing rates, network bandwidth, and RDBMS CPU such that on average across a lot of workloads and a lot of applications there should not be no one obvious bottleneck. Good performance engineering means avoiding a design like this:
because that would be nuts. If you change one component in a car e.g. the engine you need to rework the other components such as the chassis and the brakes. When we rev the Exadata hardware we go through the same exercise to make sure the components match to avoid bottlenecks.
One of the components in a well-balanced system that people often leave off this list is compression algorithm. With compression you are balancing storage costs as well as the disk scan rates against the CPU’s decompression rate. In a disk bound system you can alleviate the bottleneck by compressing the data down to the point that it is being scanned at the same rate that the CPUs can decompress and process it. Squeeze it too far, say with HCC “Compress For Archive High” which uses BZ2, the CPUs become a major bottleneck because the decompression costs unbalance the system (but it’s wonderful if you think you’re unlikely to ever need the data again).Continue reading