Benchmarks evaluate query parallelism by measuring how well a database management system can execute multiple queries or operations simultaneously. This involves running a series of queries across multiple threads or processes and assessing performance metrics such as execution time, resource utilization, and throughput. The goal is to determine how effectively the system can leverage available hardware resources, like CPU cores and memory, to retrieve and manipulate data in parallel.
For example, a benchmark might use a workload that simulates real-world scenarios, such as running several complex SELECT queries on a large dataset. By executing these queries concurrently, the benchmark records the total time taken for all queries to complete as well as individual query times. If the system can execute these queries in less time than it would take to run them sequentially, it indicates that the system has good parallelism. Moreover, metrics such as CPU usage and memory consumption during the tests provide additional insights into how well the system is utilizing its resources.
Another important aspect to consider is the behavior of the database under varying levels of concurrency. Benchmarks often include scenarios with different numbers of simultaneous queries to see how performance scales. For instance, increasing the number of concurrent queries may initially improve throughput, but at some point, adding more queries might lead to contention for resources, causing performance to plateau or even degrade. By analyzing results from these varied conditions, developers can better understand the limits of query parallelism in their database systems and make informed decisions on optimization and resource allocation.