Benchmarks evaluate query parallelism by testing how effectively a database or system processes multiple queries or parts of a single query simultaneously. They focus on metrics like throughput (queries completed per second), latency (time per query), and resource utilization (CPU, memory, I/O). For example, a benchmark might simulate a workload with concurrent queries to measure if a system scales linearly when adding more workers or threads. If doubling the number of parallel workers doubles throughput without significantly increasing latency, the system handles parallelism well. Benchmarks also check for bottlenecks, such as lock contention or uneven resource distribution, which can degrade performance under high concurrency.
A common example is the TPC-H benchmark, which uses complex analytical queries to test parallel execution. It evaluates how well a database partitions large datasets across nodes or cores and processes subqueries in parallel. For instance, a query joining multiple tables might split table scans and aggregations across workers, with the benchmark timing how efficiently these tasks overlap. Similarly, the YCSB benchmark for NoSQL systems measures parallel read/write operations. It might test if increasing the number of client threads improves throughput while maintaining consistent latency. Resource monitoring tools track CPU usage across cores to ensure the workload is distributed evenly, avoiding scenarios where some cores are overloaded while others idle.
Benchmarks also assess how systems handle varying degrees of parallelism under different workloads. For example, a hybrid transactional/analytical processing (HTAP) benchmark might mix short, frequent transactions (OLTP) with long-running analytical queries (OLAP). The goal is to see if parallel execution preserves isolation and avoids resource starvation. Tools like EXPLAIN ANALYZE in databases like PostgreSQL can reveal whether the query planner effectively parallelizes operations like sorts or joins. If a benchmark identifies inefficiencies—such as a system serializing tasks that could run in parallel—developers might adjust configuration parameters (e.g., max_parallel_workers) or optimize queries to better utilize available resources. These insights help developers tune systems for real-world scenarios where parallelism is critical for performance.