Benchmarks measure resource contention by observing how multiple applications or processes vie for the same system resources, such as CPU, memory, disk I/O, or network bandwidth. When a benchmark is run, it typically stresses the system in various ways to simulate real-world usage scenarios. By monitoring the performance metrics under these conditions, developers can see how resource contention affects their applications. For example, if a benchmark runs multiple database queries simultaneously, it can reveal how well the database manages connections and memory under load.
Using specific tools and techniques, developers can evaluate contention points in their applications. For instance, when assessing CPU contention, developers might look at the CPU utilization percentage, context switch rates, and wait times. If multiple threads are competing for CPU resources, high wait times or low CPU usage rates can indicate contention. Similarly, memory contention can be gauged by monitoring metrics such as page faults or memory allocation times. Analyzing these metrics helps identify bottlenecks, which can lead to performance issues in production.
In addition to direct measurement, benchmarks can also simulate varying levels of workload to see how applications behave under different conditions. For example, if a benchmark runs a file upload process while simultaneously querying a database, it can measure latency and throughput for both operations. This analysis can shed light on how competently an application prioritizes resources and manages conflicts. By thoroughly assessing how applications perform under contention, developers can make informed decisions about optimizing their code, adjusting configurations, or scaling resources to improve overall performance.