How does caching affect benchmarking results?

Caching significantly impacts benchmarking results by introducing variability between initial (“cold”) and repeated (“warm”) test runs. When a system uses caching, the first execution of a task typically performs slower because data must be fetched from its original source (e.g., a database, disk, or remote API). Subsequent runs benefit from cached data stored in faster-access layers (like memory), reducing latency and improving throughput. This creates a discrepancy between cold-start performance (no cached data) and warm performance (cached data available), which can skew benchmark results if not properly accounted for. For example, a database query taking 200ms on the first run might drop to 5ms on later runs due to caching, leading to misleading averages if only a single test pass is measured.

The effect varies depending on the type of caching involved. CPU caches, for instance, accelerate repeated computations by storing frequently accessed instructions or data closer to the processor. A benchmark measuring algorithm speed might show inconsistent results if the test isn’t run long enough for the CPU cache to stabilize. Similarly, application-level caches (e.g., Redis or Memcached) can make API response times appear artificially fast in benchmarks if previous test iterations pre-populate the cache. For example, an e-commerce site’s product listing endpoint might return in 50ms after caching product data, but 500ms on the first request while fetching from a database. If a benchmark doesn’t reset the cache between test scenarios, it could overestimate real-world performance for new users or uncached operations.

To mitigate caching’s impact, developers should design benchmarks to isolate specific scenarios. For cold-cache measurements, clear caches before each test iteration or run benchmarks in isolated environments. For warm-cache analysis, execute a “warm-up” phase to populate caches before recording results. Tools like docker-compose can reset containerized services between runs, while frameworks like Python’s timeit automatically run multiple iterations to account for caching effects. For example, when benchmarking a web server, running 1,000 requests and discarding the first 100 (to exclude cold-start outliers) ensures results reflect steady-state performance. Explicitly documenting whether tests include caching—and how it’s controlled—ensures benchmarks provide accurate, reproducible insights.