Benchmark tests should include both cold-start and warm cache scenarios because they represent real-world variability in system performance. Cold-start scenarios simulate the first query when the system has no cached data, forcing it to initialize resources like indexes, load data into memory, or compile execution plans. Warm cache scenarios mimic repeated queries where data is already cached, allowing the system to skip initialization steps. Measuring both ensures a balanced view of latency: cold-start reveals baseline overhead, while warm cache shows optimized performance after system “warm-up.” For example, a vector database might take 500ms on the first search (cold-start) due to index loading but drop to 50ms on subsequent queries (warm cache) when the index is memory-resident.
Including both scenarios helps identify bottlenecks specific to each phase. Cold-start tests expose initialization costs, such as disk I/O for loading vector indexes, network latency in distributed systems fetching data from remote nodes, or model warm-up in machine learning-based search systems. For instance, a graph-based vector index stored on disk might require significant time to map into memory during cold starts. Warm cache tests, on the other hand, highlight the efficiency of search algorithms and caching mechanisms. If a warm query still shows high latency, it could indicate poorly optimized code paths or inefficient caching strategies. For example, a vector search system using hierarchical navigable small world (HNSW) graphs might show fast warm-cache performance but suffer during cold starts if the graph isn’t preloaded.
From a developer perspective, benchmarking both scenarios guides optimization priorities. Cold-start results might lead to preloading frequently used indexes at startup or using lazy loading techniques. Warm cache measurements could drive improvements to query execution plans or cache eviction policies. For example, a team might discover that enabling memory-mapped files reduces cold-start latency by avoiding full index loads, while adjusting cache size limits improves warm-cache hit rates. Without testing both, developers risk over-optimizing for one scenario at the expense of the other. A system tuned only for warm cache might fail under sporadic usage patterns, while one optimized solely for cold starts could lack scalability for high-throughput workloads. Balanced benchmarks ensure realistic performance expectations and informed trade-offs.