Benchmark tests should include both cold-start and warm cache scenarios to provide a comprehensive understanding of a vector search system’s latency behavior. Cold-start scenarios simulate the system’s performance when handling a query for the first time with no preloaded data in memory (e.g., empty cache or a freshly restarted service). This reflects real-world situations like initial user interactions, system restarts, or handling entirely new queries. Measuring latency here exposes bottlenecks in data loading, index initialization, or network overhead. For example, a vector database might need to load large indexes from disk or fetch embeddings from remote storage during a cold start, adding significant delays. Without testing this scenario, developers might overlook critical performance issues affecting user experience during first-time interactions.
Warm cache scenarios, where data is already cached in memory, measure the system’s optimal performance. This represents repeated queries or cases where frequently accessed data remains readily available. For instance, a recommendation system serving popular items would benefit from cached embeddings, resulting in faster response times. Testing this scenario reveals the system’s upper performance limits and helps identify inefficiencies in query execution, such as suboptimal indexing or algorithm choices. However, relying solely on warm cache results risks overestimating real-world performance, as users and applications often encounter a mix of cached and uncached operations. For example, a search feature might respond quickly to common terms but lag on rare queries that trigger cold starts.
Including both scenarios ensures balanced optimization and realistic expectations. Cold-start tests highlight areas for improvement in data preloading, caching strategies, or hardware resource allocation (e.g., faster disks to reduce index load times). Warm cache tests validate algorithmic efficiency and resource utilization under ideal conditions. Together, they help developers prioritize fixes—like prewarming caches for critical data or optimizing disk-to-memory pipelines—while ensuring the system performs reliably across diverse use cases. For example, a hybrid approach might combine on-demand loading for cold starts with background caching for frequently accessed vectors, balancing latency and resource usage. Without both metrics, teams risk building systems that perform well in labs but fail under real-world variability.