Batching multiple queries in vector search impacts latency and throughput in distinct ways. When queries are processed individually, each request waits its turn, leading to higher overall latency if the system is busy. Batching reduces this overhead by grouping queries, allowing parallel processing—especially on hardware like GPUs that excel at handling multiple operations simultaneously. However, latency for a single batch may increase because the system must wait to collect enough queries before processing them. Throughput typically improves because batched processing utilizes resources more efficiently, handling more queries per second by amortizing fixed costs (like data loading) across multiple requests. For example, processing 100 queries in one batch might take 50ms, while processing them sequentially could take 500ms, dramatically increasing throughput at the cost of slightly higher per-batch latency.
Batch querying is beneficial in high-throughput scenarios where slight increases in latency are acceptable. For instance, offline recommendation systems generating embeddings for millions of items overnight can maximize throughput by processing large batches, leveraging GPU parallelism. Similarly, applications like bulk similarity searches (e.g., finding duplicate images in a dataset) benefit from batching to reduce total processing time. Batch processing also shines when hardware accelerators are available, as their architectures are optimized for parallel workloads. Conversely, batching is detrimental in low-latency, real-time applications. For example, a live user-facing search feature requiring instant results (e.g., autocomplete or real-time product recommendations) would suffer if queries were delayed to form batches. Small batch sizes or single queries are preferable here to prioritize responsiveness. Additionally, systems with limited memory or compute resources may struggle with large batches, causing bottlenecks or degraded performance for all queries in the batch if processing exceeds capacity.
The trade-off hinges on balancing latency tolerance and resource utilization. Batch processing is advantageous when throughput is critical and queries can be grouped without violating latency requirements. For example, a video streaming service precomputing embeddings for content categorization could use large batches during off-peak hours. However, in dynamic environments where query patterns are unpredictable or workloads vary widely (e.g., some queries require complex computations while others are simple), batching might lead to inefficient resource allocation. Similarly, if queries have strict deadlines (e.g., real-time fraud detection), delaying them to form batches could negate their usefulness. Developers should test batch sizes and monitor latency/throughput metrics to determine the optimal configuration for their specific use case and infrastructure.