The optimal batch size for indexing documents depends on three key factors: system resources, document complexity, and performance requirements. There’s no universal value because every application has different constraints. A good starting point is to test batches between 100 and 1,000 documents, then adjust based on your system’s behavior. Smaller batches reduce memory usage and make error handling easier but may increase overhead from frequent network or disk operations. Larger batches improve throughput but risk exhausting memory or causing timeouts. For example, indexing 500 plain-text articles might work smoothly in a single batch, but 50 high-resolution image PDFs could overwhelm the same system.
To determine the right batch size, monitor your system’s resource usage and latency during indexing. Tools like memory profilers or database query analyzers can help identify bottlenecks. If CPU usage spikes and latency increases, reduce the batch size. If the system is underutilized (e.g., low CPU or network activity), try larger batches. For instance, a developer using Elasticsearch’s Bulk API might start with a batch of 1,000 documents but notice slow responses due to Java heap pressure. Reducing to 500 documents per batch could resolve the issue. Similarly, a cloud-based service with autoscaling might handle larger batches during off-peak hours but need smaller ones during traffic spikes to avoid throttling.
Practical examples also depend on the indexing pipeline design. A real-time application requiring low latency (e.g., chat message search) might use batches of 10-50 documents to minimize delay. In contrast, a nightly backup job for a large CMS could process 10,000-document batches without issue. Tools often provide guidelines: Elasticsearch recommends 1,000-5,000 documents per bulk request, while PostgreSQL’s COPY command can handle millions of rows but performs best with batches under 10,000 to avoid transaction locks. Always test with production-like data and load, and build retry logic for failed batches—smaller batches make retries less costly.