qdrant-search-speed-optimization

# Diagnose a problem There the multiple possible reasons for search performance degradation. The most common ones are: * Memory pressure: if the working set exceeds available RAM * Complex requests (e.g. high `hnsw_ef`, complex filters without payload index) * Competing background processes (e.g. optimizer still running after bulk upload) * Problem with the cluster (e.g. network issues, hardware degradation) ## Single Query Too Slow (Latency) Use when: individual queries take too long regardless of load. ### Diagnostic steps: - Check if second run of the same request is significantly faster (indicates memory pressure) - Try the same query with `with_payload: false` and `with_vectors: false` to see if payload retrieval is the bottleneck - If request uses filters, try to remove them one by one to identify if a specific filter condition is the bottleneck ### Common fixes: - Tune HNSW parameters: [Fine-tuning search](https://search.qdrant.tech/md/documentation/operations/optimize/?s=fine-tuning-search-parameters) - Enable in-memory quantization: [Scalar quantization](https://search.qdrant.tech/md/documentation/manage-data/quantization/?s=scalar-quantization) - Reduce Vector Dimensionality with Matryoshka Models: [Matryoshka Models](https://search.qdrant.tech/md/documentation/inference/?s=reduce-vector-dimensionality-with-matryoshka-models) - Use oversampling + rescore for high-dimensional vectors [Search with quantization](https://search.qdrant.tech/md/documentation/manage-data/quantization/?s=searching-with-quantization) - Enable io_uring for disk-heavy workloads on Linux [io_uring](https://qdrant.tech/articles/io_uring/) ## Can't Handle Enough QPS (Throughput) Use when: system can't serve enough queries per second under load. - Reduce segment count (`default_segment_number` to 2) [Maximizing throughput](https://search.qdrant.tech/md/documentation/operations/optimize/?s=maximizing-throughput) - Use batch search API instead of single queries [Batch search](https://search.qdrant.tech/md/documentation/search/search/?s=batch-search-api) - Enable quantization to reduce CPU cost [Scalar quantization](https://search.qdrant.tech/md/documentation/manage-data/quantization/?s=scalar-quantization) - Add replicas to distribute read load [Replication](https://search.qdrant.tech/md/documentation/operations/distributed_deployment/?s=replication) ## Filtered Search Is Slow Use when: filtered search is significantly slower than unfiltered. Most common SA complaint after memory. - Create payload index on the filtered field [Payload index](https://search.qdrant.tech/md/documentation/manage-data/indexing/?s=payload-index) - Use `is_tenant=true` for primary filtering condition: [Tenant index](https://search.qdrant.tech/md/documentation/manage-data/indexing/?s=tenant-index) - Try ACORN algorithm for complex filters: [ACORN](https://search.qdrant.tech/md/documentation/search/search/?s=acorn-search-algorithm) - Avoid using `nested` filtering conditions as a primary filter. It might force qdrant to read raw payload values instead of using index. - If payload index was added after HNSW build, trigger re-index to create filterable subgraph links ## Optimize search performance with parallel updates ### Diagnostic steps - Try to run the same query with `indexed_only=true` parameter, if the query is significantly faster, it means that the optimizer is still running and has not yet indexed all segments. - If CPU or IO usage is high even with no queries, it also indicates that the optimizer is still running. ### Recommended configuration changes - reduce `optimizer_cpu_budget` to reserve more CPU for queries - Use `prevent_unoptimized=true` to prevent creating segments with a large amount of unindexed data for searches. Instead, once a segment reaches the so called indexing_threshold, all additional points will be added in ‘deferred state’. Learn more [here](https://search.qdrant.tech/md/documentation/search/low-latency-search/?s=query-indexed-data-only) ## What NOT to Do - Set `always_ram=false` on quantization (disk thrashing on every search) - Put HNSW on disk for latency-sensitive production (only for cold storage) - Increase segment count for throughput (opposite: fewer = better) - Create payload indexes on every field (wastes memory) - Blame Qdrant before checking optimizer status