Diagnose a problem

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
• Enable in-memory quantization: Scalar quantization
• Reduce Vector Dimensionality with Matryoshka Models: Matryoshka Models
• Use oversampling + rescore for high-dimensional vectors Search with quantization
• Enable io_uring for disk-heavy workloads on Linux 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
• Use batch search API instead of single queries Batch search
• Enable quantization to reduce CPU cost Scalar quantization
• Add replicas to distribute read load Replication

Filtered Search Is Slow