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Optimizing the Write and Query Performance of Vector Search

Optimizing Write Performance

• To reduce the cost of backup, disable the backup function before data import and enable it afterwards.
• Set refresh_interval to 120s or a larger value. Larger segments can reduce the vector index build overhead caused by merging.
• Increase the value of native.vector.index_threads (the default value is 4) to increase the number of threads for vector index build.

  PUT _cluster/settings
  {
    "persistent": {
      "native.vector.index_threads": 8
    }
  }

Optimizing Query Performance

• After importing data in batches, you can run the forcemerge command to improve the query efficiency.

  POST index_name/_forcemerge?max_num_segments=1

• If the off-heap memory required by the vector index exceeds the circuit breaker limit, index entry swap-in and swap-out occur, which affects the query performance. In this case, you can increase the circuit breaker threshold of off-heap memory.

  PUT _cluster/settings
  {
    "persistent": {
      "native.cache.circuit_breaker.cpu.limit": "75%"
    }
  }

• To fetch a small number of fields that support doc values, such as keywords and numerical fields, use the docvalue_fields parameter to specify the fields you want to fetch. This helps to reduce overhead during the fetch phase.

  POST my_index/_search
  {
    "size": 2,
    "stored_fields": ["_none_"], 
    "docvalue_fields": ["my_label"],
    "query": {
      "vector": {
        "my_vector": {
          "vector": [1, 1],
          "topk": 2
        }
      }
    }
  }

Parent topic: Configuring Vector Search for Elasticsearch Clusters