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Optimizing the Performance of Vector Retrieval

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%"
    }
  }

• If the end-to-end latency is greater than the took value in the returned result, you can configure _source to reduce the fdt file size and reduce the fetch overhead.

  PUT my_index
  {
    "settings": {
      "index": {
        "vector": "true"
      },
      "index.soft_deletes.enabled": false
    },
    "mappings": {
      "_source": {
        "excludes": ["my_vector"]
      },
      "properties": {
        "my_vector": {
          "type": "vector",
          "dimension": 128,
          "indexing": true,
          "algorithm": "GRAPH",
          "metric": "euclidean"
        }
      }
    }
  }

Parent topic: Vector Retrieval