Help Center/ Cloud Search Service/ User Guide (Kuala Lumpur Region)/ Vector Retrieval/ Optimizing the Performance of Vector Retrieval
Updated on 2023-06-20 GMT+08:00
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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