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 } } } }