optimize()

This operation optimizes a collection by adjusting segment sizes to improve query performance. This method performs a sequence of operations: waiting for index building, triggering force-merge compaction, waiting for completion, rebuilding indexes, and refreshing the collection load.

Request syntax

client.optimize(
    collection_name: str,
    target_size: str = None,
    wait: bool = True,
    timeout: float = None
) -> Union[OptimizeResult, OptimizeTask]

PARAMETERS:

• collection_name (str) -

  [REQUIRED]

  The name of the collection to optimize.

• target_size (str | None) -

  Target segment size. Format: "1000MB", "1GB", "1.2gb". If not provided, uses the system default.

• wait (bool) -

  Whether to wait for optimization to complete. Defaults to True. If False, returns an OptimizeTask for async tracking.

• timeout (float | None) -

  Maximum time in seconds to wait for optimization. Only applies when wait=True.

RETURN TYPE:

OptimizeResult | OptimizeTask

RETURNS:

When wait=True, returns an OptimizeResult with status, collection_name, compaction_id, target_size, and progress. When wait=False, returns an OptimizeTask supporting done(), progress(), result(), and cancel().

EXCEPTIONS:

• ParamError

  This exception will be raised when collection_name is invalid or target_size format is incorrect.

• MilvusException

  This exception will be raised when index build fails, compaction fails, or timeout occurs.

Example

from pymilvus import MilvusClient

client = MilvusClient(
    uri="http://localhost:19530",
    token="root:Milvus"
)

# Synchronous optimization
result = client.optimize(
    collection_name="my_collection",
    target_size="512MB"
)

# Asynchronous optimization
task = client.optimize(
    collection_name="my_collection",
    target_size="512MB",
    wait=False
)
while not task.done():
    print(f"Progress: {task.progress()}")
    time.sleep(1)
result = task.result()