optimize()

• is_l0 (bool) -

  Whether to run L0 compaction.

• target_size (int) -

  Target segment size after compaction. Must be a positive integer. If omitted, the server default is used.

• target_size_unit (str) -

  Unit for target_size. Supported values are "b", "kb", "mb", "gb", "tb", and "pb". The client converts this value to MB before sending the request.

This operation compacts small segments in a collection and returns a compaction job ID that you can poll for progress.

Request Syntax

client.optimize(
    collection_name: str,
    is_clustering: bool = False,
    is_l0: bool = False,
    target_size: int | None = None,
    target_size_unit: str = "mb",
    wait: bool = True,
    timeout: float | None = None,
)

PARAMETERS:

• collection_name (str) -

  [REQUIRED]

  The name of the collection to optimize.

• is_clustering (bool) -

  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) -

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

RETURN TYPE: OptimizeResult | OptimizeTask

Returns an OptimizeResult when wait=True, or an OptimizeTask when wait=False.

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.

Examples

from pymilvus import MilvusClient

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

# Wait for completion
result = client.optimize(
    collection_name="book",
    target_size=512,
    target_size_unit="mb",
    wait=True,
)
print(result)

# Run asynchronously
task = client.optimize(
    collection_name="book",
    is_clustering=True,
    target_size=1,
    target_size_unit="gb",
    wait=False,
)
print(task.job_id)