compact()

This operation compacts the collection by merging small segments into larger ones. It is recommended to call this operation after inserting a large amount of data into a collection.

Request Syntax

compact(
    collection_name: str,
    is_clustering: Optional[bool] = False,
    is_l0: Optional[bool] = False,
    target_size: Optional[int] = None,
    target_size_unit: str = "mb",
    timeout: Optional[float] = None,
    **kwargs
) -> int

PARAMETERS:

• collection_name (str) -

  [REQUIRED]

  The name of the target collection.

• is_clustering (bool) -

  Whether to perform a clustering compaction. Defaults to False.

• is_l0 (bool) -

  Whether to perform an L0 compaction, which specifically handles L0 segments by merging delete operations into existing data segments. Defaults to False.

• target_size (Optional[int]) -

  Target segment size after compaction. Must be a positive integer. The unit is specified by target_size_unit (default MB). If not provided, the server uses its default target size.

• target_size_unit (str) -

Unit for target_size. Supported values: "b", "kb", “mb” (default), "gb", "tb", "pb". The value is converted to MB before being sent to the server.

• timeout (Optional[float]) -

  The timeout duration for this operation. Setting this to None indicates that this operation timeouts when any response arrives or any error occurs.

RETURN TYPE:

int

RETURNS:

A compaction job ID, which can be used to get the compaction status.

EXCEPTIONS:

• MilvusException

  This exception will be raised when any error occurs during this operation.

Examples

from pymilvus import MilvusClient

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

# Standard compaction
job_id = client.compact(
    collection_name="my_collection"
)

# Clustering compaction
job_id = client.compact(
    collection_name="my_collection",
    is_clustering=True
)

# L0 compaction
job_id = client.compact(
    collection_name="my_collection",
    is_l0=True
)

#Force merge compaction
job_id = client.compact(
    collection_name="target_collection",
    target_size="2048 MB"  
)

# Check compaction status
state = client.get_compaction_state(job_id)
print(state)