index_building_progress()

This operation returns the progress of the index-building process.

Request Syntax

index_building_progress(
    collection_name: str,
    index_name: str = "",
    using: str = "default",
    timeout: float | None,
)

PARAMETERS:

• collection_name (str) -

  [REQUIRED]

  The name of an existing collection.

  Setting this to a non-existing collection leads to a CollectionNotExistException.

• index_name (str) -

  The name of the target index of this operation.

  If left unspecified, the default index applies. If the collection has multiple indexes, this parameter is mandatory.

  Setting this to a non-existing index leads to an IndexNotExistException.

• using (str) -

  The alias of the employed connection.

  The default value is default, indicating that this operation employs the default connection.

• timeout (float | None)

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

RETURN TYPE:

dict

RETURNS: A dictionary that contains the number of indexed entities as well as that of total entities in the specified collection. The dictionary has the following keys:

• total_rows (int)

  The total number of entities in the specified collection.

• indexed_rows (int)

  The number of indexed entities in the specified collection.

• pending_index_rows (int)

  The number of entities that are pending to be indexed.

EXCEPTIONS:

• CollectionNotExistException

  This exception will be raised if the specified collection does not exist.

• IndexNotExistException

  This exception will be raised if the specified index does not exist.

• AmbiguousIndexName

  This exception will be raised if multiple indexes exist but the index name is left unspecified.

Examples

from pymilvus import (
    connections, 
    Collection, 
    CollectionSchema, 
    FieldSchema, 
    DataType, 
    utility,
)

# Connection to localhost:19530
connections.connect()

# Create a collection
collection = Collection(
    name="test_collection",
    schema=CollectionSchema([
        FieldSchema("id", DataType.INT64, is_primary=True),
        FieldSchema("vector", DataType.FLOAT_VECTOR, dim=5)
    ])
)

# Create an index on a scalar field
collection.create_index(
    field_name="id"
)

# Set the index parameters
index_params = {
    "index_type": "IVF_FLAT",
    "metric_type": "COSINE",
    "params": {
        "nlist": 128
    }
}

# Create an index on the vector field
collection.create_index(
    field_name="vector", 
    index_params=index_params, 
    timeout=None
)

# List all indexes
utility.list_indexes(
    collection_name="test_collection"
) # ['_default_idx_101', '_default_idx_100']

# Get the building progress of a specific index
utility.index_building_progress(
    collection_name="test_collection",
    index_name="_default_idx_101"
)