load_collection()

This operation loads the data of a specific collection into memory.

Request syntax

load_collection(
    collection_name: str, 
    replica_number: int
    timeout: Optional[float] = None
) -> None

PARAMETERS:

collection_name (str) -

[REQUIRED]

The name of a collection.
replica_number (int) -

The number of replicas to create on query nodes upon collection loading.
- For a Milvus Standalone deployment, the maximum value should be 1.
- For a Milvus Cluster deployment, the maximum value should be no greater than the queryNode.replicas in your Milvus configurations. For details on how to change Milvus configurations, refer to our configuration guides.
what is a replica?

In-memory replicas are replications of the same segment on multiple query nodes. If one query node has failed or is busy with a current search request when another arrives, the system can send new requests to an idle query node that has a replication of the same segment.

For details, refer to In-Memory Replica.
timeout (float | None) -

The timeout duration for this operation.

Setting this to None indicates that this operation timeouts when any response returns or error occurs.

RETURN TYPE:

NoneType

RETURNS:

None

EXCEPTIONS:

MilvusException

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

Examples

from pymilvus import MilvusClient, DataType

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

# 1. Create schema
schema = MilvusClient.create_schema(
    auto_id=False,
    enable_dynamic_field=False,
)

# 2. Add fields to schema
schema.add_field(field_name="my_id", datatype=DataType.INT64, is_primary=True)

# {
#     'auto_id': False, 
#     'description': '', 
#     'fields': [
#         {
#             'name': 'my_id', 
#             'description': '', 
#             'type': <DataType.INT64: 5>, 
#             'is_primary': True, 
#             'auto_id': False
#         }
#     ]
# }

schema.add_field(field_name="my_vector", datatype=DataType.FLOAT_VECTOR, dim=5)

# {
#     'auto_id': False, 
#     'description': '', 
#     'fields': [
#         {
#             'name': 'my_id', 
#             'description': '', 
#             'type': <DataType.INT64: 5>, 
#             'is_primary': True, 
#             'auto_id': False
#         }, 
#         {
#             'name': 'my_vector', 
#             'description': '', 
#             'type': <DataType.FLOAT_VECTOR: 101>, 
#             'params': {
#                 'dim': 5
#             }
#         }        
#     ]
# }

# 3. Create a collection
client.create_collection(
    collection_name="customized_setup",
    schema=schema
)

# 4. Prepare index parameters
index_params = client.prepare_index_params()

# 5. Add indexes
index_params.add_index(
    field_name="my_id",
    index_type="STL_SORT"
)

index_params.add_index(
    field_name="my_vector", 
    index_type="IVF_FLAT",
    metric_type="L2",
    params: {nlist: 1024}
)

# 6. Create indexes
client.create_index(
    collection_name="customized_setup",
    index_params=index_params
)

# 7. Load indexes
client.load_collection(
    collection_name="customized_setup",
    replica_number=2
    collection_name="customized_setup",
    replica_number=2
)

load_collection()

Request syntax

Examples

Related methods