Partition¶

Constructor¶

Attributes¶

Methods¶

API Refereences¶

class pymilvus.Partition(collection, name, description='', **kwargs)¶

property description¶

Return the description text.

Return str

Partition description text, return when operation is successful

Example

>>> from pymilvus import connections, Collection, Partition, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_partition_description", schema)
>>> partition = Partition(collection, "comedy", "comedy films")
>>> partition.description
'comedy films'

property name¶

Return the partition name.

Return str

Partition name, return when operation is successful

Example

>>> from pymilvus import connections, Collection, Partition, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_partition_name", schema)
>>> partition = Partition(collection, "comedy", "comedy films")
>>> partition.name
'comedy'

property is_empty¶

Returns whether the partition is empty

Return bool

Whether the partition is empty

• True: The partition is empty.

• False: The partition is not empty.

Example

>>> from pymilvus import connections, Collection, Partition, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_partition_is_empty", schema)
>>> partition = Partition(collection, "comedy", "comedy films")
>>> partition.is_empty
True

property num_entities¶

Return the number of entities.

Return int

Number of entities in this partition.

Example

>>> from pymilvus import connections, Collection, Partition, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_partition_num_entities", schema)
>>> partition = Partition(collection, "comedy", "comedy films")
>>> data = [
...     [i for i in range(10)],
...     [[float(i) for i in range(2)] for _ in range(10)],
... ]
>>> partition.insert(data)
>>> partition.num_entities
10

drop(timeout=None, **kwargs)¶

Drop the partition, as well as its corresponding index files.

Parameters

timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur

Raises

PartitionNotExistException -- When partitoin does not exist

Example

>>> from pymilvus import connections, Collection, Partition, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_partition_drop", schema)
>>> partition = Partition(collection, "comedy", "comedy films")
>>> partition.drop()

load(timeout=None, **kwargs)¶

Load the partition from disk to memory.

Parameters

timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur

Raises

InvalidArgumentException -- If argument is not valid

Example

>>> from pymilvus import connections, Collection, Partition, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_partition_load", schema)
>>> partition = Partition(collection, "comedy", "comedy films")
>>> partition.load()

release(timeout=None, **kwargs)¶

Release the partition from memory.

Parameters

timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur

Raises

PartitionNotExistException -- When partitoin does not exist

Example

>>> from pymilvus import connections, Collection, Partition, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_partition_release", schema)
>>> partition = Partition(collection, "comedy", "comedy films")
>>> partition.load()
>>> partition.release()

insert(data, timeout=None, **kwargs)¶

Insert data into partition.

Parameters

• data (list-like(list, tuple) object or pandas.DataFrame) -- The specified data to insert, the dimension of data needs to align with column number

• timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur

• kwargs --

  • timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur.

Returns

A MutationResult object contains a property named insert_count represents how many

entities have been inserted into milvus and a property named primary_keys is a list of primary keys of the inserted entities. :rtype: MutationResult

Raises

PartitionNotExistException -- When partitoin does not exist

Example

>>> from pymilvus import connections, Collection, Partition, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_partition_insert", schema)
>>> partition = Partition(collection, "comedy", "comedy films")
>>> data = [
...     [i for i in range(10)],
...     [[float(i) for i in range(2)] for _ in range(10)],
... ]
>>> partition.insert(data)
>>> partition.num_entities
10

delete(expr, timeout=None, **kwargs)¶

Delete entities with an expression condition.

Parameters

• expr (str) -- The expression to specify entities to be deleted

• timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur

Returns

A MutationResult object contains a property named delete_count represents how many entities will be deleted.

Return type

MutationResult

Raises

• RpcError -- If gRPC encounter an error

• ParamError -- If parameters are invalid

• BaseException -- If the return result from server is not ok

Example

>>> from pymilvus import connections, Collection, Partition, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", DataType.FLOAT_VECTOR, dim=2)
... ])
>>> test_collection = Collection("test_partition_delete", schema)
>>> test_partition = test_collection.create_partition("comedy", "comedy films")
>>> data = [
...     [i for i in range(10)],
...     [[float(i) for i in range(2)] for _ in range(10)],
... ]
>>> test_partition.insert(data)
(insert count: 10, delete count: 0, upsert count: 0, timestamp: 431044482906718212)
>>> test_partition.num_entities
10
>>> test_partition.delete("film_id in [0, 1]")
(insert count: 0, delete count: 2, upsert count: 0, timestamp: 431044582560759811)

search(data, anns_field, param, limit, expr=None, output_fields=None, timeout=None, round_decimal=-1, **kwargs)¶

Vector similarity search with an optional boolean expression as filters.

Parameters

• data (list[list[float]]) -- The vectors of search data, the length of data is number of query (nq), the dim of every vector in data must be equal to vector field's of collection.

• anns_field (str) -- The vector field used to search of collection.

• param (dict) -- The parameters of search, such as nprobe, etc.

• limit -- The max number of returned record, we also called this parameter as topk.

• round_decimal (int) -- The specified number of decimal places of returned distance

• expr (str) -- The boolean expression used to filter attribute.

• output_fields (list[str]) -- The fields to return in the search result, not supported now.

• timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur.

• kwargs --

  • _async (bool) -- Indicate if invoke asynchronously. When value is true, method returns a SearchFuture object; otherwise, method returns results from server directly.

  • _callback (function) -- The callback function which is invoked after server response successfully. It only takes effect when _async is set to True.

  • consistency_level (str/int) -- Which consistency level to use when searching in the partition. For details, see https://github.com/milvus-io/milvus/blob/master/docs/developer_guides/how-guarantee-ts-works.md. Note: this parameter will overwrite the same parameter specified when user created the collection, if no consistency level was specified, search will use the consistency level when you create the collection.

  • guarantee_timestamp (int) -- This function instructs Milvus to see all operations performed before a provided timestamp. If no such timestamp is provided, then Milvus will search all operations performed to date. Note: only used in Customized consistency level.

  • graceful_time (int) -- Only used in bounded consistency level. If graceful_time is set, PyMilvus will use current timestamp minus the graceful_time as the guarantee_timestamp. This option is 5s by default if not set.

  • travel_timestamp (int) -- Users can specify a timestamp in a search to get results based on a data view

    at a specified point in time.

Returns

SearchResult: SearchResult is iterable and is a 2d-array-like class, the first dimension is the number of vectors to query (nq), the second dimension is the number of limit(topk).

Return type

SearchResult

Raises

• RpcError -- If gRPC encounter an error.

• ParamError -- If parameters are invalid.

• BaseException -- If the return result from server is not ok.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> import random
>>> connections.connect()

>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_search", schema)
>>> partition = Partition(collection, "comedy", "comedy films")
>>> # insert
>>> data = [
...     [i for i in range(10)],
...     [[random.random() for _ in range(2)] for _ in range(10)],
... ]
>>> partition.insert(data)
>>> partition.num_entities
10
>>> partition.load()
>>> # search
>>> search_param = {
...     "data": [[1.0, 1.0]],
...     "anns_field": "films",
...     "param": {"metric_type": "L2"},
...     "limit": 2,
...     "expr": "film_id > 0",
... }
>>> res = partition.search(**search_param)
>>> assert len(res) == 1
>>> hits = res[0]
>>> assert len(hits) == 2
>>> print(f"- Total hits: {len(hits)}, hits ids: {hits.ids} ")
- Total hits: 2, hits ids: [8, 5]
>>> print(f"- Top1 hit id: {hits[0].id}, distance: {hits[0].distance}, score: {hits[0].score} ")
- Top1 hit id: 8, distance: 0.10143111646175385, score: 0.10143111646175385

query(expr, output_fields=None, timeout=None, **kwargs)¶

Query with a set of criteria, and results in a list of records that match the query exactly.

Parameters

• expr (str) -- The query expression

• output_fields (list[str]) -- A list of fields to return

• timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur

• kwargs --

  • consistency_level (str/int) -- Which consistency level to use during a query on the collection. For details, see https://github.com/milvus-io/milvus/blob/master/docs/developer_guides/how-guarantee-ts-works.md. Note: this parameter will overwrite the same parameter specified when user created the collection, if no consistency level was specified, query will use the consistency level when you create the collection.

  • guarantee_timestamp (int) -- This function instructs Milvus to see all operations performed before a provided timestamp. If no such timestamp is specified, Milvus will query all operations performed to date. Note: only used in Customized consistency level.

  • graceful_time (int) -- Only used in bounded consistency level. If graceful_time is set, PyMilvus will use current timestamp minus the graceful_time as the guarantee_timestamp. This option is 5s by default if not set.

  • travel_timestamp (int) -- Users can specify a timestamp in a search to get results based on a data view

    at a specified point in time.

Returns

A list that contains all results

Return type

list

Raises

• RpcError -- If gRPC encounter an error

• ParamError -- If parameters are invalid

• BaseException -- If the return result from server is not ok

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> import random
>>> connections.connect()

>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("film_date", DataType.INT64),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_query", schema)
>>> partition = Partition(collection, "comedy", "comedy films")
>>> # insert
>>> data = [
...     [i for i in range(10)],
...     [i + 2000 for i in range(10)],
...     [[random.random() for _ in range(2)] for _ in range(10)],
... ]
>>> partition.insert(data)
>>> partition.num_entities
10
>>> partition.load()
>>> # query
>>> expr = "film_id in [ 0, 1 ]"
>>> res = partition.query(expr, output_fields=["film_date"])
>>> assert len(res) == 2
>>> print(f"- Query results: {res}")
- Query results: [{'film_id': 0, 'film_date': 2000}, {'film_id': 1, 'film_date': 2001}]