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issue: https://github.com/milvus-io/milvus/issues/27704 Add inverted index for some data types in Milvus. This index type can save a lot of memory compared to loading all data into RAM and speed up the term query and range query. Supported: `INT8`, `INT16`, `INT32`, `INT64`, `FLOAT`, `DOUBLE`, `BOOL` and `VARCHAR`. Not supported: `ARRAY` and `JSON`. Note: - The inverted index for `VARCHAR` is not designed to serve full-text search now. We will treat every row as a whole keyword instead of tokenizing it into multiple terms. - The inverted index don't support retrieval well, so if you create inverted index for field, those operations which depend on the raw data will fallback to use chunk storage, which will bring some performance loss. For example, comparisons between two columns and retrieval of output fields. The inverted index is very easy to be used. Taking below collection as an example: ```python fields = [ FieldSchema(name="pk", dtype=DataType.VARCHAR, is_primary=True, auto_id=False, max_length=100), FieldSchema(name="int8", dtype=DataType.INT8), FieldSchema(name="int16", dtype=DataType.INT16), FieldSchema(name="int32", dtype=DataType.INT32), FieldSchema(name="int64", dtype=DataType.INT64), FieldSchema(name="float", dtype=DataType.FLOAT), FieldSchema(name="double", dtype=DataType.DOUBLE), FieldSchema(name="bool", dtype=DataType.BOOL), FieldSchema(name="varchar", dtype=DataType.VARCHAR, max_length=1000), FieldSchema(name="random", dtype=DataType.DOUBLE), FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=dim), ] schema = CollectionSchema(fields) collection = Collection("demo", schema) ``` Then we can simply create inverted index for field via: ```python index_type = "INVERTED" collection.create_index("int8", {"index_type": index_type}) collection.create_index("int16", {"index_type": index_type}) collection.create_index("int32", {"index_type": index_type}) collection.create_index("int64", {"index_type": index_type}) collection.create_index("float", {"index_type": index_type}) collection.create_index("double", {"index_type": index_type}) collection.create_index("bool", {"index_type": index_type}) collection.create_index("varchar", {"index_type": index_type}) ``` Then, term query and range query on the field can be speed up automatically by the inverted index: ```python result = collection.query(expr='int64 in [1, 2, 3]', output_fields=["pk"]) result = collection.query(expr='int64 < 5', output_fields=["pk"]) result = collection.query(expr='int64 > 2997', output_fields=["pk"]) result = collection.query(expr='1 < int64 < 5', output_fields=["pk"]) ``` --------- Signed-off-by: longjiquan <jiquan.long@zilliz.com> |
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| flowgraph | ||
| funcutil | ||
| grpcclient | ||
| importutil | ||
| indexcgowrapper | ||
| initcore | ||
| metrics | ||
| mock | ||
| pipeline | ||
| proxyutil | ||
| segmentutil | ||
| sessionutil | ||
| streamrpc | ||
| tsoutil | ||
| typeutil | ||
| wrappers | ||