issue: #29988
This pr adds full-support for wildcard pattern matching from end to end.
Before this pr, the users can only use prefix match in their expression,
for example, "like 'prefix%'". With this pr, more flexible syntax can be
combined.
To do so, this pr makes these changes:
- 1. support regex query both on index and raw data;
- 2. translate the pattern matching to regex query, so that it can be
handled by the regex query logic;
- 3. loose the limit of the expression parsing, which allows general
pattern matching syntax;
With the support of regex query in segcore backend, we can also add
mysql-like `REGEXP` syntax later easily.
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Signed-off-by: longjiquan <jiquan.long@zilliz.com>
See also #29803
This PR:
- Add trace span for `LoadIndex` & `LoadFieldData` in segment loader
- Add `TraceCtx` parameter for `Index.Load` in segcore
- Add span for ReadFiles & Engine Load for Memory/Disk Vector index
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Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
When the TimeTravel functionality was previously removed, it
inadvertently affected the MVCC functionality within the system. This PR
aims to reintroduce the internal MVCC functionality as follows:
1. Add MvccTimestamp to the requests of Search/Query and the results of
Search internally.
2. When the delegator receives a Query/Search request and there is no
MVCC timestamp set in the request, set the delegator's current tsafe as
the MVCC timestamp of the request. If the request already has an MVCC
timestamp, do not modify it.
3. When the Proxy handles Search and triggers the second phase ReQuery,
divide the ReQuery into different shards and pass the MVCC timestamp to
the corresponding Query requests.
issue: #29656
Signed-off-by: zhenshan.cao <zhenshan.cao@zilliz.com>
related: #25324
Search GroupBy function, used to aggregate result entities based on a
specific scalar column.
several points to mention:
1. Temporarliy, the whole groupby is implemented separated from
iterative expr framework **for the first period**
2. In the long term, the groupBy operation will be incorporated into the
iterative expr framework:https://github.com/milvus-io/milvus/pull/28166
3. This pr includes some unrelated mocked interface regarding alterIndex
due to some unworth-to-mention reasons. All these un-associated content
will be removed before the final pr is merged. This version of pr is
only for review
4. All other related details were commented in the files comparison
Signed-off-by: MrPresent-Han <chun.han@zilliz.com>
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>
This will significantly reduce the memory usage while loading
- 1x memory usage and MBs overhead for buffer (memory mode)
- only MBs overhead for buffer (mmap mode)
Signed-off-by: yah01 <yang.cen@zilliz.com>
