This PR adds the ability to search/get sparse float vectors in segcore,
and added unit tests by modifying lots of existing tests into
parameterized ones.
https://github.com/milvus-io/milvus/issues/29419
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
This commit adds sparse float vector support to segcore with the
following:
1. data type enum declarations
2. Adds corresponding data structures for handling sparse float vectors
in various scenarios, including:
* FieldData as a bridge between the binlog and the in memory data
structures
* mmap::Column as the in memory representation of a sparse float vector
column of a sealed segment;
* ConcurrentVector as the in memory representation of a sparse float
vector of a growing segment which supports inserts.
3. Adds logic in payload reader/writer to serialize/deserialize from/to
binlog
4. Adds the ability to allow the index node to build sparse float vector
index
5. Adds the ability to allow the query node to build growing index for
growing segment and temp index for sealed segment without index built
This commit also includes some code cleanness, comment improvement, and
some unit tests for sparse vector.
https://github.com/milvus-io/milvus/issues/29419
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
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.
---------
Signed-off-by: longjiquan <jiquan.long@zilliz.com>
issue: https://github.com/milvus-io/milvus/issues/29020
Json can't not pass a max_int32 value to int32_t, so let knowhere check
value range by itself.
After fix this, pymilvus will report:
pymilvus.exceptions.MilvusException: <MilvusException: (code=65535,
message=fail to search on QueryNode 6: worker(6) query failed: => failed
to search: arithmetic overflow: param search_list_size should be at most
2147483647)>
Signed-off-by: cqy123456 <qianya.cheng@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
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
related to : #29417
cardinal indexes upload index files in `Serialize` interface, and throw
exception when the `Serialize` failed.
Signed-off-by: xianliang <xianliang.li@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>
The tests need to call a private method, Milvus uses `#define` to
replace private with public, the hack trick works but would be broken if
the including order changed.
This uses friend to make all things work well
Signed-off-by: yah01 <yang.cen@zilliz.com>
Signed-off-by: yah01 <yah2er0ne@outlook.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>
