Related to #39173
`null_bitmap_data()` returns raw pointer of null bitmap of Array. While
after slicing, this bitmap is not rewritten due to zero copy
implementation, so the current start pos maybe non-zero while
FillFieldData generating column `valid_data` array.
This PR add `offset` param for `FillFieldData` method, and force all
invocation pass correct offset of `null_bitmap_data` ptr.
Also update milvus-storage commit fixing reader failed to return data
when buffer size smaller than row group size problem.
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
Related to #39173#39718
In storage v2, the `lack_bin_rows` cannot be used since field id is not
column group id, which will not be matched forever.
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
after the pr merged, we can support to insert, upsert, build index,
query, search in the added field.
can only do the above operates in added field after add field request
complete, which is a sync operate.
compact will be supported in the next pr.
#39718
---------
Signed-off-by: lixinguo <xinguo.li@zilliz.com>
Co-authored-by: lixinguo <xinguo.li@zilliz.com>
issue: #38715
- Current milvus use a serialized index size(compressed) for estimate
resource for loading.
- Add a new field `MemSize` (before compressing) for index to estimate
resource.
---------
Signed-off-by: chyezh <chyezh@outlook.com>
1. support read and write null in segcore
will store valid_data(use uint8_t type to save memory) in fieldData.
2. support load null
binlog reader read and write data into column(sealed segment),
insertRecord(growing segment). In sealed segment, store valid_data
directly. In growing segment, considering prior implementation and easy
code reading, it covert uint8_t to fbvector<bool>, which may optimize in
future.
3. retrieve valid_data.
parse valid_data in search/query.
#31728
---------
Signed-off-by: lixinguo <xinguo.li@zilliz.com>
Co-authored-by: lixinguo <xinguo.li@zilliz.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>
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>
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>
Chun Han
congqixia
zhagnlu
sthuang
Buqian Zheng
smellthemoon
Zhen Ye
Jiquan Long