issue: #30361
- Delete may be lost when segment is not data-loaded status in lru
cache. skip filtering to fix it.
- `stats_` and `variable_fields_avg_size_` should be reset when
`ReleaseData`
- Remove repeat load delta log operation in lru.
---------
Signed-off-by: chyezh <chyezh@outlook.com>
issue: #29892
This PR:
1. Move the process of gathering materialized search info to when the
search plan is created, before it goes to each segment, to avoid
repeated work and access the plan node under multi-threaded
circumstances.
2. Enforce the supported MV type to `VARCHAR`
3. Add integration test
Signed-off-by: Patrick Weizhi Xu <weizhi.xu@zilliz.com>
Issue: #31752
This PR improves the performance for bitset utilities (introduced in PR
#30454), including varchar filtering
Signed-off-by: Alexandr Guzhva <alexanderguzhva@gmail.com>
issue: #29892
This PR
1. Pass Materialized View (MV) search information obtained from the
expression parsing planning procedure to Knowhere. It only performs when
MV is enabled and the partition key is involved in the expression. The
search information includes:
1. Touched field_id and the count of related categories in the
expression. E.g., `color == red && color == blue` yields `field_id ->
2`.
2. Whether the expression only includes AND (&&) logical operator,
default `true`.
3. Whether the expression has NOT (!) operator, default `false`.
4. Store if turning on MV on the proxy to eliminate reading from
paramtable for every search request.
5. Renames to MV.
## Rebuttals
1. Did not write in `ExtractInfoPlanNodeVisitor` since the new scalar
framework was introduced and this part might be removed in the future.
2. Currently only interested in `==` and `in` expression, `string` data
type, anything else is a bonus.
3. Leave handling expressions like `F == A || F == A` for future works
of the optimizer.
## Detailed MV Info
Signed-off-by: Patrick Weizhi Xu <weizhi.xu@zilliz.com>
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>
/kind improvement
this removes the 1x copying while loading variable length data, also
avoids constructing std::string, which could lead to memory
fragmentation
---------
Signed-off-by: yah01 <yah2er0ne@outlook.com>
Signed-off-by: longjiquan <jiquan.long@zilliz.com>
Co-authored-by: yah01 <yah2er0ne@outlook.com>
issue: https://github.com/milvus-io/milvus/issues/30687
We store all the varchar datas in an continuous address and use
string_view to quickly find them. In this case, using string_view.data()
directly will point to all rest varchar datas.
---------
Signed-off-by: longjiquan <jiquan.long@zilliz.com>
See also #30651
Append operator of `std::filesystem::path` will replace whole path when
the param of "/" operation is an absolute path.
In "All-in-one" mode, this shall cause ChunkCache removing the original
vector data file when building chunk cache during/after load procedure.
This PR changes the ChunkCache path generation logic to a separate
function in which will check whether the file path is absolute or not.
If the file path is absolute, it removes the root path prefix and return
concatenated file path.
---------
Signed-off-by: Congqi Xia <congqi.xia@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>
Allows proactive warming up of chunk cache. Original vector data will be
asynchronously loaded into the chunk cache during the load process. It
has the potential to significantly reduce query/search latency for a
certain duration after the load, albeit with a concurrent increase in
disk usage.
issue: https://github.com/milvus-io/milvus/issues/30181
---------
Signed-off-by: bigsheeper <yihao.dai@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>
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: #29672
the storage account need privileges of actions
`Microsoft.Storage/storageAccounts/blobServices/containers/blobs/*` at
least
Signed-off-by: PowderLi <min.li@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>
