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>
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: #28521#29732
include
1. list collection's import jobs
2. create a new import job
3. get the progress of an import job
fix:
1. mix the order of dbName & collectionName #29728
2. trace log keep the same as v1
3. support traceID
4. azure precheck, blob name cannot end with / #29703
---------
Signed-off-by: PowderLi <min.li@zilliz.com>
according to our benchmark, concurrency level 16 is enough to fully
utilize the object storage network bandwidth
Signed-off-by: yah01 <yang.cen@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>
before this, every time writting the index chunk data into the disk,
there are 4 I/O operations:
- open the file
- seek to the offset
- write the data
- close the file
this optimized this to open only once and continiously write all data.
This also makes it concurrent to load the files from object storage
Signed-off-by: yah01 <yang.cen@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>
issue: #29494
1. link with install path's libblob-chunk-manager
2. performance of `ShouldBindWith` is better than `ShouldBindBodyWith`
3. the middleware shouldn't read the unrefreshed parameter repeatly
Signed-off-by: PowderLi <min.li@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>
issue: #28781#28329
1. There is no need to call `DescribeCollection`, if the collection's
schema is found in the globalMetaCache
2. did `GetProperties` to check the access to Azure Blob Service while
construct the ChunkManager
Signed-off-by: PowderLi <min.li@zilliz.com>
