issue: #44212
Implement search/query storage usage statistics in go side(result
reduce), only record storage usage in vector search C++ path. Need to be
implemented in query c++ path in next prs.
---------
Signed-off-by: chasingegg <chao.gao@zilliz.com>
Signed-off-by: marcelo.chen <marcelo.chen@zilliz.com>
Co-authored-by: marcelo.chen <marcelo.chen@zilliz.com>
#42032
Also, fix the cacheoptfield method to work in storagev2.
Also, change the sparse related interface for knowhere version bump
#43974 .
Also, includes https://github.com/milvus-io/milvus/pull/44046 for metric
lost.
---------
Signed-off-by: chasingegg <chao.gao@zilliz.com>
Signed-off-by: marcelo.chen <marcelo.chen@zilliz.com>
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
Co-authored-by: marcelo.chen <marcelo.chen@zilliz.com>
Co-authored-by: Congqi Xia <congqi.xia@zilliz.com>
Ref https://github.com/milvus-io/milvus/issues/42148
This PR supports create index for vector array (now, only for
`DataType.FLOAT_VECTOR`) and search on it.
The index type supported in this PR is `EMB_LIST_HNSW` and the metric
type is `MAX_SIM` only.
The way to use it:
```python
milvus_client = MilvusClient("xxx:19530")
schema = milvus_client.create_schema(enable_dynamic_field=True, auto_id=True)
...
struct_schema = milvus_client.create_struct_array_field_schema("struct_array_field")
...
struct_schema.add_field("struct_float_vec", DataType.ARRAY_OF_VECTOR, element_type=DataType.FLOAT_VECTOR, dim=128, max_capacity=1000)
...
schema.add_struct_array_field(struct_schema)
index_params = milvus_client.prepare_index_params()
index_params.add_index(field_name="struct_float_vec", index_type="EMB_LIST_HNSW", metric_type="MAX_SIM", index_params={"nlist": 128})
...
milvus_client.create_index(COLLECTION_NAME, schema=schema, index_params=index_params)
```
Note: This PR uses `Lims` to convey offsets of the vector array to
knowhere where vectors of multiple vector arrays are concatenated and we
need offsets to specify which vectors belong to which vector array.
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
Signed-off-by: SpadeA-Tang <tangchenjie1210@gmail.com>
issue: #43088
issue: #43038
The current loading process:
* When loading an index, we first download the index files into a list
of buffers, say A
* then constructing(copying) them into a vector of FieldDatas(each file
is a FieldData), say B
* assembles them together as a huge BinarySet, say C
* lastly, copy into the actual index data structure, say D
The problem:
* We can see that, after each step, we don't need the data in previous
step.
* But currently, we release the memory of A, B, C only after we have
finished constructing D
* This leads to a up to 4x peak memory usage comparing with the raw
index size, during the loading process
* This PR allows timely releasing of B after we assembled C. So after
this PR, the peak memory usage during loading will be up to 3x of the
raw index size.
I will create another PR to release A after we created B, that seems
more complicated and need more work.
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
issue: #41435
this is to prevent AI from thinking of our exception throwing as a
dangerous PANIC operation that terminates the program.
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
issue: #43040
This patch introduces a disk file writer that supports Direct IO.
Currently, it is exclusively utilized during the QueryNode load process.
Below is its parameters:
1. `common.diskWriteMode`
This parameter controls the write mode of the local disk, which is used
to write temporary data downloaded from remote storage.
Currently, only QueryNode uses 'common.diskWrite*' parameters. Support
for other components will be added in the future.
The options include 'direct' and 'buffered'. The default value is
'buffered'.
2. `common.diskWriteBufferSizeKb`
Disk write buffer size in KB, only used when disk write mode is
'direct', default is 64KB.
Current valid range is [4, 65536]. If the value is not aligned to 4KB,
it will be rounded up to the nearest multiple of 4KB.
3. `common.diskWriteNumThreads`
This parameter controls the number of writer threads used for disk write
operations. The valid range is [0, hardware_concurrency].
It is designed to limit the maximum concurrency of disk write operations
to reduce the impact on disk read performance.
For example, if you want to limit the maximum concurrency of disk write
operations to 1, you can set this parameter to 1.
The default value is 0, which means the caller will perform write
operations directly without using an additional writer thread pool.
In this case, the maximum concurrency of disk write operations is
determined by the caller's thread pool size.
Both parameters can be updated during runtime.
---------
Signed-off-by: Shawn Wang <shawn.wang@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: #34685
knowhere needs a new json param `range_search_k` for RangeSearch to
early terminate the iterator.
Signed-off-by: min.tian <min.tian.cn@gmail.com>
add scalar filtering and vector search latency metrics to distinguish
the cost of scalar filtering.
To add metrics in query chain, add a monitor module and move the metric
files from original storage module.
issue: #34780
Signed-off-by: xianliang.li <xianliang.li@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>
