issue: https://github.com/milvus-io/milvus/issues/42032
- Use bytes to estimate load resource in the whole estimation procedure
- Add num_rows and dim info for vector index to better estimate
- Disable eviction for tiered index's meta
---------
Signed-off-by: chasingegg <chao.gao@zilliz.com>
issue: https://github.com/milvus-io/milvus/issues/42148
Optimized from
Go VectorArray → VectorArray Proto → Binary → C++ VectorArray Proto →
C++ VectorArray local impl → Memory
to
Go VectorArray → Arrow ListArray → Memory
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.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>
Related to #43655
This patch add a padding when writing mmap file for ScalarSortedIndex in
case of mmap falure due to 0 mmap length.
Signed-off-by: Congqi Xia <congqi.xia@zilliz.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>
fix: https://github.com/milvus-io/milvus/issues/43354
The current implementation of stdsort index is not supported for
std::string. Remove the code.
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
Ref https://github.com/milvus-io/milvus/issues/42053
This PR enable ngram to support more kinds of matches such as prefix and
postfix match.
---------
Signed-off-by: SpadeA <tangchenjie1210@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>
Ref #42053
This is the first PR for optimizing `LIKE` with ngram inverted index.
Now, only VARCHAR data type is supported and only InnerMatch LIKE
(%xxx%) query is supported.
How to use it:
```
milvus_client = MilvusClient("http://localhost:19530")
schema = milvus_client.create_schema()
...
schema.add_field("content_ngram", DataType.VARCHAR, max_length=10000)
...
index_params = milvus_client.prepare_index_params()
index_params.add_index(field_name="content_ngram", index_type="NGRAM", index_name="ngram_index", min_gram=2, max_gram=3)
milvus_client.create_collection(COLLECTION_NAME, ...)
```
min_gram and max_gram controls how we tokenize the documents. For
example, for min_gram=2 and max_gram=4, we will tokenize each document
with 2-gram, 3-gram and 4-gram.
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
Signed-off-by: SpadeA-Tang <tangchenjie1210@gmail.com>
ref https://github.com/milvus-io/milvus/issues/42626
This PR makes text match index and json key stats index be loaded based
on mmap config.
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
Ref #42626
This path tidy up path for scalar index including path for loading index
from remote storage and temporary path for buliding index.
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
Ref https://github.com/milvus-io/milvus/issues/42148
This PR mainly enables segcore to support array of vector (read and
write, but not indexing). Now only float vector as the element type is
supported.
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
Signed-off-by: SpadeA-Tang <tangchenjie1210@gmail.com>
