See #39173
In this PR:
- Adjusted the delta log serialization APIs.
- Refactored the stats collector to improve the collection and digest of
primary key and BM25 statistics.
- Introduced new tests for the delta log reader/writer and stats
collectors to ensure functionality and correctness.
---------
Signed-off-by: Ted Xu <ted.xu@zilliz.com>
issue: #43427
This pr's main goal is merge #37417 to milvus 2.5 without conflicts.
# Main Goals
1. Create and describe collections with geospatial type
2. Insert geospatial data into the insert binlog
3. Load segments containing geospatial data into memory
4. Enable query and search can display geospatial data
5. Support using GIS funtions like ST_EQUALS in query
6. Support R-Tree index for geometry type
# Solution
1. **Add Type**: Modify the Milvus core by adding a Geospatial type in
both the C++ and Go code layers, defining the Geospatial data structure
and the corresponding interfaces.
2. **Dependency Libraries**: Introduce necessary geospatial data
processing libraries. In the C++ source code, use Conan package
management to include the GDAL library. In the Go source code, add the
go-geom library to the go.mod file.
3. **Protocol Interface**: Revise the Milvus protocol to provide
mechanisms for Geospatial message serialization and deserialization.
4. **Data Pipeline**: Facilitate interaction between the client and
proxy using the WKT format for geospatial data. The proxy will convert
all data into WKB format for downstream processing, providing column
data interfaces, segment encapsulation, segment loading, payload
writing, and cache block management.
5. **Query Operators**: Implement simple display and support for filter
queries. Initially, focus on filtering based on spatial relationships
for a single column of geospatial literal values, providing parsing and
execution for query expressions.Now only support brutal search
7. **Client Modification**: Enable the client to handle user input for
geospatial data and facilitate end-to-end testing.Check the modification
in pymilvus.
---------
Signed-off-by: Yinwei Li <yinwei.li@zilliz.com>
Signed-off-by: Cai Zhang <cai.zhang@zilliz.com>
Co-authored-by: ZhuXi <150327960+Yinwei-Yu@users.noreply.github.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>
Ref https://github.com/milvus-io/milvus/issues/42148https://github.com/milvus-io/milvus/pull/42406 impls the segcore part of
storage for handling with VectorArray.
This PR:
1. impls the go part of storage for VectorArray
2. impls the collection creation with StructArrayField and VectorArray
3. insert and retrieve data from the collection.
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
Signed-off-by: SpadeA-Tang <tangchenjie1210@gmail.com>
Signed-off-by: SpadeA-Tang <u6748471@anu.edu.au>
This parameter determines whether the returned value should be a copy or
a reference from the arrow array. The updates enhance memory management
and provide more control over data handling during deserialization.
See #43186
---------
Signed-off-by: Ted Xu <ted.xu@zilliz.com>
Related to #43522
Currently, passing partial schema to storage v2 packed reader may
trigger SEGV during clustering compaction unit test.
This patch implement `NeededFields` differently in each `RecordReader`
imlementation. For now, v2 will implemented as no-op. This will be
supported after packed reader support this API.
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
Fix issues in end-to-end tests:
1. **Split column groups based on schema**, rather than estimating by
average chunk row size. **Ensure column group consistency within a
segment**, to avoid errors caused by loading multiple column group
chunks simultaneously.
2. **Use sorted segmentId** when generating the stats binlog path, to
ensure consistent and correct file path resolution.
3. **Determine field IDs as follows**:
For multi-column column groups, retrieve the field ID list from
metadata.
For single-column column groups, use the column group ID directly as the
field ID.
related: #39173fix: #42862
---------
Signed-off-by: shaoting-huang <shaoting.huang@zilliz.com>
- Feat: Support Mix compaction. Covering tests include compatibility and
rollback ability.
- Read v1 segments and compact with v2 format.
- Read both v1 and v2 segments and compact with v2 format.
- Read v2 segments and compact with v2 format.
- Compact with duplicate primary key test.
- Compact with bm25 segments.
- Compact with merge sort segments.
- Compact with no expiration segments.
- Compact with lack binlog segments.
- Compact with nullable field segments.
- Feat: Support Clustering compaction. Covering tests include
compatibility and rollback ability.
- Read v1 segments and compact with v2 format.
- Read both v1 and v2 segments and compact with v2 format.
- Read v2 segments and compact with v2 format.
- Compact bm25 segments with v2 format.
- Compact with memory limit.
- Enhance: Use serdeMap serialize in BuildRecord function to support all
Milvus data types.
related: #39173
Signed-off-by: shaoting-huang <shaoting.huang@zilliz.com>
issue: https://github.com/milvus-io/milvus/issues/39818
This PR mimics Varchar data type, allows insert, search, query, delete,
full-text search and others.
Functionalities related to filter expressions are disabled temporarily.
Storage changes for Text data type will be in the following PRs.
Signed-off-by: Patrick Weizhi Xu <weizhi.xu@zilliz.com>
issue:https://github.com/milvus-io/milvus/issues/27576
# Main Goals
1. Create and describe collections with geospatial fields, enabling both
client and server to recognize and process geo fields.
2. Insert geospatial data as payload values in the insert binlog, and
print the values for verification.
3. Load segments containing geospatial data into memory.
4. Ensure query outputs can display geospatial data.
5. Support filtering on GIS functions for geospatial columns.
# Solution
1. **Add Type**: Modify the Milvus core by adding a Geospatial type in
both the C++ and Go code layers, defining the Geospatial data structure
and the corresponding interfaces.
2. **Dependency Libraries**: Introduce necessary geospatial data
processing libraries. In the C++ source code, use Conan package
management to include the GDAL library. In the Go source code, add the
go-geom library to the go.mod file.
3. **Protocol Interface**: Revise the Milvus protocol to provide
mechanisms for Geospatial message serialization and deserialization.
4. **Data Pipeline**: Facilitate interaction between the client and
proxy using the WKT format for geospatial data. The proxy will convert
all data into WKB format for downstream processing, providing column
data interfaces, segment encapsulation, segment loading, payload
writing, and cache block management.
5. **Query Operators**: Implement simple display and support for filter
queries. Initially, focus on filtering based on spatial relationships
for a single column of geospatial literal values, providing parsing and
execution for query expressions.
6. **Client Modification**: Enable the client to handle user input for
geospatial data and facilitate end-to-end testing.Check the modification
in pymilvus.
---------
Signed-off-by: tasty-gumi <1021989072@qq.com>
See also #34746
This PR add segment level field in response of
`GetPersistentSegmentInfo` and `GetQuerySegmentInfo`
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
issue: #34357
Go Parquet uses dictionary encoding by default, and it will fall back to
plain encoding if the dictionary size exceeds the dictionary size page
limit. Users can specify custom fallback encoding by using
`parquet.WithEncoding(ENCODING_METHOD)` in writer properties. However,
Go Parquet [fallbacks to plain
encoding](e65c1e295d/go/parquet/file/column_writer_types.gen.go.tmpl (L238))
rather than custom encoding method users provide. Therefore, this patch
only turns off dictionary encoding for the primary key.
With a 5 million auto ID primary key benchmark, the parquet file size
improves from 13.93 MB to 8.36 MB when dictionary encoding is turned
off, reducing primary key storage space by 40%.
Signed-off-by: shaoting-huang <shaoting.huang@zilliz.com>
issue: #34123
Benchmark case: The benchmark run the go benchmark function
`BenchmarkDeltalogFormat` which is put in the Files changed. It tests
the performance of serializing and deserializing from two different data
formats under a 10 million delete log dataset.
Metrics: The benchmarks measure the average time taken per operation
(ns/op), memory allocated per operation (MB/op), and the number of
memory allocations per operation (allocs/op).
| Test Name | Avg Time (ns/op) | Time Comparison | Memory Allocation
(MB/op) | Memory Comparison | Allocation Count (allocs/op) | Allocation
Comparison |
|---------------------------------|------------------|-----------------|---------------------------|-------------------|------------------------------|------------------------|
| one_string_format_reader | 2,781,990,000 | Baseline | 2,422 | Baseline
| 20,336,539 | Baseline |
| pk_ts_separate_format_reader | 480,682,639 | -82.72% | 1,765 | -27.14%
| 20,396,958 | +0.30% |
| one_string_format_writer | 5,483,436,041 | Baseline | 13,900 |
Baseline | 70,057,473 | Baseline |
| pk_and_ts_separate_format_writer| 798,591,584 | -85.43% | 2,178 |
-84.34% | 30,270,488 | -56.78% |
Both read and write operations show significant improvements in both
speed and memory allocation.
Signed-off-by: shaoting-huang <shaoting.huang@zilliz.com>
