related: #36380
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
- Core invariant: aggregation is centralized and schema-aware — all
aggregate functions are created via the exec Aggregate registry
(milvus::exec::Aggregate) and validated by ValidateAggFieldType, use a
single in-memory accumulator layout (Accumulator/RowContainer) and
grouping primitives (GroupingSet, HashTable, VectorHasher), ensuring
consistent typing, null semantics and offsets across planner → exec →
reducer conversion paths (toAggregateInfo, Aggregate::create,
GroupingSet, AggResult converters).
- Removed / simplified logic: removed ad‑hoc count/group-by and reducer
code (CountNode/PhyCountNode, GroupByNode/PhyGroupByNode, cntReducer and
its tests) and consolidated into a unified AggregationNode →
PhyAggregationNode + GroupingSet + HashTable execution path and
centralized reducers (MilvusAggReducer, InternalAggReducer,
SegcoreAggReducer). AVG now implemented compositionally (SUM + COUNT)
rather than a bespoke operator, eliminating duplicate implementations.
- Why this does NOT cause data loss or regressions: existing data-access
and serialization paths are preserved and explicitly validated —
bulk_subscript / bulk_script_field_data and FieldData creation are used
for output materialization; converters (InternalResult2AggResult ↔
AggResult2internalResult, SegcoreResults2AggResult ↔
AggResult2segcoreResult) enforce shape/type/row-count validation; proxy
and plan-level checks (MatchAggregationExpression,
translateOutputFields, ValidateAggFieldType, translateGroupByFieldIds)
reject unsupported inputs (ARRAY/JSON, unsupported datatypes) early.
Empty-result generation and explicit error returns guard against silent
corruption.
- New capability and scope: end-to-end GROUP BY and aggregation support
added across the stack — proto (plan.proto, RetrieveRequest fields
group_by_field_ids/aggregates), planner nodes (AggregationNode,
ProjectNode, SearchGroupByNode), exec operators (PhyAggregationNode,
PhyProjectNode) and aggregation core (Aggregate implementations:
Sum/Count/Min/Max, SimpleNumericAggregate, RowContainer, GroupingSet,
HashTable) plus proxy/querynode reducers and tests — enabling grouped
and global aggregation (sum, count, min, max, avg via sum+count) with
schema-aware validation and reduction.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Signed-off-by: MrPresent-Han <chun.han@gmail.com>
Co-authored-by: MrPresent-Han <chun.han@gmail.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/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>
