Related to #31293
Implement QueryIterator for the Go SDK to enable efficient iteration
over large query result sets using PK-based pagination.
Key changes:
- Add QueryIterator interface and implementation with PK-based
pagination
- Support Int64 and VarChar primary key types for pagination filtering
- Add QueryIteratorOption with batchSize, limit, filter, outputFields
config
- Fix ResultSet.Slice to handle Query results without IDs/Scores
- Add comprehensive unit tests and integration tests
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
- Core invariant: the iterator requires the collection primary key (PK)
to be present in outputFields so PK-based pagination and accurate row
counting work. The constructor enforces this by appending the PK to
outputFields when absent, and all pagination (lastPK tracking, PK-range
filters) and ResultCount calculations depend on that guaranteed PK
column.
- New capability: adds a public QueryIterator API (Client.QueryIterator,
QueryIterator interface, QueryIteratorOption) that issues server-side
Query RPCs in configurable batches and implements PK-based pagination
supporting Int64 and VarChar PKs, with options for batchSize, limit,
filter, outputFields and an upfront first-batch validation to fail fast
on invalid params.
- Removed/simplified logic: ResultSet.Slice no longer assumes IDs and
Scores are always present — it branches on presence of IDs (use IDs
length when non-nil; otherwise derive row count from Fields[0]) and
guards Scores slicing. This eliminates redundant/unsafe assumptions and
centralizes correct row-count logic based on actual returned fields.
- No data loss or behavior regression: pagination composes the user
filter with a PK-range filter and always requests the PK field, so
lastPK is extracted from a real column and fetchNextBatch only advances
when rows are returned; EOF is returned only when the server returns no
rows or iterator limit is reached. ResultSet.Slice guards prevent panics
for queries that lack IDs/Scores; Query RPC → ResultSet.Fields remains
the authoritative path for row data, so rows are not dropped and
existing query behavior is preserved.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
issue: #43897
- Part of collection/index related DDL is implemented by WAL-based DDL
framework now.
- Support following message type in wal, CreateCollection,
DropCollection, CreatePartition, DropPartition, CreateIndex, AlterIndex,
DropIndex.
- Part of collection/index related DDL can be synced by new CDC now.
- Refactor some UT for collection/index DDL.
- Add Tombstone scheduler to manage the tombstone GC for collection or
partition meta.
- Move the vchannel allocation into streaming pchannel manager.
---------
Signed-off-by: chyezh <chyezh@outlook.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: #29735
Implement partial field update functionality for upsert operations,
supporting scalar, vector, and dynamic JSON fields without requiring all
collection fields.
Changes:
- Add queryPreExecute to retrieve existing records before upsert
- Implement UpdateFieldData function for merging data
- Add IDsChecker utility for efficient primary key lookups
- Fix JSON data creation in tests using proper map marshaling
- Add test cases for partial updates of different field types
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
Related to #39095
Previous PR #39990 update pkg module path using "/v2" package name, this
PR update milvusclient go sdk dependency for this update
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
Related to #31293
Client source files under client pkg cannot be evaluate correctly by
codecov. This PR moves them to `milvusclient` sub-package to fix this
issue and follow go major version best practice.
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
See also #34483
Some lint issues are introduced due to lack of static check run. This PR
fixes these problems.
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
