issue: #46500
- simplify the run_go_codecov.sh to make sure the set -e to protect any
sub command failure.
- remove all embed etcd in test to make full test can be run at local.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## PR Summary: Simplify Go Unit Tests by Removing Embedded etcd and
Async Startup Scaffolding
**Core Invariant:**
This PR assumes that unit tests can be simplified by running without
embedded etcd servers (delegating to environment-based or external etcd
instances via `kvfactory.GetEtcdAndPath()` or `ETCD_ENDPOINTS`) and by
removing goroutine-based async startup scaffolding in favor of
synchronous component initialization. Tests remain functionally
equivalent while becoming simpler to run and debug locally.
**What is Removed or Simplified:**
1. **Embedded etcd test infrastructure deleted**: Removes
`EmbedEtcdUtil` type and its public methods (SetupEtcd,
TearDownEmbedEtcd) from `pkg/util/testutils/embed_etcd.go`, removes the
`StartTestEmbedEtcdServer()` helper from `pkg/util/etcd/etcd_util.go`,
and removes etcd embedding from test suites (e.g., `TaskSuite`,
`EtcdSourceSuite`, `mixcoord/client_test.go`). Tests now either skip
etcd-dependent tests (via `MILVUS_UT_WITHOUT_KAFKA=1` environment flag
in `kafka_test.go`) or source etcd from external configuration (via
`kvfactory.GetEtcdAndPath()` in `task_test.go`, or `ETCD_ENDPOINTS`
environment variable in `etcd_source_test.go`). This eliminates the
overhead of spinning up temporary etcd servers for unit tests.
2. **Async startup scaffolding replaced with synchronous
initialization**: In `internal/proxy/proxy_test.go` and
`proxy_rpc_test.go`, the `startGrpc()` method signature removes the
`sync.WaitGroup` parameter; components are now created, prepared, and
run synchronously in-place rather than in goroutines (e.g., `go
testServer.startGrpc(ctx, &p)` becomes `testServer.startGrpc(ctx, &p)`
running synchronously). Readiness checks (e.g., `waitForGrpcReady()`)
remain in place to ensure startup safety without concurrency constructs.
This simplifies control flow and reduces debugging complexity.
3. **Shell script orchestration unified with proper error handling**: In
`scripts/run_go_codecov.sh` and `scripts/run_intergration_test.sh`,
per-package inline test invocations are consolidated into a single
`test_cmd()` function with unified `TEST_CMD_WITH_ARGS` array containing
race, coverage, verbose, and other flags. The problematic `set -ex` is
replaced with `set -e` alone (removing debug output noise while
preserving strict error semantics), ensuring the scripts fail fast on
any command failure.
**Why No Regression:**
- Test assertions and code paths remain unchanged; only deployment
source of etcd (embedded → external) and startup orchestration (async →
sync) change.
- Readiness verification (e.g., `waitForGrpcReady()`) is retained,
ensuring components are initialized before test execution.
- Test flags (race detection, coverage, verbosity) are uniformly applied
across all packages via unified `TEST_CMD_WITH_ARGS`, preserving test
coverage and quality.
- `set -e` alone is sufficient for strict failure detection without the
`-x` flag's verbose output.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: chyezh <chyezh@outlook.com>
related: #45993
This commit extends nullable vector support to the proxy layer,
querynode,
and adds comprehensive validation, search reduce, and field data
handling
for nullable vectors with sparse storage.
Proxy layer changes:
- Update validate_util.go checkAligned() with getExpectedVectorRows()
helper
to validate nullable vector field alignment using valid data count
- Update checkFloatVectorFieldData/checkSparseFloatVectorFieldData for
nullable vector validation with proper row count expectations
- Add FieldDataIdxComputer in typeutil/schema.go for logical-to-physical
index translation during search reduce operations
- Update search_reduce_util.go reduceSearchResultData to use
idxComputers
for correct field data indexing with nullable vectors
- Update task.go, task_query.go, task_upsert.go for nullable vector
handling
- Update msg_pack.go with nullable vector field data processing
QueryNode layer changes:
- Update segments/result.go for nullable vector result handling
- Update segments/search_reduce.go with nullable vector offset
translation
Storage and index changes:
- Update data_codec.go and utils.go for nullable vector serialization
- Update indexcgowrapper/dataset.go and index.go for nullable vector
indexing
Utility changes:
- Add FieldDataIdxComputer struct with Compute() method for efficient
logical-to-physical index mapping across multiple field data
- Update EstimateEntitySize() and AppendFieldData() with fieldIdxs
parameter
- Update funcutil.go with nullable vector support functions
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Full support for nullable vector fields (float, binary, float16,
bfloat16, int8, sparse) across ingest, storage, indexing, search and
retrieval; logical↔physical offset mapping preserves row semantics.
* Client: compaction control and compaction-state APIs.
* **Bug Fixes**
* Improved validation for adding vector fields (nullable + dimension
checks) and corrected search/query behavior for nullable vectors.
* **Chores**
* Persisted validity maps with indexes and on-disk formats.
* **Tests**
* Extensive new and updated end-to-end nullable-vector tests.
<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: marcelo-cjl <marcelo.chen@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>
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>
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>
issue: #29419
added helper functions to parse JSON representation of sparse float
vectors, will be used by both the restful server and the import utils.
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
add sparse float vector support to different milvus components,
including proxy, data node to receive and write sparse float vectors to
binlog, query node to handle search requests, index node to build index
for sparse float column, etc.
https://github.com/milvus-io/milvus/issues/29419
---------
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
See also #30832
This PR removes time tick delay metrics when rootcoord GetMetrics
response does not have previously existed querynode/datanode
Also add unit tests for this case
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
Signed-off-by: Congqi.Xia <congqi.xia@zilliz.com>
