issue: #45640
- After async logging, the C log and go log has no order promise,
meanwhile the C log format is not consistent with Go Log; so we close
the output of glog, just forward the log result operation into Go side
which will be handled by the async zap logger.
- Use CGO to filter all cgo logging and promise the order between c log
and go log.
- Also fix the metric name, add new metric to count the logging.
- TODO: after woodpecker use the logger of milvus, we can add bigger
buffer for logging.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
- Core invariant: all C (glog) and Go logs must be routed through the
same zap async pipeline so ordering and formatting are preserved; this
PR ensures every glog emission is captured and forwarded to zap before
any async buffering diverges the outputs.
- Logic removed/simplified: direct glog outputs and hard
stdout/stderr/log_dir settings are disabled (configs/glog.conf and flags
in internal/core/src/config/ConfigKnowhere.cpp) because they are
redundant once a single zap sink handles all logs; logging metrics were
simplified from per-length/volatile gauges to totalized counters
(pkg/metrics/logging_metrics.go & pkg/log/*), removing duplicate
length-tracking and making accounting consistent.
- No data loss or behavior regression (concrete code paths): Google
logging now adds a GoZapSink (internal/core/src/common/logging_c.h,
logging_c.cpp) that calls the exported CGO bridge goZapLogExt
(internal/util/cgo/logging/logging.go). Go side uses
C.GoStringN/C.GoString to capture full message and file, maps glog
severities to zapcore levels, preserves caller info, and writes via the
existing zap async core (same write path used by Go logs). The C++
send() trims glog's trailing newline and forwards exact buffers/lengths,
so message content, file, line, and severity are preserved and
serialized through the same async writer—no log entries are dropped or
reordered relative to Go logs.
- Capability added (where it takes effect): a CGO bridge that forwards
glog into zap—new Go-exported function goZapLogExt
(internal/util/cgo/logging/logging.go), a GoZapSink in C++ that forwards
glog sends (internal/core/src/common/logging_c.h/.cpp), and blank
imports of the cgo initializer across multiple packages (various
internal/* files) to ensure the bridge is registered early so all C logs
are captured.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Signed-off-by: chyezh <chyezh@outlook.com>
issue: https://github.com/milvus-io/milvus/issues/42148
For a vector field inside a STRUCT, since a STRUCT can only appear as
the element type of an ARRAY field, the vector field in STRUCT is
effectively an array of vectors, i.e. an embedding list.
Milvus already supports searching embedding lists with metrics whose
names start with the prefix MAX_SIM_.
This PR allows Milvus to search embeddings inside an embedding list
using the same metrics as normal embedding fields. Each embedding in the
list is treated as an independent vector and participates in ANN search.
Further, since STRUCT may contain scalar fields that are highly related
to the embedding field, this PR introduces an element-level filter
expression to refine search results.
The grammar of the element-level filter is:
element_filter(structFieldName, $[subFieldName] == 3)
where $[subFieldName] refers to the value of subFieldName in each
element of the STRUCT array structFieldName.
It can be combined with existing filter expressions, for example:
"varcharField == 'aaa' && element_filter(struct_field, $[struct_int] ==
3)"
A full example:
```
struct_schema = milvus_client.create_struct_field_schema()
struct_schema.add_field("struct_str", DataType.VARCHAR, max_length=65535)
struct_schema.add_field("struct_int", DataType.INT32)
struct_schema.add_field("struct_float_vec", DataType.FLOAT_VECTOR, dim=EMBEDDING_DIM)
schema.add_field(
"struct_field",
datatype=DataType.ARRAY,
element_type=DataType.STRUCT,
struct_schema=struct_schema,
max_capacity=1000,
)
...
filter = "varcharField == 'aaa' && element_filter(struct_field, $[struct_int] == 3 && $[struct_str] == 'abc')"
res = milvus_client.search(
COLLECTION_NAME,
data=query_embeddings,
limit=10,
anns_field="struct_field[struct_float_vec]",
filter=filter,
output_fields=["struct_field[struct_int]", "varcharField"],
)
```
TODO:
1. When an `element_filter` expression is used, a regular filter
expression must also be present. Remove this restriction.
2. Implement `element_filter` expressions in the `query`.
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.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>
issue: #34298
because all vector index config checker has been moved into
vector_index_checker, then the useless checkers can be removed.
Signed-off-by: xianliang.li <xianliang.li@zilliz.com>
