## Summary
Fixes#47540
- **Bug 1 (MixCoord panic):** `compactionInspector.analyzeScheduler` was
never initialized in production code. When FloatVector is used as
ClusteringKey, the `doAnalyze()` path calls `analyzeScheduler.Enqueue()`
on a nil pointer → panic. Fixed by passing `analyzeScheduler` to
`newCompactionInspector`.
- **Bug 2 (DataNode round-robin panic):** `validateClusteringKey()` had
no field type whitelist, so JSON/Bool/Array passed schema validation.
During clustering compaction, `NewScalarFieldValue()` panics on
unsupported types. Fixed by adding `IsClusteringKeyType()` check to
reject unsupported types at collection creation time.
## Test plan
- [x] `TestIsClusteringKeyType` — verifies supported/unsupported type
classification
- [x] `TestClusteringKey` — new sub-tests for JSON, Bool, Array as
ClusteringKey (all rejected)
- [ ] Existing `TestClusteringKey` sub-tests (normal, multiple keys,
vector key) still pass
- [ ] `TestCompactionPlanHandler*` tests pass with updated
`newCompactionInspector` signature
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Signed-off-by: Cai Zhang <cai.zhang@zilliz.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
issue: #46953
- GenNullableFieldData only supported scalar types, causing error when
partial upsert after adding nullable vector fields
issue: #47160
- pickFieldData used row index directly to access Contents array without
converting to data index, causing index out of range panic
relate: #45993
Row-based processing had two problems:
- Nullable vector handling was tightly coupled with other field types,
requiring complex special-case logic
- Update path was inefficient for nullable vectors: data had to be read
first then updated in-place. For example, changing a valid vector to
null requires shifting subsequent data since null values are not stored,
which is very inefficient.
Column-based processing solves both:
- Each field type is processed independently, nullable vector logic is
cleanly separated from other types
- Nullable vectors are appended directly without read-then-update,
avoiding expensive data shifting operations - Iterate over fields
instead of rows when merging upsert and existing data
key changes:
- Remove nullable vector special handling: nullableVectorMergeContext,
buildNullableVectorIdxMap, rebuildNullableVectorFieldData, etc.
- Use generic column utilities: AppendFieldDataByColumn,
UpdateFieldDataByColumn
- Add vector type support to GenNullableFieldData
- Add unit test for nullable vector upsert scenarios
---------
Signed-off-by: marcelo-cjl <marcelo.chen@zilliz.com>
Add automatic conversion of fp32 search vectors to fp16/bf16 when the
collection field uses lower precision types. This allows users to search
fp16/bf16 collections with fp32 query vectors (which is what most
embedding models output).
Changes:
- Add vector type matching utility (isVectorTypeMatch)
- Add float16/bfloat16 range validation before conversion
- Add ConvertPlaceholderGroup function for type conversion
- Integrate conversion into normal search and hybrid search
- Add GetFieldByID helper function in typeutil
The conversion happens in the Proxy layer before requests are sent to
QueryNode. Out-of-range values are rejected with clear error messages.
issue: #47207
Signed-off-by: Li Liu <li.liu@zilliz.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
issue : https://github.com/milvus-io/milvus/issues/41746
This PR adds MinHash "DIDO" (Data In, Data Out) support to Milvus, which
allows computing MinHash signatures on-the-fly during search operations
instead of requiring pre-stored vectors.
Key changes:
- Implemented SIMD-optimized C++ MinHash computation (AVX2/AVX512 for
x86, NEON/SVE for ARM)
- Added runtime CPU detection and function hooks to automatically select
the best SIMD implementation
- Integrated MinHash computation into search pipeline (brute force
search, growing segment search)
- Added support for LSH-based MinHash search with configurable band
width and bit width parameters
- Enabled direct text-to-signature conversion during query execution,
reducing storage overhead
This enables efficient text deduplication and similarity search without
storing pre-computed MinHash vectors.
Signed-off-by: cqy123456 <qianya.cheng@zilliz.com>
issue: #45881
- Add ExternalSource and ExternalSpec fields to collection schema
- Add ExternalField mapping for field schema to map external columns
- Implement ValidateExternalCollectionSchema() to enforce restrictions:
- No primary key (virtual PK generated automatically)
- No dynamic fields, partition keys, clustering keys, or auto ID
- No text match or function features
- All user fields must have external_field mapping
- Return virtual PK schema for external collections in
GetPrimaryFieldSchema()
- Skip primary key validation for external collections during creation
- Add comprehensive unit tests and integration tests
- Add design document and user guide
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
Co-authored-by: sunby <sunbingyi1992@gmail.com>
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>
relate: https://github.com/milvus-io/milvus/issues/46498
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
- Core invariant: text fields configured with multi_analyzer_params must
include a "by_field" string that names another field containing per-row
analyzer choices; schemaInfo.GetMultiAnalyzerNameFieldID caches and
returns the dependent field ID (or 0 if none) and relies on that mapping
to make per-row analyzer names available to the highlighter.
- What changed / simplified: the highlighter is now schema-aware —
addTaskWithSearchText accepts *schemaInfo and uses
GetMultiAnalyzerNameFieldID to resolve the analyzer-name field;
resolution and caching moved into schemaInfo.multiAnalyzerFieldMap
(meta_cache.go), eliminating ad-hoc/typeutil-only lookups and duplicated
logic; GetMultiAnalyzerParams now gates on EnableAnalyzer(),
centralizing analyzer enablement checks.
- Why this fixes the bug (root cause): fixes#46498 — previously the
highlighter failed when the analyzer-by-field was not in output_fields.
The change (1) populates task.AnalyzerNames (defaulting missing names to
"default") when multi-analyzer is configured and (2) appends the
analyzer-name field ID to LexicalHighlighter.extraFields so FieldIDs
includes it; the operator then requests the analyzer-name column at
search time, ensuring per-row analyzer selection is available for
highlighting.
- No data-loss or regression: when no multi-analyzer is configured
GetMultiAnalyzerNameFieldID returns 0 and behavior is unchanged; the
patch only adds the analyzer-name field to requested output IDs (no
mutation of stored data). Error handling on malformed params is
preserved (errors are returned instead of silently changing data), and
single-analyzer behavior remains untouched.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Signed-off-by: aoiasd <zhicheng.yue@zilliz.com>
issue: #44452
## Summary
Reduce test combinations in `TestSparsePlaceholderGroupSize` to decrease
test execution time:
- `nqs`: from `[1, 10, 100, 1000, 10000]` to `[1, 100, 10000]`
- `averageNNZs`: from `[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024,
2048]` to `[1, 4, 16, 64, 256, 1024]`
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## TestSparsePlaceholderGroupSize Test Reduction
**Core Invariant:** The sparse vector NNZ estimation algorithm
(`EstimateSparseVectorNNZFromPlaceholderGroup`) must maintain accuracy
within bounded error thresholds—individual cases < 10% error and no more
than 2% of cases exceeding 5% error—across representative parameter
ranges.
**Test Coverage Optimized, Not Removed:** Test combinations reduced from
60 to 18 by pruning redundant parameter points while retaining critical
coverage: nqs now tests [1, 100, 10000] (min, mid, max) and averageNNZs
tests [1, 4, 16, 64, 256, 1024] (exponential spacing). Variant
generation logic (powers of 2 scaling) remains unchanged, ensuring error
scenarios are still exercised.
**No Behavioral Regression:** The algorithm under test is untouched;
only test case frequency decreases. The same assertions validate error
bounds are satisfied—individual assertions (`assert.Less(errorRatio,
10.0)`) and statistical assertions (`assert.Less(largeErrorRatio, 2.0)`)
remain identical, confirming that estimation quality is still verified.
**Why Safe:** Exponential spacing of removed parameters (e.g., nqs: 10,
1000 removed; averageNNZs: 2, 8, 32, 128, 512, 2048 removed) addresses
diminishing returns—intermediate values provide no new error scenarios
beyond what surrounding powers-of-2 values expose, while keeping test
execution time proportional to coverage value gained.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.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: #45640
- log may be dropped if the underlying file system is busy.
- use async write syncer to avoid the log operation block the milvus
major system.
- remove some log dependency from the until function to avoid
dependency-loop.
---------
Signed-off-by: chyezh <chyezh@outlook.com>
issue: https://github.com/milvus-io/milvus/issues/45006
ref: https://github.com/milvus-io/milvus/issues/42148
Previsouly, the parquet import is implemented based on that the STRUCT
in the parquet files is hanlded in the way that each field in struct is
stored in a single column.
However, in the user's perspective, the array of STRUCT contains data is
something like STRUCT_A:
for one row, [struct{field1_1, field2_1, field3_1}, struct{field1_2,
field2_2, field3_2}, ...], rather than {[field1_1, field1_2, ...],
[field2_1, field2_2, ...], [field3_1, field3_2, field3_3, ...]}.
This PR fixes this.
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
relate: https://github.com/milvus-io/milvus/issues/43687
We used to run the temporary analyzer and validate analyzer on the
proxy, but the proxy should not be a computation-heavy node. This PR
move all analyzer calculations to the streaming node.
---------
Signed-off-by: aoiasd <zhicheng.yue@zilliz.com>
issue: #44800
This commit enhances the upsert and validation logic to properly handle
nullable Geometry (WKT/WKB) and Timestamptz data types:
- Add ToCompressedFormatNullable support for TimestamptzData,
GeometryWktData, and GeometryData to filter out null values during data
compression
- Implement GenNullableFieldData for Timestamptz and Geometry types to
generate nullable field data structures
- Update FillWithNullValue to handle both GeometryData and
GeometryWktData with null value filling logic
- Add UpdateFieldData support for Timestamptz, GeometryData, and
GeometryWktData field updates
- Comprehensive unit tests covering all new data type handling scenarios
Signed-off-by: Wei Liu <wei.liu@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/27467
>My plan is as follows.
>- [x] M1 Create collection with timestamptz field
>- [x] M2 Insert timestamptz field data
>- [x] M3 Retrieve timestamptz field data
>- [x] M4 Implement handoff
>- [x] M5 Implement compare operator
>- [x] M6 Implement extract operator
>- [x] M8 Support database/collection level default timezone
>- [x] M7 Support STL-SORT index for datatype timestamptz
---
The third PR of issue: https://github.com/milvus-io/milvus/issues/27467,
which completes M5, M6, M7, M8 described above.
## M8 Default Timezone
We will be able to use alter_collection() and alter_database() in a
future Python SDK release to modify the default timezone at the
collection or database level.
For insert requests, the timezone will be resolved using the following
order of precedence: String Literal-> Collection Default -> Database
Default.
For retrieval requests, the timezone will be resolved in this order:
Query Parameters -> Collection Default -> Database Default.
In both cases, the final fallback timezone is UTC.
## M5: Comparison Operators
We can now use the following expression format to filter on the
timestamptz field:
- `timestamptz_field [+/- INTERVAL 'interval_string'] {comparison_op}
ISO 'iso_string' `
- The interval_string follows the ISO 8601 duration format, for example:
P1Y2M3DT1H2M3S.
- The iso_string follows the ISO 8601 timestamp format, for example:
2025-01-03T00:00:00+08:00.
- Example expressions: "tsz + INTERVAL 'P0D' != ISO
'2025-01-03T00:00:00+08:00'" or "tsz != ISO
'2025-01-03T00:00:00+08:00'".
## M6: Extract
We will be able to extract sepecific time filed by kwargs in a future
Python SDK release.
The key is `time_fields`, and value should be one or more of "year,
month, day, hour, minute, second, microsecond", seperated by comma or
space. Then the result of each record would be an array of int64.
## M7: Indexing Support
Expressions without interval arithmetic can be accelerated using an
STL-SORT index. However, expressions that include interval arithmetic
cannot be indexed. This is because the result of an interval calculation
depends on the specific timestamp value. For example, adding one month
to a date in February results in a different number of added days than
adding one month to a date in March.
---
After this PR, the input / output type of timestamptz would be iso
string. Timestampz would be stored as timestamptz data, which is int64_t
finally.
> for more information, see https://en.wikipedia.org/wiki/ISO_8601
---------
Signed-off-by: xtx <xtianx@smail.nju.edu.cn>
issue: #43980
This commit optimizes the partial update merge logic by standardizing
nullable field representation before merge operations to avoid corner
cases during the merge process.
Key changes:
- Unify nullable field data format to FULL FORMAT before merge execution
- Add extensive unit tests for bounds checking and edge cases
The optimization ensures:
- Consistent nullable field representation across SDK and internal
- Proper handling of null values during merge operations
- Prevention of index out-of-bounds errors in vector field updates
- Better error handling and validation for partial update scenarios
This resolves issues where different nullable field formats could cause
merge failures or data corruption during partial update operations.
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
issue: #43980
Fix panic issue caused by incorrect nullable field merging logic when
upsert converts to insert operation on empty tables.
- Add AppendFieldDataWithNullData to handle nullable field merging
- Fix existing data merge with skipAppendNullData=false
- Fix insert data merge with skipAppendNullData=true
- Add unit tests for nullable field data appending scenarios
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
issue: #43980
Fixes a panic that occurred when a partial update was converted to an
insert due to a non-existent primary key. The panic was caused by
missing nullable fields that were not provided in the original partial
update request.
The upsert pre-execution logic is refactored to handle this correctly:
- Explicitly splits upsert data into 'insert' and 'update' batches.
- Automatically generates data for missing nullable or default-value
fields during inserts, preventing the panic.
- Enhances `typeutil.UpdateFieldData` to support different source and
destination indexes for flexible data merging.
- Adds comprehensive unit tests for mixed upsert, pure insert, and pure
update scenarios.
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.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: #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>
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>
Previous code uses diskSegmentMaxSize if and only if all of the
collection's vector fields are indexed with DiskANN index.
When introducing sparse vectors, since sparse vector cannot be indexed
with DiskANN index, collections with both dense and sparse vectors will
use maxSize instead.
This PR changes the requirments of using diskSegmentMaxSize to all dense
vectors are indexed with DiskANN indexs, ignoring sparse vector fields.
See also: #43193
Signed-off-by: yangxuan <xuan.yang@zilliz.com>
Related to #42489
Since load list works as hint after cachelayer implemented, the related
check logic could be removed to keep code logic clean.
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
See #36264
In this PR:
- Enhanced error handling in parse of grouping field.
- Fixed null handling in reduce tasks in proxy nodes.
- Updated tests to reflect changes in error handling and data processing
logic.
---------
Signed-off-by: Ted Xu <ted.xu@zilliz.com>
Merge RootCoord, DataCoord And QueryCoord into MixCoord
Make Session into one
issue : https://github.com/milvus-io/milvus/issues/37764
---------
Signed-off-by: Xianhui.Lin <xianhui.lin@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>
cai.zhang
marcelo-cjl
Spade A
Li Liu
cqy123456
wei liu
Chun Han
aoiasd
Buqian Zheng
Xiaofan
Zhen Ye
zhenshan.cao
junjiejiangjjj
Tianx
yihao.dai
XuanYang-cn
congqixia
Ted Xu
SimFG
Xianhui Lin
Patrick Weizhi Xu