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related: #45993 Add nullable vector support in import utility layer Key changes: ImportV2 util: - Add nullable vector types (FloatVector, Float16Vector, BFloat16Vector, BinaryVector, SparseFloatVector, Int8Vector) to AppendNullableDefaultFieldsData() - Add tests for nullable vector field data appending CSV/JSON/Numpy readers: - Add nullPercent parameter to test data generation for better null coverage - Mark vector fields as nullable in test schemas - Add test cases for nullable vector field parsing - Refactor tests to use loop-based approach with 0%, 50%, 100% null percentages Parquet field reader: - Add ReadNullableBinaryData() for nullable BinaryVector/Float16Vector/BFloat16Vector - Add ReadNullableFloatVectorData() for nullable FloatVector - Add ReadNullableSparseFloatVectorData() for nullable SparseFloatVector - Add ReadNullableInt8VectorData() for nullable Int8Vector - Add ReadNullableStructData() for generic nullable struct data - Update Next() to use nullable read methods when field is nullable - Add null data validation for non-nullable fields <!-- This is an auto-generated comment: release notes by coderabbit.ai --> - Core invariant: import must preserve per-row alignment and validity for every field — nullable vector fields are expected to be encoded with per-row validity masks and all readers/writers must emit arrays aligned to original input rows (null entries represented explicitly). - New feature & scope: adds end-to-end nullable-vector support in the import utility layer — AppendNullableDefaultFieldsData in internal/datanode/importv2/util.go now appends nil placeholders for nullable vectors (FloatVector, Float16Vector, BFloat16Vector, BinaryVector, SparseFloatVector, Int8Vector); parquet reader (internal/util/importutilv2/parquet/field_reader.go) adds ReadNullableBinaryData, ReadNullableFloatVectorData, ReadNullableSparseFloatVectorData, ReadNullableInt8VectorData, ReadNullableStructData and routes nullable branches to these helpers; CSV/JSON/Numpy readers and test utilities updated to generate and validate 0/50/100% null scenarios and mark vector fields as nullable in test schemas. - Logic removed / simplified: eliminates ad-hoc "parameter-invalid" rejections for nullable vectors inside FieldReader.Next by centralizing nullable handling into ReadNullable* helpers and shared validators (getArrayDataNullable, checkNullableVectorAlignWithDim/checkNullableVectorAligned), simplifying control flow and removing scattered special-case checks. - No data loss / no regression (concrete code paths): nulls are preserved end-to-end — AppendNullableDefaultFieldsData explicitly inserts nil entries per null row (datanode import append path); ReadNullable*Data helpers return both data and []bool validity masks so callers in field_reader.go and downstream readers receive exact per-row validity; testutil.BuildSparseVectorData was extended to accept validData so sparse vectors are materialized only for valid rows while null rows are represented as missing. These concrete paths ensure null rows are represented rather than dropped, preventing data loss or behavioral regression. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: marcelo-cjl <marcelo.chen@zilliz.com>
Data Node
DataNode is the component to write insert and delete messages into persistent blob storage, for example MinIO or S3.
Dependency
- KV store: a kv store that persists messages into blob storage.
- Message stream: receive messages and publish information
- Root Coordinator: get the latest unique IDs.
- Data Coordinator: get the flush information and which message stream to subscribe.