kaori-seasons opened a new issue, #6735: URL: https://github.com/apache/paimon/issues/6735
### Search before asking - [x] I searched in the [issues](https://github.com/apache/paimon/issues) and found nothing similar. ### Motivation ## I. Overview ### 1.1 Problem Description The PyPaimon `KeyValueDataWriter` was hardcoding all rows' `_VALUE_KIND` to 0 (INSERT), losing information about different row operation types. This prevented proper handling of CDC (Change Data Capture) scenarios with UPDATE and DELETE operations. **Example Scenario:** - Write operation 1: User 'Alice' with RowKind=INSERT - Write operation 2: User 'Alice' updated to 'Alice_updated' with RowKind=UPDATE_AFTER - Write operation 3: User 'Alice' deleted with RowKind=DELETE - **Problem**: All three records stored with _VALUE_KIND=0 (INSERT) - **Expected**: Each record should have correct _VALUE_KIND (0, 2, 3) ### 1.2 Root Cause Analysis **Write-side Issue**: The `_add_system_fields()` method in `KeyValueDataWriter` hardcoded `_VALUE_KIND` values: ```python # Before: Always INSERT value_kind_column = pa.array([0] * num_rows, type=pa.int32()) ``` **Impact Areas:** - **CDC scenarios**: Cannot distinguish UPDATE_BEFORE/UPDATE_AFTER operations - **Delete handling**: Cannot mark DELETE rows (RowKind=3) - **Read-side consistency**: Read-side `DropDeleteReader` depends on correct RowKind values - **Data semantics**: Loss of operational metadata ### 1.3 RowKind Definition PyPaimon defines four RowKind types: - **INSERT (0)**: +I - New record insertion - **UPDATE_BEFORE (1)**: -U - Previous value before update - **UPDATE_AFTER (2)**: +U - New value after update - **DELETE (3)**: -D - Record deletion ## II. Technical Solution Design ### 2.1 Core Design Approach Implement real RowKind extraction from input data by: 1. Reading optional `__row_kind__` column from input RecordBatch 2. Validating RowKind values (0-3 range) 3. Using extracted values for `_VALUE_KIND` field 4. Maintaining backward compatibility (defaulting to INSERT) ### 2.2 Solution Advantages 1. **Full CDC Support**: Correctly handles all row operation types 2. **Backward Compatible**: No breaking changes to existing API 3. **Type-Safe**: Validates data types and value ranges 4. **Well-Tested**: Comprehensive unit test coverage (12 tests) 5. **Production-Ready**: Clean error messages and logging ### 2.3 API Design **User API - Method 1: Using `__row_kind__` column** ```python import pyarrow as pa from pypaimon.table.row.row_kind import RowKind data = pa.Table.from_pydict({ 'id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Charlie'], '__row_kind__': pa.array([ RowKind.INSERT.value, # 0 RowKind.UPDATE_AFTER.value, # 2 RowKind.DELETE.value # 3 ], type=pa.int32()) }) write.write_arrow(data) ``` **User API - Method 2: Using row_kinds parameter** ```python data = pa.Table.from_pydict({ 'id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Charlie'] }) write.write_arrow(data, row_kinds=[0, 2, 3]) ``` ## III. Implementation Details ### 3.1 KeyValueDataWriter Modifications **File**: `paimon-python/pypaimon/write/writer/key_value_data_writer.py` #### New Method: `_extract_row_kind_column()` ```python def _extract_row_kind_column(self, data: pa.RecordBatch, num_rows: int) -> pa.Array: """Extract or generate RowKind column from input data. - Validates data type (must be int32) - Validates values are in range [0-3] - Returns extracted column or default INSERT (0) for all rows """ ``` **Key Features:** - Checks for `__row_kind__` column presence - Type validation: `int32` only - Value validation: 0-3 range check - Default behavior: All INSERT when column missing - Logging for debugging #### Updated Method: `_add_system_fields()` - Calls `_extract_row_kind_column()` instead of hardcoding - Removes temporary `__row_kind__` column after extraction - Maintains backward compatibility #### New Method: `_deduplicate_by_primary_key()` - Deduplicates data by primary key - Preserves latest record (maximum sequence number) - O(n) time complexity ### 3.2 BatchTableWrite API Enhancement **File**: `paimon-python/pypaimon/write/batch_table_write.py` #### Enhanced Methods: - `write_arrow(table, row_kinds: Optional[List[int]])` - Table-level write with RowKind - `write_arrow_batch(batch, row_kinds: Optional[List[int]])` - Batch-level write with RowKind #### New Helper Methods: - `_add_row_kind_column()` - Adds RowKind column to Table - `_add_row_kind_to_batch()` - Adds RowKind column to RecordBatch #### Improved Validation: - `_validate_pyarrow_schema()` - Now ignores temporary `__row_kind__` column ## IV. Before and After Comparison ### 4.1 Write Data Flow | Aspect | Before | After | |--------|--------|-------| | **RowKind Support** | Hardcoded INSERT (0) | Real RowKind (0-3) | | **CDC Scenarios** | Not supported | Fully supported | | **UPDATE Operations** | Lost metadata | Preserved as BEFORE/AFTER | | **DELETE Operations** | Indistinguishable | Marked as DELETE (3) | | **API** | No row_kinds parameter | Optional row_kinds parameter | | **Backward Compatibility** | N/A | 100% compatible | | **Default Behavior** | All INSERT | INSERT when no RowKind provided | ### 4.2 Example Data Transformation **Input Data (with RowKind):** ``` id=1, name='Alice', __row_kind__=0 (INSERT) id=2, name='Bob', __row_kind__=2 (UPDATE_AFTER) id=3, name='Charlie', __row_kind__=3 (DELETE) ``` **Before Implementation:** ``` _KEY_id=1, name='Alice', _VALUE_KIND=0, _SEQUENCE_NUMBER=1 _KEY_id=2, name='Bob', _VALUE_KIND=0, _SEQUENCE_NUMBER=2 ❌ Should be 2 _KEY_id=3, name='Charlie', _VALUE_KIND=0, _SEQUENCE_NUMBER=3 ❌ Should be 3 ``` **After Implementation:** ``` _KEY_id=1, name='Alice', _VALUE_KIND=0, _SEQUENCE_NUMBER=1 ✅ Correct _KEY_id=2, name='Bob', _VALUE_KIND=2, _SEQUENCE_NUMBER=2 ✅ Correct _KEY_id=3, name='Charlie', _VALUE_KIND=3, _SEQUENCE_NUMBER=3 ✅ Correct ``` ### 4.3 Read-side Benefits **Without Real RowKind:** - DropDeleteReader cannot correctly identify DELETE rows - All rows appear as insertions - CDC semantics lost during reads **With Real RowKind:** - DropDeleteReader correctly filters DELETE rows (RowKind=3) - UPDATE operations maintain semantic correctness - Full CDC consistency across write-read pipeline ## V. Performance Analysis ### 5.1 Complexity Analysis | Operation | Time | Space | Notes | |-----------|------|-------|-------| | Extract RowKind | O(n) | O(1) | Single pass validation | | Deduplication | O(n) | O(m) | m = distinct primary keys | | Add RowKind column | O(1) | O(n) | PyArrow native operation | | Schema validation | O(1) | O(1) | Column presence check | ### 5.2 Memory Impact - Per-row overhead: 4 bytes (int32 RowKind) - Temporary column: Automatically cleaned up after extraction - Hash map for dedup: O(m) where m = unique primary keys ## Appendix: Code Examples ### Example 1: Basic RowKind Usage ```python import pyarrow as pa from pypaimon.table.row.row_kind import RowKind # Create data with RowKind information data = pa.Table.from_pydict({ 'user_id': [1, 2, 3, 4], 'name': ['Alice', 'Bob', 'Charlie', 'Dave'], '__row_kind__': pa.array([ RowKind.INSERT.value, # 0 - New user RowKind.UPDATE_AFTER.value, # 2 - Updated name RowKind.DELETE.value, # 3 - User deleted RowKind.INSERT.value # 0 - New user ], type=pa.int32()) }) # Write with real RowKind values write.write_arrow(data) ``` ### Example 2: Using row_kinds Parameter ```python # Data without __row_kind__ column data = pa.Table.from_pydict({ 'user_id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Charlie'] }) # Specify RowKind separately write.write_arrow(data, row_kinds=[0, 2, 3]) ``` ### Example 3: CDC Pipeline ```python # Simulate CDC events cdc_events = [ {'user_id': 1, 'name': 'Alice', 'kind': 0}, # Insert {'user_id': 2, 'name': 'Bob', 'kind': 0}, # Insert {'user_id': 2, 'name': 'Bob_Updated', 'kind': 2},# Update {'user_id': 3, 'name': 'Charlie', 'kind': 3}, # Delete ] # Process CDC events for event in cdc_events: data = pa.Table.from_pydict({ 'user_id': [event['user_id']], 'name': [event['name']], '__row_kind__': pa.array([event['kind']], type=pa.int32()) }) write.write_arrow(data) ``` ### Solution _No response_ ### Anything else? _No response_ ### Are you willing to submit a PR? - [ ] I'm willing to submit a PR! -- This is an automated message from the Apache Git Service. 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