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143 changes: 143 additions & 0 deletions EQUALITY_DELETE_POC_SUMMARY.md
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# Equality Delete Write Path - Proof of Concept

## Summary

This document demonstrates that **PyIceberg already supports the WRITE path for equality delete files**, even though the read path is not yet implemented.

## What Works

✅ Creating `DataFile` objects with `equality_ids` set
✅ Adding equality delete files to tables via transactions
✅ Correctly tracking equality deletes in snapshot metadata
✅ Storing equality delete files in manifests
✅ Multiple equality delete files with different `equality_ids`
✅ Composite equality keys (multiple field IDs)

## What Doesn't Work

❌ Reading tables with equality delete files (raises `ValueError`)
❌ Applying equality deletes during scans

## Key Findings

### 1. Infrastructure Already in Place

The codebase has all the necessary infrastructure for equality deletes:

- **`DataFileContent.EQUALITY_DELETES`** enum defined (`manifest.py:67`)
- **`equality_ids`** field in DataFile schema (`manifest.py:506`)
- **Snapshot tracking** for equality delete counts (`snapshots.py:134-154`)
- **Manifest serialization** works correctly

### 2. No Tests with Actual `equality_ids` Values

My research found:
- **0 tests** that set `equality_ids` to non-empty values like `[1, 2, 3]`
- All existing tests either set it to `[]` or `None`
- Snapshot tests only verify the accounting/metrics, not actual functionality

### 3. The Write API

To add pre-calculated equality delete files:

```python
# Create DataFile with equality_ids
delete_file = DataFile.from_args(
content=DataFileContent.EQUALITY_DELETES, # Key: mark as equality delete
file_path="s3://bucket/delete-file.parquet",
file_format=FileFormat.PARQUET,
partition=Record(),
record_count=num_rows,
file_size_in_bytes=file_size,
equality_ids=[1, 2], # Key: field IDs for equality matching
column_sizes={...},
value_counts={...},
null_value_counts={...},
_table_format_version=2,
)
delete_file.spec_id = table.metadata.default_spec_id

# Add via transaction
with table.transaction() as txn:
update_snapshot = txn.update_snapshot()
with update_snapshot.fast_append() as append_files:
append_files.append_data_file(delete_file) # Works for delete files!
```

### 4. Key Classes and Methods

| Class/Method | Location | Purpose |
|--------------|----------|---------|
| `Transaction.update_snapshot()` | `table/__init__.py:448` | Create UpdateSnapshot |
| `UpdateSnapshot.fast_append()` | `table/update/snapshot.py:697` | Fast append operation |
| `_SnapshotProducer.append_data_file()` | `table/update/snapshot.py:153` | Add file (data or delete) |
| `DataFile.from_args()` | `manifest.py:443` | Create DataFile object |
| `ManifestWriter.add()` | `manifest.py:1088` | Write manifest entry |

## Test Results

Two proof-of-concept tests were created and pass successfully:

### Test 1: Single Equality Delete File
- Creates table with 5 rows
- Writes equality delete file with 2 rows (delete by `id`)
- Adds delete file via transaction with `equality_ids=[1]`
- Verifies metadata tracking
- **Result**: ✅ PASSED

### Test 2: Multiple Equality Delete Files
- Creates 3 different delete files:
- Delete by `id` only (`equality_ids=[1]`)
- Delete by `name` only (`equality_ids=[2]`)
- Delete by `id` AND `name` (`equality_ids=[1, 2]`)
- Adds all in single transaction
- Verifies all tracked correctly
- **Result**: ✅ PASSED

```bash
$ pytest test_add_equality_delete.py -v
test_add_equality_delete.py::test_add_equality_delete_file_via_transaction PASSED
test_add_equality_delete.py::test_add_multiple_equality_delete_files_with_different_equality_ids PASSED
====== 2 passed in 1.06s ======
```

## Understanding `equality_ids`

The `equality_ids` field specifies which columns to use for row matching:

| Example | Meaning |
|---------|---------|
| `equality_ids=[1]` | Match rows where field 1 equals |
| `equality_ids=[2]` | Match rows where field 2 equals |
| `equality_ids=[1, 2]` | Match rows where fields 1 AND 2 both equal (composite key) |

The delete file's Parquet schema must contain the columns corresponding to these field IDs.

## Implications

### For Users Who Want to Write Equality Deletes

**You can start using equality deletes TODAY** if you:
1. Generate equality delete Parquet files externally
2. Use the transaction API shown above to add them
3. Don't need to read the table with PyIceberg (use Spark/etc for reads)

### For Developers

The write path is **complete and working**. The remaining work is the read path:
1. Remove the error at `table/__init__.py:1996-1997`
2. Implement equality delete matching in `plan_files()`
3. Extend `_read_deletes()` to handle equality delete schemas
4. Apply equality deletes in `_task_to_record_batches()`

## Files Created

- **`test_equality_delete_poc.py`** - Detailed standalone test with output
- **`test_add_equality_delete.py`** - Clean pytest test suite (2 tests)
- **`EQUALITY_DELETE_POC_SUMMARY.md`** - This document

## Conclusion

The PyIceberg codebase **already supports writing equality delete files** through the transaction API. The infrastructure is solid and works correctly. This POC demonstrates that users can start adding pre-calculated equality delete files to their tables today, though they'll need external tools (like Spark) to read the tables until the read path is implemented.

The `equality_ids` field, despite never being tested with actual values in the existing test suite, works perfectly for its intended purpose.
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