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‎BULK_PROGRESS_SUMMARY.md‎

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# async-cassandra-bulk Progress Summary
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## Current Status
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- **Date**: 2025-07-11
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- **Branch**: bulk
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- **State**: Production-ready, awaiting release decision
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## What We've Built
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A production-ready bulk operations library for Apache Cassandra with comprehensive writetime/TTL filtering and export capabilities.
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## Key Features Implemented
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### 1. Writetime/TTL Filtering
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- Filter data by writetime (before/after specific timestamps)
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- Filter by TTL values
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- Support for multiple columns with "any" or "all" matching
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- Automatic column detection from table metadata
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- Precision preservation (microseconds)
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### 2. Export Formats
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- **JSON**: With precise timestamp serialization
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- **CSV**: With proper escaping and writetime columns
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- **Parquet**: With PyArrow integration
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### 3. Advanced Capabilities
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- Token-based parallel export for distributed reads
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- Checkpoint/resume for fault tolerance
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- Progress tracking with callbacks
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- Memory-efficient streaming
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- Configurable batch sizes and concurrency
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## Testing Coverage
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### 1. Integration Tests (100% passing - 106 tests)
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- All Cassandra data types with writetime
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- NULL handling (explicit NULL vs missing columns)
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- Empty collections behavior (stored as NULL in Cassandra)
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- UDTs, tuples, nested collections
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- Static columns
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- Clustering columns
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### 2. Error Scenarios (comprehensive)
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- Network failures (intermittent and total)
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- Disk space exhaustion
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- Corrupted checkpoints
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- Concurrent exports
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- Thread pool exhaustion
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- Schema changes during export
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- Memory pressure with large rows
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### 3. Critical Fixes Made
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- **Timestamp parsing**: Fixed microsecond precision handling
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- **NULL writetime**: Corrected filter logic for NULL values
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- **Precision preservation**: ISO format for CSV/JSON serialization
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- **Error handling**: Capture in stats rather than raising exceptions
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## Code Quality
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- ✅ All linting passed (ruff, black, isort, mypy)
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- ✅ Comprehensive docstrings with production context
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- ✅ No mocking in integration tests
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- ✅ Thread-safe implementation
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- ✅ Proper resource cleanup
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## Architecture Decisions
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1. **Thin wrapper** around cassandra-driver
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2. **Reuses async-cassandra** for all DB operations
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3. **Stateless operation** with checkpoint support
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4. **Producer-consumer pattern** for parallel export
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5. **Pluggable exporter interface** for format extensibility
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## Files Changed/Created
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### New Library Structure
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```
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libs/async-cassandra-bulk/
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├── src/async_cassandra_bulk/
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│ ├── __init__.py
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│ ├── operators/
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│ │ ├── __init__.py
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│ │ └── bulk_operator.py
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│ ├── exporters/
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│ │ ├── __init__.py
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│ │ ├── base.py
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│ │ ├── csv.py
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│ │ ├── json.py
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│ │ └── parquet.py
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│ ├── serializers/
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│ │ ├── __init__.py
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│ │ ├── base.py
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│ │ ├── ttl.py
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│ │ └── writetime.py
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│ ├── models.py
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│ ├── parallel_export.py
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│ └── exceptions.py
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├── tests/
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│ ├── integration/
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│ │ ├── test_bulk_export_basic.py
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│ │ ├── test_checkpoint_resume.py
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│ │ ├── test_error_scenarios_comprehensive.py
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│ │ ├── test_null_handling_comprehensive.py
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│ │ ├── test_parallel_export.py
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│ │ ├── test_serializers.py
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│ │ ├── test_ttl_export.py
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│ │ ├── test_writetime_all_types_comprehensive.py
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│ │ ├── test_writetime_export.py
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│ │ └── test_writetime_filtering.py
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│ └── unit/
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│ ├── test_exporters.py
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│ └── test_models.py
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├── pyproject.toml
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├── README.md
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└── examples/
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└── bulk_export_example.py
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```
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### Removed from async-cassandra
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- `examples/bulk_operations/` directory
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- `examples/export_large_table.py`
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- `examples/export_to_parquet.py`
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- `examples/exampleoutput/` directory
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- Updated `Makefile` to remove bulk-related targets
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- Updated `examples/README.md`
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- Updated `examples/requirements.txt`
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- Updated `tests/integration/test_example_scripts.py`
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## Open Questions for Research
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### Current Implementation
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- Uses token ranges for distribution
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- Leverages prepared statements
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- Implements streaming to avoid memory issues
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- Supports writetime/TTL filtering at query level
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### Potential Research Areas
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1. **Different partitioning strategies?**
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- Current: Token-based ranges
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- Alternative: Partition key based?
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2. **Alternative export mechanisms?**
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- Current: Producer-consumer with queues
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- Alternative: Direct streaming?
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3. **Integration with other bulk tools?**
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- Spark Cassandra Connector patterns?
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- DataStax Bulk Loader compatibility?
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4. **Performance optimizations?**
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- Larger page sizes?
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- Different threading models?
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- Connection pooling strategies?
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## Next Steps
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1. Decide on research direction for bulk operations
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2. Tag and release if current approach is acceptable
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3. Or refactor based on research findings
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## Key Takeaways
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- The library is **production-ready** as implemented
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- Comprehensive test coverage ensures reliability
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- Architecture allows for future enhancements
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- Clean separation from main async-cassandra library

‎libs/async-cassandra-bulk/tests/integration/test_writetime_all_types_comprehensive.py‎

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@@ -168,8 +168,9 @@ async def test_writetime_basic_types(self, session):
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SET text_col = 'updated text',
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int_col = 999,
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boolean_col = false
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WHERE id = {test_id}
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"""
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WHERE id = %s
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""",
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(test_id,),
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)
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# Export with writetime for all columns
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f"""
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INSERT INTO {keyspace}.{table_name}
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(tenant_id, user_id, tenant_name, tenant_active)
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VALUES ({tenant1}, {user1}, 'Test Tenant', true)
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VALUES (%s, %s, 'Test Tenant', true)
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USING TIMESTAMP {base_writetime}
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"""
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""",
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(tenant1, user1),
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)
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# Insert regular rows
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INSERT INTO {keyspace}.{table_name}
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(tenant_id, user_id, timestamp, event_type, event_data, ip_address)
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VALUES (
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{tenant1},
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{user1},
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%s,
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%s,
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'{datetime.now(timezone.utc) + timedelta(hours=i)}',
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'login',
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'data_{i}',
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'192.168.1.{i}'
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)
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USING TIMESTAMP {base_writetime + i * 1000000}
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"""
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""",
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(tenant1, user1),
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)
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# Update static column with different writetime
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UPDATE {keyspace}.{table_name}
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USING TIMESTAMP {base_writetime + 5000000}
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SET tenant_active = false
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WHERE tenant_id = {tenant1} AND user_id = {user1}
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"""
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WHERE tenant_id = %s AND user_id = %s
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""",
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(tenant1, user1),
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)
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# Export with writetime
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INSERT INTO {keyspace}.{table_name}
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(id, username, profile, profiles_history)
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VALUES (
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{test_id},
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%s,
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'testuser',
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{{
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first_name: 'John',
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]
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)
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USING TIMESTAMP {base_writetime}
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"""
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""",
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(test_id,),
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)
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# Update UDT (replaces entire UDT)
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email: 'newemail@example.com',
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age: 31
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}}
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WHERE id = {test_id}
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"""
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WHERE id = %s
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""",
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(test_id,),
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)
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# Export with writetime
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INSERT INTO {keyspace}.{table_name}
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(id, data, updated_at, version)
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VALUES (
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{test_id},
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%s,
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'test_data_{i}',
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'{datetime.now(timezone.utc)}',
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{i}
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)
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USING TIMESTAMP {wt}
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"""
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""",
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(test_id,),
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)
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# Export to both CSV and JSON

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