# Master List of All Database Synchronization Scenarios

This document contains all scenario cases from all plan documents as a comprehensive reference for creating a new unified plan.

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## Scenarios from plan.md

### Scenario 1.1: Simple Size Difference (2 vs 4)

- Source: record_id 2025-01-30.08:00:00.ABC, 2025-01-30.08:30:00.DEF (2 records)
- Target: record_id 2025-01-30.08:00:00.ABC, 2025-01-30.08:15:00.XYZ, 2025-01-30.08:30:00.DEF, 2025-01-30.09:00:00.GHI (4 records)
- Boundary cleanup deletes record 2025-01-30.09:00:00.GHI (outside source range)
- Binary search finds record 2025-01-30.08:15:00.XYZ (target-only) → DELETE operation
- Records 2025-01-30.08:00:00.ABC, 2025-01-30.08:30:00.DEF exist in both → check for modifications

### Scenario 1.2: Large Size Difference (10 vs 1000)

- Source: 10 records
- Target: 1000 records
- Large size difference with efficient boundary cleanup
- System handles extreme ratios (1:100) effectively

### Scenario 1.3: Source Much Smaller with Gaps (1 vs 100)

- Source: 1 record
- Target: 100 records with gaps
- Sparse distribution with size difference
- Boundary cleanup removes 99 target records

### Scenario 2.1: Simple Size Difference (4 vs 2)

- Source: 4 records
- Target: 2 records
- Source larger than target scenario
- Missing records detected as INSERT operations

### Scenario 2.2: Large Size Difference (1000 vs 10)

- Source: 1000 records
- Target: 10 records
- Extreme ratio (100:1) handling
- Bulk additions processed efficiently

### Scenario 2.3: Source Dominated with Sparse Target (1000 vs 1)

- Source: 1000 records
- Target: 1 record
- Single target record creates minimal binary search overhead
- New record detection handles bulk additions deterministically

### Scenario 3.1: Same Count, Different Records (4 vs 4)

- Source: 4 records
- Target: 4 records (different middle records)
- Same counts don't hide differences
- Each record processed individually

### Scenario 3.2: Complex Record Swaps (5 vs 5)

- Source: 5 records
- Target: 5 records (complex swapping pattern)
- Multiple changes within single range
- Individual record comparison catches all differences

### Scenario 3.3: Gaps and Sparse Distribution (6 vs 6)

- Source: 6 records with gaps
- Target: 6 records with different distribution
- Sparse distribution handling
- Large gaps don't cause problems

### Scenario 4.1: Target Empty (5 vs 0)

- Source: 5 records
- Target: 0 records (empty)
- All source records → INSERT operations
- Direct bulk addition

### Scenario 4.2: Source Empty (0 vs 5)

- Source: 0 records (empty)
- Target: 5 records
- All target records → DELETE operations
- Complete target cleanup

### Scenario 4.3: Both Empty (0 vs 0)

- Source: 0 records (empty)
- Target: 0 records (empty)
- No operations needed
- Trivial case handling

### Scenario 5.1: Complete Range Separation

- Source and target have completely separate ranges
- No shared boundaries
- Boundary cleanup eliminates all target records
- Complete database replacement scenario

### Scenario 5.2: Partial Range Overlap

- Source and target have partially overlapping ranges
- Some shared boundary records
- Mixed cleanup and sync operations

### Scenario 6.1: Large Gaps in Source

- Source has large gaps between records
- Target may have different distribution
- Sparse source data handling

### Scenario 6.2: Dense Target, Sparse Source

- Source: sparse distribution
- Target: dense distribution
- Inverse density scenario
- Range-bounded queries handle differences

### Scenario 7.1: lastId at Source Min

- Source: record_id 2025-01-30.08:00:00.ABC, 2025-01-30.08:30:00.DEF, 2025-01-30.09:00:00.GHI
- Target: record_id 2025-01-30.08:00:00.ABC (only shares the minimum record)
- Edge boundary case with minimal binary search range
- Majority of records handled as new additions

### Scenario 7.2: lastId at Source Max

- Source: record_id 2025-01-30.08:00:00.ABC, 2025-01-30.08:30:00.DEF, 2025-01-30.09:00:00.GHI
- Target: record_id 2025-01-30.09:00:00.GHI (only shares the maximum record)
- Maximum boundary creates full-range binary search
- All records processed within binary search

### Scenario 7.3: No Shared Records (lastId = 0)

- Source: record_id 2025-01-30.08:00:00.ABC, 2025-01-30.08:30:00.DEF, 2025-01-30.09:00:00.GHI
- Target: record_id 2025-01-30.10:00:00.PQR, 2025-01-30.10:30:00.STU (no overlap)
- No overlap creates all-new scenario after boundary cleanup
- Binary search eliminated entirely - optimal performance

### Scenario 8.1: Multiple Changes in Single Range

- Multiple modifications within single binary search range
- Complex change patterns
- Individual record comparison handles all differences

### Scenario 8.2: Primary Key Changes Within Range

- Primary key modifications within binary search range
- Constraint handling
- Temporary key mechanisms for constraint violations

### Scenario 9.1: Extreme Size Difference (1 vs 1 Billion)

- Source: 1 record
- Target: 1 billion records
- Extreme ratio scenario
- Resource management considerations

### Scenario 9.2: Memory Constraint Scenario

- Large dataset with memory limitations
- Batch processing requirements
- Resource exhaustion handling

### Scenario 10.1: Duplicate Record IDs in Source

- Source: record_id 2025-01-30.08:00:00.ABC, 2025-01-30.08:00:00.ABC, 2025-01-30.08:30:00.DEF (duplicate record_id 2025-01-30.08:00:00.ABC)
- Target: record_id 2025-01-30.08:00:00.ABC, 2025-01-30.08:30:00.DEF
- Data integrity violation
- System fails safely with clear error reporting

### Scenario 10.2: Constraint Violations During Sync

- Source: record_id 2025-01-30.08:00:00.ABC (PK:A), record_id 2025-01-30.08:30:00.DEF (PK:B)
- Target: record_id 2025-01-30.08:00:00.ABC (PK:B), record_id 2025-01-30.09:00:00.GHI (PK:A) (swapped primary keys)
- Constraint violation scenarios
- Order-dependent failures handled by temporary PKs

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## Scenarios from plan2.md

### Constraint Violation Scenarios

#### Scenario: Primary Key Swap Cycles

- Record A changes PK from X→Y, Record B changes PK from Y→X
- Circular dependencies
- Tarjan's algorithm for cycle detection

#### Scenario: Referential Integrity Violations

- Changes that violate referential integrity if applied individually
- Order-dependent constraint violations
- Temporary key mechanisms

#### Scenario: Duplicate Record IDs

- Source contains duplicate `record_id` values
- Ordering assumption violation
- Error detection and handling

#### Scenario: Index Corruption

- Database indexes are inconsistent with actual data
- Range query inaccuracies
- Validation mechanisms

#### Scenario: Concurrent Modifications

- Source database changes during sync
- Axiom violation scenarios
- Database locking requirements

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## Scenarios from plan3.md

### Mathematical Analysis Scenarios

#### Concrete Database Scenario (Binary Search Failure)

- Source: record_id 2025-01-30.08:00:00.ABC, 2025-01-30.08:15:00.DEF, 2025-01-30.08:30:00.GHI, 2025-01-30.08:45:00.JKL, 2025-01-30.09:00:00.MNO with specific data
- Target: Same IDs with completely different data
- Binary search using only count comparisons fails
- Individual record comparison needed

#### Concurrent Source Modification Scenario

- Source database changes during synchronization
- Record deletion and addition during sync
- Inconsistent sync results

#### Failure Mode Scenarios

##### 1. Record ID Uniqueness Violations

- Multiple records share same record_id
- Hash table corruption
- Mathematical ordering breaks

##### 2. Binary Search Logical Errors

- Equal counts but different records
- Mathematical impossibility detection
- Silent data corruption risk

##### 3. Delete First Constraint Violations

- Foreign key constraints prevent deletion
- Boundary cleanup incomplete
- Impossible matches in later phases

##### 4. Concurrent Source Modifications

- Source changes between count and data queries
- Inconsistent sync results
- Data corruption risk

##### 5. Empty Database Validation Gaps

- Empty target skips validation
- Missing consistency checks
- Data integrity gaps

##### 6. Memory Exhaustion Issues

- Large datasets exceed memory limits
- System crashes, partial sync
- No graceful handling

##### 7. Concurrent Operation Conflicts

- Multiple sync processes simultaneously
- Race conditions, data corruption
- No global locking

##### 8. Transaction Boundary Issues

- Large operations exceed transaction limits
- Partial commits, inconsistent state
- No rollback mechanism

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## Scenarios from plan4.md

### Architecture Scenarios

#### Scenario: Simple Size Difference (2 vs 4)

- Source: 2 records
- Target: 4 records
- Boundary cleanup removes excess
- Binary search finds differences

#### Scenario: Source Larger Than Target (5 vs 3)

- Source: 5 records
- Target: 3 records
- Missing records → INSERT operations

#### Scenario: Complete Database Replacement (5 vs 5)

- Source: 5 records
- Target: 5 completely different records
- Boundary cleanup deletes all target records
- Complete replacement

#### Scenario: Symmetric Differences (3 vs 3)

- Source: 3 records
- Target: 3 records (one deleted, one added)
- Balanced changes handling

#### Scenario: Empty Target Database

- Source: 3 records
- Target: 0 records
- All records → INSERT operations

#### Scenario: All Records Different (3 vs 3)

- Source: 3 records
- Target: 3 completely different records
- Complete database replacement

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## Currently Implemented Scenarios (scenario.md)

1. **Scenario 1**: Simple Size Difference (2 vs 4)
2. **Scenario 2**: Source Larger Than Target (5 vs 3)
3. **Scenario 3**: Complete Database Replacement (5 vs 5)
4. **Scenario 4**: Symmetric Differences (3 vs 3)
5. **Scenario 5**: Empty Target Database
6. **Scenario 6**: Extreme Size Difference (1 vs 100)
7. **Scenario 7**: Same Count, Different Records (4 vs 4)
8. **Scenario 8**: Large Source, Tiny Target (1000 vs 1)
9. **Scenario 9**: Sparse Distribution with Gaps
10. **Scenario 10**: All Records Different (3 vs 3)
11. **Scenario 11**: Edge Boundary - Shared Record at Source Min
12. **Scenario 12**: Edge Boundary - Shared Record at Source Max
13. **Scenario 13**: No Shared Records (Complete Non-Overlap)
14. **Scenario 14**: Data Integrity - Duplicate Record IDs

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## Missing Scenarios to Implement

### From plan.md (Not Yet Implemented)

- 1.2: Large Size Difference (10 vs 1000)
- 1.3: Source Much Smaller with Gaps (1 vs 100)
- 2.1: Simple Size Difference (4 vs 2) - *Similar to existing*
- 2.2: Large Size Difference (1000 vs 10)
- 3.2: Complex Record Swaps (5 vs 5)
- 3.3: Gaps and Sparse Distribution (6 vs 6)
- 4.2: Source Empty (0 vs 5)
- 4.3: Both Empty (0 vs 0)
- 5.1: Complete Range Separation
- 5.2: Partial Range Overlap
- 6.1: Large Gaps in Source
- 6.2: Dense Target, Sparse Source
- 8.1: Multiple Changes in Single Range
- 8.2: Primary Key Changes Within Range
- 9.1: Extreme Size Difference (1 vs 1 Billion)
- 9.2: Memory Constraint Scenario
- 10.2: Constraint Violations During Sync

### Edge Cases and Failure Scenarios

- Concurrent modification scenarios
- Memory exhaustion handling
- Transaction boundary failures
- Index corruption scenarios
- Network interruption scenarios
- Partial sync recovery
- Rollback scenarios
- Lock contention scenarios
- Time synchronization issues
- Timezone handling differences
- Format inconsistency scenarios
- Data type mismatches
- Character encoding issues
- Large object handling
- Blob/CLOB synchronization
- Compressed data handling
- Encrypted data synchronization
- Multi-table dependency scenarios
- Cascade delete handling
- Trigger conflicts during sync
- Stored procedure synchronization
- View synchronization scenarios
- Index synchronization
- Constraint synchronization
- Schema evolution scenarios

### Performance Scenarios

- High-latency network sync
- Low-memory environment sync
- High-concurrency sync scenarios
- Batch size optimization scenarios
- Parallel sync scenarios
- Distributed sync coordination
- Multi-database sync scenarios
- Cross-platform sync scenarios
- Legacy database compatibility
- Cloud database synchronization
- Real-time sync scenarios
- Near-real-time sync scenarios
- Periodic batch sync scenarios
- Incremental sync scenarios
- Full refresh scenarios
- Delta synchronization scenarios

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**Total Count**: 60+ unique scenario types identified across all documents
**Currently Implemented**: 14 scenarios
**Remaining to Document**: 46+ scenarios

This comprehensive list provides the foundation for creating a unified plan with complete scenario coverage.
