# Database Synchronization Scenarios

## Group 1: Size Difference Scenarios

**Core Pattern**: Source and target databases have different numbers of records, requiring either boundary cleanup with deletions or bulk insert operations.

### Scenario: Size Difference with Boundary Cleanup

**Small Scale Example (2 vs 4)**:

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:30:00.DEF | 2025-01-30.08:30:00.DEF | P002 | Product B |

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:15:00.XYZ | 2025-01-30.08:15:00.XYZ | P999 | Old Product 1 |
| 2025-01-30.08:30:00.DEF | 2025-01-30.08:30:00.DEF | P002 | Product B |
| 2025-01-30.09:00:00.GHI | 2025-01-30.09:00:00.GHI | P998 | Old Product 2 |

**Large Scale Example (1 vs 1000)**:

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.07:00:00.001 | 2025-01-30.07:00:00.001 | P100 | Old Product 1 |
| 2025-01-30.07:00:00.002 | 2025-01-30.07:00:00.002 | P101 | Old Product 2 |
| 2025-01-30.08:00:00.050 | 2025-01-30.08:00:00.050 | P150 | Old Product 50 |
| ... (996 more records) |
| 2025-01-30.09:00:00.999 | 2025-01-30.09:00:00.999 | P1099 | Old Product 1000 |

#### Insights (Preserved from All Sources)

**Insight 1.1 (plan.md 1.1)**: Boundary cleanup deletes records outside source range, binary search finds target-only records for DELETE operations

**Insight 1.2 (plan.md 1.2)**: Large size differences with extreme ratios (1:100) are handled effectively through boundary reduction

**Insight 1.3 (plan.md 1.3)**: Sparse distribution with gaps, boundary cleanup removes majority of target records

**Insight 2.1 (plan.md 2.1)**: Missing records detected as INSERT operations when source is larger

**Insight 2.2 (plan.md 2.2)**: Extreme ratios (100:1) with bulk additions processed efficiently

**Insight 2.3 (plan.md 2.3)**: Single target record creates minimal binary search overhead, deterministic new record detection

**Insight 6 (scenario.md)**: Extreme ratios require special handling for resource management and performance optimization

**Insight 8 (scenario.md)**: Sparse target with single record - same as 2.3 pattern

**Insight plan4**: Same pattern as 1.1, boundary cleanup removes excess, binary search finds differences

---

## Group 2: Same Count, Different Records

**Core Pattern**: Source and target have the same number of records but different actual records, requiring individual comparison and complete record replacement.

### Scenario: Same Count with Different Data

**Small Scale Example (3 vs 3)**:

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:30:00.DEF | 2025-01-30.08:30:00.DEF | P002 | Product B |
| 2025-01-30.09:00:00.GHI | 2025-01-30.09:00:00.GHI | P003 | Product C |

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.10:00:00.PQR | 2025-01-30.10:00:00.PQR | P100 | Old Product X |
| 2025-01-30.10:30:00.STU | 2025-01-30.10:30:00.STU | P101 | Old Product Y |
| 2025-01-30.11:00:00.VWX | 2025-01-30.11:00:00.VWX | P102 | Old Product Z |

**Complex Swap Example (5 vs 5)**:

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:15:00.DEF | 2025-01-30.08:15:00.DEF | P002 | Product B |
| 2025-01-30.08:30:00.GHI | 2025-01-30.08:30:00.GHI | P003 | Product C |
| 2025-01-30.08:45:00.JKL | 2025-01-30.08:45:00.JKL | P004 | Product D |
| 2025-01-30.09:00:00.MNO | 2025-01-30.09:00:00.MNO | P005 | Product E |

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:05:000.ABC | 2025-01-30.08:05:000.ABC | P001 | Modified Product A |
| 2025-01-30.08:20:000.DEF | 2025-01-30.08:20:000.DEF | P002 | Modified Product B |
| 2025-01-30.08:35:000.GHI | 2025-01-30.08:35:000.GHI | P003 | Modified Product C |
| 2025-01-30.08:50:000.JKL | 2025-01-30.08:50:000.JKL | P004 | Modified Product D |
| 2025-01-30.09:05:000.MNO | 2025-01-30.09:05:000.MNO | P005 | Modified Product E |

#### Insights (Preserved from All Sources)

**Insight 3.1 (plan.md)**: Same counts don't hide differences, each record processed individually

**Insight 3.2 (plan.md)**: Complex swapping patterns within single range, individual record comparison catches all

**Insight 3.3 (plan.md)**: Sparse distribution handling, large gaps don't cause problems

**Insight 7 (scenario.md)**: Same as 3.1 - individual processing needed despite same counts

**Insight 10 (scenario.md)**: Complete database replacement scenario despite same count

**Insight plan4**: Same as 10 - complete replacement needed for all records

---

## Group 3: Empty Database Scenarios

**Core Pattern**: One database is empty, requiring either complete insertion or deletion operations.

### Scenario: Empty Database Handling

**Target Empty Example (5 vs 0)**:

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:15:00.DEF | 2025-01-30.08:15:00.DEF | P002 | Product B |
| 2025-01-30.08:30:00.GHI | 2025-01-30.08:30:00.GHI | P003 | Product C |
| 2025-01-30.08:45:00.JKL | 2025-01-30.08:45:00.JKL | P004 | Product D |
| 2025-01-30.09:00:00.MNO | 2025-01-30.09:00:00.MNO | P005 | Product E |

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
*No records in target database*

**Source Empty Example (0 vs 5)**:

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
*No records in source database*

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.10:00:00.PQR | 2025-01-30.10:00:00.PQR | P100 | Old Product X |
| 2025-01-30.10:15:00.STU | 2025-01-30.10:15:00.STU | P101 | Old Product Y |
| 2025-01-30.10:30:00.VWX | 2025-01-30.10:30:00.VWX | P102 | Old Product Z |
| 2025-01-30.10:45:00.YZA | 2025-01-30.10:45:00.YZA | P103 | Old Product W |
| 2025-01-30.11:00:00.ZAB | 2025-01-30.11:00:00.ZAB | P104 | Old Product V |

#### Insights (Preserved from All Sources)

**Insight 4.1 (plan.md)**: All source records become INSERT operations, direct bulk addition

**Insight 4.2 (plan.md)**: All target records become DELETE operations, complete target cleanup

**Insight 4.3 (plan.md)**: No operations needed, trivial case handling

**Insight 5 (scenario.md)**: Same as 4.1 - all records become INSERT operations

**Insight plan4**: Same as 4.1 and 5 - bulk insert scenario

---

## Group 4: Edge Boundary Cases

**Core Pattern**: LastId positioning creates edge cases for binary search ranges, affecting performance and behavior.

### Scenario: Edge Boundary Optimization

**LastId at Source Min**:

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:30:00.DEF | 2025-01-30.08:30:00.DEF | P002 | Product B |
| 2025-01-30.09:00:00.GHI | 2025-01-30.09:00:00.GHI | P003 | Product C |

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |

**LastId at Source Max**:

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:30:00.DEF | 2025-01-30.08:30:00.DEF | P002 | Product B |
| 2025-01-30.09:00:00.GHI | 2025-01-30.09:00:00.GHI | P003 | Product C |

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.09:00:00.GHI | 2025-01-30.09:00:00.GHI | P003 | Product C |

**No Shared Records**:

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:30:00.DEF | 2025-01-30.08:30:00.DEF | P002 | Product B |
| 2025-01-30.09:00:00.GHI | 2025-01-30.09:00:00.GHI | P003 | Product C |

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.10:00:00.PQR | 2025-01-30.10:00:00.PQR | P100 | Old Product X |
| 2025-01-30.10:30:00.STU | 2025-01-30.10:30:00.STU | P101 | Old Product Y |

#### Insights (Preserved from All Sources)

**Insight 7.1 (plan.md)**: Edge boundary with minimal binary search range, majority handled as new additions

**Insight 7.2 (plan.md)**: Maximum boundary creates full-range binary search, all processed within binary search

**Insight 7.3 (plan.md)**: No overlap eliminates binary search entirely, optimal performance

**Insight 11 (scenario.md)**: Same as 7.1 - minimal range scenario

**Insight 12 (scenario.md)**: Same as 7.2 - full-range scenario

**Insight 13 (scenario.md)**: Same as 7.3 - no overlap eliminates binary search

---

## Group 5: Data Integrity Violations

**Core Pattern**: Constraint violations and data integrity issues that must be detected and handled safely.

### Scenario: Data Integrity Violation Handling

**Duplicate Record IDs**:

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P002 | Product B |
| 2025-01-30.08:30:00.DEF | 2025-01-30.08:30:00.DEF | P003 | Product C |

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:30:00.DEF | 2025-01-30.08:30:00.DEF | P003 | Product C |

**Constraint Violations**:

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:30:00.DEF | 2025-01-30.08:30:00.DEF | P002 | Product B |

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.09:00:00.GHI | 2025-01-30.09:00:00.GHI | P003 | Product C |

#### Insights (Preserved from All Sources)

**Insight 10.1 (plan.md)**: Data integrity violation, system fails safely with clear error reporting

**Insight 10.2 (plan.md)**: Constraint violation scenarios, order-dependent failures handled by temporary PKs

**Insight 14 (scenario.md)**: Same as 10.1 - duplicate ID detection and safe failure

**Insight plan2-1**: Duplicate record_ids violate ordering assumptions, need error detection

**Insight plan2-2**: Circular dependencies require Tarjan's algorithm for cycle detection

**Insight plan2-3**: Order-dependent constraint violations need temporary key mechanisms

---

## Group 6: Complete Replacement Scenarios

**Core Pattern**: All target records need to be replaced, either through boundary cleanup or individual operations.

### Scenario: Complete Database Replacement

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:15:00.DEF | 2025-01-30.08:15:00.DEF | P002 | Product B |
| 2025-01-30.08:30:00.GHI | 2025-01-30.08:30:00.GHI | P003 | Product C |
| 2025-01-30.08:45:00.JKL | 2025-01-30.08:45:00.JKL | P004 | Product D |
| 2025-01-30.09:00:00.MNO | 2025-01-30.09:00:00.MNO | P005 | Product E |

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.10:00:00.PQR | 2025-01-30.10:00:00.PQR | P100 | Old Product X |
| 2025-01-30.10:15:00.STU | 2025-01-30.10:15:00.STU | P101 | Old Product Y |
| 2025-01-30.10:30:00.VWX | 2025-01-30.10:30:00.VWX | P102 | Old Product Z |
| 2025-01-30.10:45:00.YZA | 2025-01-30.10:45:00.YZA | P103 | Old Product W |
| 2025-01-30.11:00:00.ZAB | 2025-01-30.11:00:00.ZAB | P104 | Old Product V |

#### Insights (Preserved from All Sources)

**Insight 3 (scenario.md)**: Boundary cleanup deletes all target records, complete replacement

**Insight plan4**: Same pattern - boundary cleanup deletes all, complete replacement

**Insight 5.1 (plan.md)**: No shared boundaries, boundary cleanup eliminates all target records

---

## Group 7: Symmetric Difference Scenarios

**Core Pattern**: Balanced additions and deletions between source and target databases.

### Scenario: Symmetric Differences

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:30:00.DEF | 2025-01-30.08:30:00.DEF | P002 | Product B |
| 2025-01-30.09:00:00.GHI | 2025-01-30.09:00:00.GHI | P003 | Product C |

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:30:00.DEF | 2025-01-30.08:30:00.DEF | P002 | Product B |
| 2025-01-30.09:00:00.GHI | 2025-01-30.09:00:00.GHI | P003 | Product C |
| 2025-01-30.09:30:00.JKL | 2025-01-30.09:30:00.JKL | P999 | Old Product |

#### Insights (Preserved from All Sources)

**Insight 4 (scenario.md)**: Balanced changes handling (one deleted, one added)

**Insight plan4**: Same pattern - balanced changes between databases

---

## Group 8: Sparse Distribution Scenarios

**Core Pattern**: Large gaps between records create unique challenges for range-based operations.

### Scenario: Sparse Distribution Handling

### Source Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.09:00:00.GHI | 2025-01-30.09:00:00.GHI | P003 | Product C |
| 2025-01-30.10:00:00.MNO | 2025-01-30.10:00:00.MNO | P005 | Product E |

### Target Data

| record_id | modify_id | product_id | name |
|-----------|-----------|------------|------|
| 2025-01-30.08:00:00.ABC | 2025-01-30.08:00:00.ABC | P001 | Product A |
| 2025-01-30.08:30:00.DEF | 2025-01-30.08:30:00.DEF | P002 | Product B |
| 2025-01-30.09:00:00.GHI | 2025-01-30.09:00:00.GHI | P003 | Product C |
| 2025-01-30.09:30:00.JKL | 2025-01-30.09:30:00.JKL | P004 | Product D |
| 2025-01-30.10:00:00.MNO | 2025-01-30.10:00:00.MNO | P005 | Product E |

#### Insights (Preserved from All Sources)

**Insight 9 (scenario.md)**: Large gaps between records don't cause problems - individual record processing works

**Insight 6.1 (plan.md)**: Large gaps in source require special handling

**Insight 6.2 (plan.md)**: Dense target, sparse source - inverse density scenario

---

## Separate Section: Failure Mode Scenarios

These scenarios represent unique failure conditions that don't fit into the core pattern groups.

### 1. Concurrent Modification During Sync

**Scenario**: Source database changes during synchronization

- Source starts with records [ABC, DEF, GHI]
- During sync, record DEF is deleted and record JKL is added
- Binary search operates on inconsistent view

**Insights (plan3.md)**: Violates Axiom 1 (Source Never Changes), requires database locking or read-only snapshots

### 2. Record ID Uniqueness Violations

**Scenario**: Multiple records share same record_id

- Hash table lookups find wrong records, binary search becomes invalid
- System only warns about duplicate record_ids but continues processing

**Insights (plan3.md)**: Violates Axiom 2 (Record ID is Only Truth), causes data corruption

### 3. Binary Search Logical Errors

**Scenario**: Equal counts but different records

- Binary search detects mathematical impossibility but continues processing
- Silent data corruption when equal counts hide different records

**Insights (plan3.md)**: Mathematical impossibility, needs validation and halting

### 4. Memory Exhaustion

**Scenario**: Large datasets exceed memory limits

- 10 million records need synchronization
- System runs out of memory after processing 5 million
- Remaining records not processed

**Insights (plan3.md)**: Incomplete sync due to resource constraints, needs batch processing

### 5. Transaction Boundary Issues

**Scenario**: Large operations exceed transaction limits

- Partial commits, inconsistent database state
- No rollback mechanism implemented

**Insights (plan3.md)**: Transaction management failure, needs proper rollback

### 6. Index Corruption

**Scenario**: Database indexes inconsistent with actual data

- Range queries miss or double-count records
- Binary search works on incorrect data

**Insights (plan2.md)**: Index validation and repair needed

### 7. Constraint Violations During Sync

**Scenario**: Foreign key constraints prevent applying valid changes

- Source has record A referencing record B
- Target has record B referencing record A
- Circular dependency prevents updates

**Insights (plan2.md)**: Temporary key mechanisms and dependency resolution needed

### 8. Empty Database Validation Gaps

**Scenario**: Empty target skips all validation

- No verification of source data integrity
- Missing consistency checks

**Insights (plan3.md)**: Empty databases need validation, not just direct insertion

---

## Separate Section: Performance and Resource Scenarios

### 1. High-Latency Network Sync

- Large datasets over slow connections
- Timeout handling needed
- Chunked synchronization approaches

### 2. Low-Memory Environment Sync

- Resource-constrained environments
- Streaming processing vs batch loading
- Memory usage optimization

### 3. High-Concurrency Scenarios

- Multiple sync processes simultaneously
- Race conditions and data corruption
- Operation locking and serialization

### 4. Multi-Database Coordination

- Distributed sync across multiple databases
- Consistency guarantees across systems
- Conflict resolution strategies

---

## Summary of Consolidation

**Before**: 60+ individual scenarios across multiple documents
**After**: 8 core pattern groups + separate failure mode and performance sections

**Benefits**:

- Reduced complexity while maintaining comprehensive coverage
- All insights preserved with source attribution
- Clear organization for new plan development
- Systematic approach to testing and validation
- Evolution of understanding visible through numbered insights

**Implementation Priority**:

1. **High Priority**: Core patterns (Groups 1-5) - essential functionality
2. **Medium Priority**: Data integrity (Group 6) - critical for safety
3. **Low Priority**: Edge cases and performance - important but less common
