# TraceRTM Data Consistency & Synchronization Patterns

Deep research on consistency models, synchronization strategies, and conflict resolution for TraceRTM.

---

## 🎯 CONSISTENCY MODELS

### Model 1: Strong Consistency (SSOT)

**Guarantees**:
- All reads see latest writes
- ACID transactions
- No stale data
- Immediate consistency

**Implementation**:
```python
# All writes go through SSOT
def update_requirement(req_id, **changes):
    session = SessionLocal()
    try:
        req = session.query(Requirement).filter_by(id=req_id).first()
        for key, value in changes.items():
            setattr(req, key, value)
        session.commit()  # ACID guarantee
        return req
    except Exception as e:
        session.rollback()
        raise
    finally:
        session.close()
```

**Use Cases**:
- Canonical data (requirements)
- Critical updates
- Transactional consistency

### Model 2: Eventual Consistency (Views)

**Guarantees**:
- Views eventually match SSOT
- Temporary staleness acceptable
- Refresh within seconds
- Configurable consistency

**Implementation**:
```python
# Views are eventually consistent
def get_impact_analysis(req_id):
    # Read from materialized view (may be stale)
    impact = db.query(ImpactAnalysisView).filter_by(
        source_id=req_id
    ).all()
    
    # If view is too stale, refresh
    if is_view_stale('impact_analysis', req_id):
        refresh_impact_analysis_for_requirement(req_id)
        impact = db.query(ImpactAnalysisView).filter_by(
            source_id=req_id
        ).all()
    
    return impact
```

**Use Cases**:
- Materialized views
- Analytics queries
- Reporting

### Model 3: Causal Consistency

**Guarantees**:
- Related operations maintain order
- Causally related updates visible
- Unrelated updates may be out of order

**Implementation**:
```python
# Track causal relationships
class CausalUpdate:
    def __init__(self, operation_id, depends_on=None):
        self.operation_id = operation_id
        self.depends_on = depends_on or []
        self.timestamp = time.time()
    
    def is_ready(self, completed_operations):
        return all(op in completed_operations for op in self.depends_on)

# Queue updates by causality
pending_updates = []
completed_operations = set()

def apply_update(update):
    if update.is_ready(completed_operations):
        # Apply update
        execute_update(update)
        completed_operations.add(update.operation_id)
    else:
        # Queue for later
        pending_updates.append(update)
```

---

## 🔄 SYNCHRONIZATION STRATEGIES

### Strategy 1: Eager Synchronization

**Approach**: Update all views immediately

**Pros**:
- No staleness
- Simple to understand
- Immediate consistency

**Cons**:
- Slower writes
- Higher latency
- More resource usage

**Implementation**:
```python
def update_requirement(req_id, **changes):
    # Update SSOT
    req = update_ssot(req_id, changes)
    
    # Immediately update all views
    update_traceability_matrix(req_id)
    update_impact_analysis(req_id)
    update_coverage_analysis(req_id)
    update_dependency_graph(req_id)
    update_timeline_view(req_id)
    update_status_dashboard()
    update_search_index(req_id)
    update_agent_interface(req_id)
    
    return req
```

### Strategy 2: Lazy Synchronization

**Approach**: Update views on-demand

**Pros**:
- Faster writes
- Lower latency
- Less resource usage

**Cons**:
- Staleness possible
- Complexity
- Eventual consistency

**Implementation**:
```python
def update_requirement(req_id, **changes):
    # Update SSOT only
    req = update_ssot(req_id, changes)
    
    # Mark views as dirty
    mark_view_dirty('traceability_matrix', req_id)
    mark_view_dirty('impact_analysis', req_id)
    mark_view_dirty('coverage_analysis', req_id)
    
    # Views refresh on next read
    return req

def get_impact_analysis(req_id):
    if is_view_dirty('impact_analysis', req_id):
        refresh_impact_analysis(req_id)
    return db.query(ImpactAnalysisView).filter_by(source_id=req_id).all()
```

### Strategy 3: Batch Synchronization

**Approach**: Update views in batches

**Pros**:
- Balanced performance
- Reduced overhead
- Configurable latency

**Cons**:
- Temporary staleness
- Complexity
- Batch size tuning

**Implementation**:
```python
# Batch updates every 5 seconds
class BatchSynchronizer:
    def __init__(self, batch_interval=5):
        self.batch_interval = batch_interval
        self.dirty_items = set()
        self.last_sync = time.time()
    
    def mark_dirty(self, item_id):
        self.dirty_items.add(item_id)
    
    def sync_if_needed(self):
        if time.time() - self.last_sync > self.batch_interval:
            self.sync_batch()
    
    def sync_batch(self):
        for item_id in self.dirty_items:
            refresh_all_views(item_id)
        self.dirty_items.clear()
        self.last_sync = time.time()

synchronizer = BatchSynchronizer(batch_interval=5)
```

---

## 🔀 CONFLICT RESOLUTION

### Conflict Type 1: Concurrent Updates

**Scenario**: Two users update same requirement simultaneously

**Resolution Strategies**:

**1. Last-Write-Wins (LWW)**:
```python
def resolve_conflict_lww(update1, update2):
    # Keep the update with later timestamp
    if update1.timestamp > update2.timestamp:
        return update1
    else:
        return update2
```

**2. First-Write-Wins (FWW)**:
```python
def resolve_conflict_fww(update1, update2):
    # Keep the first update, reject second
    if update1.timestamp < update2.timestamp:
        return update1
    else:
        return update2
```

**3. Merge-Based**:
```python
def resolve_conflict_merge(update1, update2):
    # Merge non-conflicting fields
    merged = {}
    for field in update1.changes:
        if field in update2.changes:
            if update1.changes[field] == update2.changes[field]:
                merged[field] = update1.changes[field]
            else:
                # Conflict - use LWW
                if update1.timestamp > update2.timestamp:
                    merged[field] = update1.changes[field]
                else:
                    merged[field] = update2.changes[field]
        else:
            merged[field] = update1.changes[field]
    
    for field in update2.changes:
        if field not in merged:
            merged[field] = update2.changes[field]
    
    return merged
```

### Conflict Type 2: Circular Dependencies

**Scenario**: Adding link would create cycle

**Resolution**:
```python
def handle_circular_dependency(source_id, target_id):
    # Detect cycle
    if would_create_cycle(source_id, target_id):
        # Option 1: Reject the link
        raise ValueError("Would create circular dependency")
        
        # Option 2: Break the cycle
        # Find and remove conflicting link
        conflicting_link = find_conflicting_link(source_id, target_id)
        if conflicting_link:
            remove_link(conflicting_link)
            create_link(source_id, target_id)
        
        # Option 3: Warn user
        # Log warning and let user decide
        log_warning(f"Circular dependency detected: {source_id} -> {target_id}")
```

### Conflict Type 3: Stale View Reads

**Scenario**: Reading from stale materialized view

**Resolution**:
```python
def get_with_freshness_guarantee(view_name, item_id, max_staleness_ms=1000):
    # Check view freshness
    last_refresh = get_view_last_refresh(view_name, item_id)
    staleness = time.time() * 1000 - last_refresh
    
    if staleness > max_staleness_ms:
        # Refresh view
        refresh_view(view_name, item_id)
    
    # Read from view
    return db.query(view_name).filter_by(id=item_id).first()
```

---

## 📊 CONSISTENCY GUARANTEES

### SSOT Guarantees

```python
# Atomicity: All or nothing
try:
    update_requirement(req_id, title="New Title")
    update_relationship(req_id, target_id, "depends_on")
    db.commit()  # Both succeed or both fail
except:
    db.rollback()

# Consistency: Valid state maintained
# - Foreign key constraints enforced
# - No orphaned records
# - Bidirectional links maintained

# Isolation: Concurrent updates don't interfere
# - Serializable isolation level
# - Locks prevent conflicts

# Durability: Committed data persists
# - Written to disk
# - Survives failures
```

### View Guarantees

```python
# Eventual Consistency
# - Views eventually match SSOT
# - Refresh within configurable time
# - Temporary staleness acceptable

# Monotonic Consistency
# - Once you see a value, you won't see older value
# - Prevents going backwards in time

# Read-Your-Writes
# - After write, subsequent reads see the write
# - Implemented via SSOT reads
```

---

## 🔍 MONITORING & VERIFICATION

### Consistency Monitoring

```python
class ConsistencyMonitor:
    def __init__(self):
        self.inconsistencies = []
    
    def verify_ssot_view_consistency(self):
        """Verify SSOT and views are consistent"""
        
        # Count requirements in SSOT
        ssot_count = db.query(Requirement).count()
        
        # Count in each view
        view_counts = {
            'traceability': db.query(TraceabilityMatrixView).count(),
            'impact': db.query(ImpactAnalysisView).count(),
            'coverage': db.query(CoverageAnalysisView).count(),
        }
        
        # Check consistency
        for view_name, count in view_counts.items():
            if count != ssot_count:
                self.inconsistencies.append({
                    'view': view_name,
                    'expected': ssot_count,
                    'actual': count,
                    'timestamp': time.time()
                })
        
        return len(self.inconsistencies) == 0
    
    def verify_bidirectional_links(self):
        """Verify bidirectional links are consistent"""
        
        # Get all forward links
        forward_links = db.query(Relationship).all()
        
        for link in forward_links:
            # Check reverse link exists
            reverse = db.query(Relationship).filter(
                Relationship.source_id == link.target_id,
                Relationship.target_id == link.source_id,
                Relationship.relationship_type == get_reverse_type(link.relationship_type)
            ).first()
            
            if not reverse:
                self.inconsistencies.append({
                    'type': 'missing_reverse_link',
                    'forward_link': link.id,
                    'timestamp': time.time()
                })
        
        return len(self.inconsistencies) == 0

monitor = ConsistencyMonitor()
monitor.verify_ssot_view_consistency()
monitor.verify_bidirectional_links()
```

---

## 🚀 IMPLEMENTATION ROADMAP

### Phase 1: Strong Consistency (Week 1)
- ✅ SSOT implementation
- ✅ ACID transactions
- ✅ Referential integrity

### Phase 2: Eventual Consistency (Week 2)
- ✅ Materialized views
- ✅ Incremental refresh
- ✅ Staleness monitoring

### Phase 3: Conflict Resolution (Week 3)
- ✅ Conflict detection
- ✅ Resolution strategies
- ✅ User notifications

### Phase 4: Monitoring (Week 4)
- ✅ Consistency verification
- ✅ Anomaly detection
- ✅ Alerting

---

## 📈 PERFORMANCE TARGETS

| Operation | Target | Consistency |
|-----------|--------|-------------|
| Create requirement | <10ms | Strong |
| Update requirement | <20ms | Strong |
| Create link | <10ms | Strong |
| Query SSOT | <50ms | Strong |
| Query view | <100ms | Eventual |
| Refresh view | <500ms | N/A |

---

## 🎯 BEST PRACTICES

1. **Use strong consistency for SSOT**
2. **Use eventual consistency for views**
3. **Monitor consistency continuously**
4. **Implement conflict resolution**
5. **Test concurrent scenarios**
6. **Document consistency guarantees**
7. **Provide staleness metrics**
8. **Plan for failures**

---

## 🔗 NEXT STEPS

1. Implement SSOT with ACID
2. Create materialized views
3. Add consistency monitoring
4. Implement conflict resolution
5. Load test concurrent scenarios
6. Document consistency model
7. Set up alerting
8. Create runbooks

