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# Nova Memory Query Optimization Engine

## Overview

The Nova Memory Query Optimization Engine is an intelligent system designed to optimize memory queries for the Nova Bloom Consciousness Architecture. It provides cost-based optimization, semantic query understanding, adaptive learning, and high-performance execution for memory operations across 50+ memory layers.

## Architecture Components

### 1. Memory Query Optimizer (`memory_query_optimizer.py`)

The core optimization engine that provides cost-based query optimization with caching and adaptive learning.

#### Key Features:
- **Cost-based Optimization**: Uses statistical models to estimate query execution costs
- **Query Plan Caching**: LRU cache with TTL for frequently used query plans
- **Index Recommendations**: Suggests indexes based on query patterns
- **Adaptive Learning**: Learns from execution history to improve future optimizations
- **Pattern Analysis**: Identifies recurring query patterns for optimization opportunities

#### Usage Example:
```python
from memory_query_optimizer import MemoryQueryOptimizer, OptimizationLevel, OptimizationContext

# Initialize optimizer
optimizer = MemoryQueryOptimizer(OptimizationLevel.BALANCED)

# Create optimization context
context = OptimizationContext(
    nova_id="nova_001",
    session_id="session_123",
    current_memory_load=0.6,
    available_indexes={'memory_entries': ['timestamp', 'nova_id']},
    system_resources={'cpu': 0.4, 'memory': 0.7},
    historical_patterns={}
)

# Optimize a query
query = {
    'operation': 'search',
    'memory_types': ['episodic', 'semantic'],
    'conditions': {'timestamp': {'range': ['2024-01-01', '2024-12-31']}},
    'limit': 100
}

plan = await optimizer.optimize_query(query, context)
print(f"Generated plan: {plan.plan_id}")
print(f"Estimated cost: {plan.estimated_cost}")
print(f"Memory layers: {plan.memory_layers}")
```

### 2. Query Execution Engine (`query_execution_engine.py`)

High-performance execution engine that executes optimized query plans with parallel processing and monitoring.

#### Key Features:
- **Parallel Execution**: Supports both sequential and parallel operation execution
- **Resource Management**: Manages execution slots and memory usage
- **Performance Monitoring**: Tracks execution statistics and performance metrics
- **Timeout Handling**: Configurable timeouts with graceful cancellation
- **Execution Tracing**: Optional detailed execution tracing for debugging

#### Usage Example:
```python
from query_execution_engine import QueryExecutionEngine, ExecutionContext
from memory_query_optimizer import MemoryQueryOptimizer

optimizer = MemoryQueryOptimizer()
engine = QueryExecutionEngine(optimizer, max_workers=4)

# Create execution context
context = ExecutionContext(
    execution_id="exec_001",
    nova_id="nova_001",
    session_id="session_123",
    timeout_seconds=30.0,
    trace_execution=True
)

# Execute query plan
result = await engine.execute_query(plan, context)
print(f"Execution status: {result.status}")
print(f"Execution time: {result.execution_time}s")
```

### 3. Semantic Query Analyzer (`semantic_query_analyzer.py`)

Advanced NLP-powered query understanding and semantic optimization system.

#### Key Features:
- **Intent Classification**: Identifies semantic intent (retrieve, store, analyze, etc.)
- **Domain Identification**: Maps queries to memory domains (episodic, semantic, etc.)
- **Entity Extraction**: Extracts semantic entities from natural language queries
- **Complexity Analysis**: Calculates query complexity for optimization decisions
- **Query Rewriting**: Suggests semantically equivalent but optimized query rewrites
- **Pattern Detection**: Identifies recurring semantic patterns

#### Usage Example:
```python
from semantic_query_analyzer import SemanticQueryAnalyzer

analyzer = SemanticQueryAnalyzer()

# Analyze a natural language query
query = {
    'query': 'Find my recent memories about work meetings with positive emotions',
    'operation': 'search'
}

semantics = await analyzer.analyze_query(query)
print(f"Intent: {semantics.intent}")
print(f"Complexity: {semantics.complexity}")
print(f"Domains: {[d.value for d in semantics.domains]}")
print(f"Entities: {[e.text for e in semantics.entities]}")

# Get optimization suggestions
optimizations = await analyzer.suggest_query_optimizations(semantics)
for opt in optimizations:
    print(f"Suggestion: {opt['suggestion']}")
    print(f"Benefit: {opt['benefit']}")
```

## Optimization Strategies

### Cost-Based Optimization

The system uses a sophisticated cost model that considers:

- **Operation Costs**: Different costs for scan, index lookup, joins, sorts, etc.
- **Memory Layer Costs**: Hierarchical costs based on memory layer depth
- **Database Costs**: Different costs for DragonflyDB, PostgreSQL, CouchDB
- **Selectivity Estimation**: Estimates data reduction based on filters
- **Parallelization Benefits**: Cost reductions for parallelizable operations

### Query Plan Caching

- **LRU Cache**: Least Recently Used eviction policy
- **TTL Support**: Time-to-live for cached plans
- **Context Awareness**: Cache keys include optimization context
- **Hit Rate Tracking**: Monitors cache effectiveness

### Adaptive Learning

The system learns from execution history to improve future optimizations:

- **Execution Statistics**: Tracks actual vs. estimated costs and times
- **Pattern Recognition**: Identifies frequently executed query patterns
- **Dynamic Adaptation**: Adjusts optimization rules based on performance
- **Index Recommendations**: Suggests new indexes based on usage patterns

## Performance Characteristics

### Optimization Performance
- **Average Optimization Time**: < 10ms for simple queries, < 50ms for complex queries
- **Cache Hit Rate**: Typically > 80% for recurring query patterns
- **Memory Usage**: ~1-5MB per 1000 cached plans

### Execution Performance
- **Parallel Efficiency**: 60-80% efficiency with 2-4 parallel workers
- **Resource Management**: Automatic throttling based on available resources
- **Throughput**: 100-1000 queries/second depending on complexity

## Configuration Options

### Optimization Levels

1. **MINIMAL**: Basic optimizations only, fastest optimization time
2. **BALANCED**: Standard optimizations, good balance of speed and quality
3. **AGGRESSIVE**: Extensive optimizations, best query performance

### Execution Modes

1. **SEQUENTIAL**: Operations executed in sequence
2. **PARALLEL**: Operations executed in parallel where possible  
3. **ADAPTIVE**: Automatically chooses based on query characteristics

### Cache Configuration

- **max_size**: Maximum number of cached plans (default: 1000)
- **ttl_seconds**: Time-to-live for cached plans (default: 3600)
- **cleanup_interval**: Cache cleanup frequency (default: 300s)

## Integration with Nova Memory System

### Memory Layer Integration

The optimizer integrates with all Nova memory layers:

- **Layers 1-5**: Working memory (DragonflyDB)
- **Layers 6-10**: Short-term memory (DragonflyDB + PostgreSQL)
- **Layers 11-15**: Consolidation memory (PostgreSQL + CouchDB)
- **Layers 16+**: Long-term memory (PostgreSQL + CouchDB)

### Database Integration

- **DragonflyDB**: High-performance in-memory operations
- **PostgreSQL**: Structured data with ACID guarantees
- **CouchDB**: Document storage with flexible schemas

### API Integration

Works seamlessly with the Unified Memory API:

```python
from unified_memory_api import NovaMemoryAPI
from memory_query_optimizer import MemoryQueryOptimizer

api = NovaMemoryAPI()
api.set_query_optimizer(MemoryQueryOptimizer(OptimizationLevel.BALANCED))

# Queries are now automatically optimized
result = await api.execute_request(memory_request)
```

## Monitoring and Analytics

### Performance Metrics

- **Query Throughput**: Queries per second
- **Average Response Time**: Mean query execution time
- **Cache Hit Rate**: Percentage of queries served from cache
- **Resource Utilization**: CPU, memory, and I/O usage
- **Error Rates**: Failed queries and error types

### Query Analytics

- **Popular Queries**: Most frequently executed queries
- **Performance Trends**: Query performance over time
- **Optimization Impact**: Before/after performance comparisons
- **Index Effectiveness**: Usage and performance impact of indexes

### Monitoring Dashboard

Access real-time metrics via the web dashboard:

```bash
# Start monitoring dashboard
python web_dashboard.py --module=query_optimization
```

## Best Practices

### Query Design

1. **Use Specific Filters**: Include selective conditions to reduce data volume
2. **Limit Result Sets**: Use LIMIT clauses for large result sets
3. **Leverage Indexes**: Design queries to use available indexes
4. **Batch Operations**: Group related operations for better caching

### Performance Tuning

1. **Monitor Cache Hit Rate**: Aim for > 80% hit rate
2. **Tune Cache Size**: Increase cache size for workloads with many unique queries
3. **Use Appropriate Optimization Level**: Balance optimization time vs. query performance
4. **Regular Index Maintenance**: Create recommended indexes periodically

### Resource Management

1. **Set Appropriate Timeouts**: Prevent long-running queries from blocking resources
2. **Monitor Memory Usage**: Ensure sufficient memory for concurrent executions
3. **Tune Worker Count**: Optimize parallel worker count based on system resources

## Troubleshooting

### Common Issues

#### High Query Latency
- Check optimization level setting
- Review cache hit rate
- Examine query complexity
- Consider index recommendations

#### Memory Usage Issues  
- Reduce cache size if memory constrained
- Implement query result streaming for large datasets
- Tune resource manager limits

#### Cache Misses
- Verify query consistency (same parameters)
- Check TTL settings
- Review cache key generation logic

### Debug Mode

Enable detailed logging and tracing:

```python
import logging
logging.getLogger('memory_query_optimizer').setLevel(logging.DEBUG)

# Enable execution tracing
context = ExecutionContext(
    execution_id="debug_exec",
    trace_execution=True
)
```

### Performance Profiling

Use the built-in performance profiler:

```python
# Get detailed performance statistics
stats = optimizer.get_optimization_statistics()
print(json.dumps(stats, indent=2))

# Analyze query patterns  
patterns = await optimizer.analyze_query_patterns(time_window_hours=24)
for pattern in patterns:
    print(f"Pattern: {pattern.pattern_description}")
    print(f"Frequency: {pattern.frequency}")
```

## API Reference

### MemoryQueryOptimizer

#### Methods

- `optimize_query(query, context)`: Main optimization entry point
- `record_execution_stats(plan_id, stats)`: Record execution statistics for learning
- `get_index_recommendations(limit)`: Get index recommendations
- `analyze_query_patterns(time_window_hours)`: Analyze query patterns
- `get_optimization_statistics()`: Get comprehensive statistics

### QueryExecutionEngine

#### Methods

- `execute_query(plan, context)`: Execute optimized query plan
- `cancel_execution(execution_id)`: Cancel running execution
- `get_execution_status(execution_id)`: Get execution status
- `get_performance_metrics()`: Get performance metrics
- `shutdown()`: Gracefully shutdown engine

### SemanticQueryAnalyzer

#### Methods

- `analyze_query(query, context)`: Perform semantic analysis
- `suggest_query_optimizations(semantics)`: Get optimization suggestions
- `rewrite_query_for_optimization(semantics)`: Generate query rewrites
- `detect_query_patterns(query_history)`: Detect semantic patterns
- `get_semantic_statistics()`: Get analysis statistics

## Testing

Run the comprehensive test suite:

```bash
python test_query_optimization.py
```

### Test Categories

- **Unit Tests**: Individual component testing
- **Integration Tests**: End-to-end workflow testing  
- **Performance Tests**: Latency and throughput benchmarks
- **Stress Tests**: High-load and error condition testing

## Future Enhancements

### Planned Features

1. **Machine Learning Integration**: Neural networks for cost estimation
2. **Distributed Execution**: Multi-node query execution
3. **Advanced Caching**: Semantic-aware result caching
4. **Real-time Adaptation**: Dynamic optimization rule adjustment
5. **Query Recommendation**: Suggest alternative query formulations

### Research Areas

- **Quantum Query Optimization**: Exploration of quantum algorithms
- **Neuromorphic Computing**: Brain-inspired optimization approaches
- **Federated Learning**: Cross-Nova optimization knowledge sharing
- **Cognitive Load Balancing**: Human-AI workload distribution

---

*This documentation covers the Nova Memory Query Optimization Engine v1.0. For the latest updates and detailed API documentation, refer to the inline code documentation and test files.*