# Performance Optimization Guide

This comprehensive guide covers performance optimization, benchmarking, and monitoring strategies for SAFLA (Self-Aware Feedback Loop Algorithm). Our performance approach focuses on measurable improvements with specific targets and continuous monitoring.

## Table of Contents

1. [Performance Philosophy](#performance-philosophy)
2. [Performance Targets](#performance-targets)
3. [Benchmarking Framework](#benchmarking-framework)
4. [Component-Specific Optimizations](#component-specific-optimizations)
5. [Memory Optimization](#memory-optimization)
6. [Concurrency and Parallelization](#concurrency-and-parallelization)
7. [Caching Strategies](#caching-strategies)
8. [Performance Monitoring](#performance-monitoring)
9. [Profiling and Analysis](#profiling-and-analysis)
10. [Optimization Techniques](#optimization-techniques)
11. [Performance Testing](#performance-testing)
12. [Troubleshooting Performance Issues](#troubleshooting-performance-issues)

## Performance Philosophy

SAFLA's performance optimization is guided by several key principles:

### Measurement-Driven Optimization
All optimizations are based on concrete measurements and benchmarks:
- **Baseline Establishment**: Measure current performance before optimization
- **Target-Driven**: Set specific, measurable performance targets
- **Continuous Monitoring**: Track performance over time
- **Regression Prevention**: Ensure optimizations don't introduce regressions

### Component-Specific Targets
Each component has specific performance requirements:
- **Vector Search**: <1ms latency for 10k vectors
- **Delta Evaluation**: 50% throughput improvement over baseline
- **MCP Communication**: <5ms round-trip latency
- **Safety Validation**: <10ms real-time performance

### Adaptive Performance
The system adapts its performance strategies based on load and conditions:
- **Load-Based Optimization**: Adjust strategies based on current load
- **Resource-Aware**: Consider available system resources
- **Graceful Degradation**: Maintain functionality under high load

## Performance Targets

### Core Performance Benchmarks

Based on our performance test suite in [`test_performance_benchmarks.py`](../../tests/test_performance_benchmarks.py):

#### Vector Similarity Search
```python
VECTOR_SEARCH_BENCHMARKS = [
    PerformanceBenchmark(
        name="vector_search_latency_10k",
        target_value=1.0,  # <1ms
        target_unit="ms",
        description="Vector similarity search latency for 10k vectors"
    ),
    PerformanceBenchmark(
        name="vector_search_throughput",
        target_value=1000.0,  # 1000 searches/second
        target_unit="ops/sec",
        description="Vector similarity search throughput"
    ),
    PerformanceBenchmark(
        name="vector_batch_store_latency",
        target_value=10.0,  # <10ms for 1000 vectors
        target_unit="ms",
        description="Batch vector storage latency"
    ),
    PerformanceBenchmark(
        name="vector_memory_efficiency",
        target_value=0.8,  # 80% memory efficiency
        target_unit="ratio",
        description="Vector memory storage efficiency"
    )
]
```

#### Delta Evaluation Pipeline
```python
DELTA_EVALUATION_BENCHMARKS = [
    PerformanceBenchmark(
        name="delta_evaluation_throughput",
        target_value=500.0,  # 50% improvement over baseline
        target_unit="ops/sec",
        description="Delta evaluation pipeline throughput"
    ),
    PerformanceBenchmark(
        name="delta_calculation_latency",
        target_value=2.0,  # <2ms per evaluation
        target_unit="ms",
        description="Individual delta calculation latency"
    ),
    PerformanceBenchmark(
        name="batch_delta_processing",
        target_value=100.0,  # 100 evaluations per batch
        target_unit="ops/batch",
        description="Batch delta evaluation processing"
    )
]
```

#### MCP Communication
```python
MCP_COMMUNICATION_BENCHMARKS = [
    PerformanceBenchmark(
        name="mcp_round_trip_latency",
        target_value=5.0,  # <5ms round-trip
        target_unit="ms",
        description="MCP server communication round-trip latency"
    ),
    PerformanceBenchmark(
        name="mcp_concurrent_requests",
        target_value=100.0,  # 100 concurrent requests
        target_unit="requests",
        description="MCP server concurrent request handling"
    ),
    PerformanceBenchmark(
        name="mcp_message_throughput",
        target_value=1000.0,  # 1000 messages/second
        target_unit="msgs/sec",
        description="MCP message processing throughput"
    )
]
```

#### Safety Validation
```python
SAFETY_VALIDATION_BENCHMARKS = [
    PerformanceBenchmark(
        name="safety_validation_latency",
        target_value=10.0,  # <10ms real-time performance
        target_unit="ms",
        description="Safety validation pipeline latency"
    ),
    PerformanceBenchmark(
        name="safety_validation_throughput",
        target_value=100.0,  # 100 validations/second
        target_unit="ops/sec",
        description="Safety validation throughput"
    ),
    PerformanceBenchmark(
        name="safety_rule_evaluation_speed",
        target_value=1000.0,  # 1000 rules/second
        target_unit="rules/sec",
        description="Safety rule evaluation speed"
    )
]
```

## Benchmarking Framework

SAFLA includes a comprehensive benchmarking framework for measuring and tracking performance across all system components. The framework provides modular, extensible benchmarking capabilities with persistent storage and trend analysis.

### Framework Architecture

The benchmarking framework consists of several key components:

- **Core Framework** ([`benchmarks/core.py`](../../benchmarks/core.py)): Abstract base classes and execution engine
- **Database Layer** ([`benchmarks/database.py`](../../benchmarks/database.py)): SQLite-based persistent storage
- **CLI Benchmarks** ([`benchmarks/cli_benchmarks.py`](../../benchmarks/cli_benchmarks.py)): SAFLA CLI-specific benchmarks
- **Utilities** ([`benchmarks/utils.py`](../../benchmarks/utils.py)): Configuration, metrics, and analysis tools

### Core Classes

#### Benchmark Base Class
```python
class Benchmark(ABC):
    """Abstract base class for all benchmarks."""
    
    def __init__(self, name: str, description: str = "", config: Optional[BenchmarkConfig] = None):
        self.name = name
        self.description = description
        self.config = config or BenchmarkConfig()
    
    @abstractmethod
    async def run(self) -> BenchmarkResult:
        """Execute the benchmark and return results."""
        pass
    
    @abstractmethod
    async def setup(self) -> None:
        """Setup benchmark environment."""
        pass
    
    @abstractmethod
    async def teardown(self) -> None:
        """Cleanup benchmark environment."""
        pass
```

#### Benchmark Result
```python
@dataclass
class BenchmarkResult:
    """Result of a benchmark execution."""
    benchmark_name: str
    execution_time: float
    memory_usage: float
    success: bool
    error_message: Optional[str] = None
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert result to dictionary for storage."""
        return {
            'benchmark_name': self.benchmark_name,
            'execution_time': self.execution_time,
            'memory_usage': self.memory_usage,
            'success': self.success,
            'error_message': self.error_message,
            'metadata': json.dumps(self.metadata),
            'timestamp': self.timestamp.isoformat()
        }
```

#### Benchmark Runner
```python
class BenchmarkRunner:
    """Executes benchmarks with monitoring and error handling."""
    
    def __init__(self, database: Optional[BenchmarkDatabase] = None):
        self.database = database or BenchmarkDatabase()
        self.results: List[BenchmarkResult] = []
    
    async def run_benchmark(self, benchmark: Benchmark) -> BenchmarkResult:
        """Run a single benchmark with full monitoring."""
        # Memory monitoring setup
        process = psutil.Process()
        initial_memory = process.memory_info().rss / 1024 / 1024  # MB
        
        try:
            await benchmark.setup()
            
            start_time = time.perf_counter()
            await benchmark.run()
            end_time = time.perf_counter()
            
            execution_time = end_time - start_time
            final_memory = process.memory_info().rss / 1024 / 1024  # MB
            memory_usage = final_memory - initial_memory
            
            result = BenchmarkResult(
                benchmark_name=benchmark.name,
                execution_time=execution_time,
                memory_usage=memory_usage,
                success=True
            )
            
        except Exception as e:
            result = BenchmarkResult(
                benchmark_name=benchmark.name,
                execution_time=0.0,
                memory_usage=0.0,
                success=False,
                error_message=str(e)
            )
        finally:
            await benchmark.teardown()
        
        # Store result
        self.results.append(result)
        if self.database:
            await self.database.store_result(result)
        
        return result
```

### CLI Integration

The benchmarking framework is integrated into the SAFLA CLI with dedicated commands:

```bash
# Run all benchmarks
safla benchmark run

# Run specific benchmark
safla benchmark run --name "cli_help_performance"

# List available benchmarks
safla benchmark list

# View benchmark results
safla benchmark results

# Analyze performance trends
safla benchmark analyze
```

### Available CLI Benchmarks

The framework includes comprehensive CLI benchmarks:

1. **CLI Help Performance** - Tests `safla --help` response time
2. **CLI Version Performance** - Tests `safla --version` response time
3. **CLI List Commands Performance** - Tests command listing speed
4. **CLI Invalid Command Performance** - Tests error handling speed
5. **CLI Benchmark List Performance** - Tests benchmark listing speed
6. **CLI Benchmark Results Performance** - Tests results retrieval speed
7. **CLI Benchmark Run Performance** - Tests benchmark execution speed
8. **CLI Benchmark Analyze Performance** - Tests analysis performance

### Database Schema

The framework uses SQLite for persistent storage with the following schema:

```sql
CREATE TABLE benchmark_results (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    benchmark_name TEXT NOT NULL,
    execution_time REAL NOT NULL,
    memory_usage REAL NOT NULL,
    success BOOLEAN NOT NULL,
    error_message TEXT,
    metadata TEXT,
    timestamp TEXT NOT NULL
);

CREATE INDEX idx_benchmark_name ON benchmark_results(benchmark_name);
CREATE INDEX idx_timestamp ON benchmark_results(timestamp);
```

### Performance Analysis

The framework provides comprehensive analysis capabilities:

#### Trend Analysis
```python
class BenchmarkAnalyzer:
    """Analyzes benchmark results for trends and insights."""
    
    async def analyze_trends(self, benchmark_name: str, days: int = 30) -> Dict[str, Any]:
        """Analyze performance trends over time."""
        results = await self.database.get_results_by_name(benchmark_name, days)
        
        if len(results) < 2:
            return {"error": "Insufficient data for trend analysis"}
        
        execution_times = [r.execution_time for r in results]
        
        # Statistical analysis
        mean_time = statistics.mean(execution_times)
        median_time = statistics.median(execution_times)
        std_dev = statistics.stdev(execution_times) if len(execution_times) > 1 else 0
        
        # Trend detection using linear regression
        x = list(range(len(execution_times)))
        slope = self._calculate_slope(x, execution_times)
        
        return {
            "benchmark_name": benchmark_name,
            "total_runs": len(results),
            "mean_execution_time": mean_time,
            "median_execution_time": median_time,
            "std_deviation": std_dev,
            "trend_slope": slope,
            "trend_direction": "improving" if slope < 0 else "degrading" if slope > 0 else "stable",
            "performance_stability": "stable" if std_dev < mean_time * 0.1 else "variable"
        }
```

#### Performance Metrics
```python
class PerformanceMetrics:
    """Calculates and tracks performance metrics."""
    
    @staticmethod
    def calculate_percentiles(values: List[float]) -> Dict[str, float]:
        """Calculate performance percentiles."""
        if not values:
            return {}
        
        sorted_values = sorted(values)
        n = len(sorted_values)
        
        return {
            "p50": sorted_values[int(n * 0.5)],
            "p90": sorted_values[int(n * 0.9)],
            "p95": sorted_values[int(n * 0.95)],
            "p99": sorted_values[int(n * 0.99)] if n >= 100 else sorted_values[-1]
        }
    
    @staticmethod
    def detect_regressions(current_results: List[float], baseline_results: List[float], threshold: float = 0.2) -> bool:
        """Detect performance regressions."""
        if not current_results or not baseline_results:
            return False
        
        current_mean = statistics.mean(current_results)
        baseline_mean = statistics.mean(baseline_results)
        
        regression_ratio = (current_mean - baseline_mean) / baseline_mean
        return regression_ratio > threshold
```

### Configuration

Benchmarks are configured using the `BenchmarkConfig` class:

```python
@dataclass
class BenchmarkConfig:
    """Configuration for benchmark execution."""
    iterations: int = 10
    warmup_iterations: int = 2
    timeout_seconds: float = 30.0
    memory_limit_mb: float = 1000.0
    parallel_execution: bool = False
    store_results: bool = True
    
    @classmethod
    def from_dict(cls, config_dict: Dict[str, Any]) -> 'BenchmarkConfig':
        """Create config from dictionary."""
        return cls(**{k: v for k, v in config_dict.items() if hasattr(cls, k)})
```

### Usage Examples

#### Running Benchmarks Programmatically
```python
from benchmarks import BenchmarkRunner, BenchmarkDatabase
from benchmarks.cli_benchmarks import CLIHelpBenchmark

# Initialize components
database = BenchmarkDatabase()
await database.initialize()

runner = BenchmarkRunner(database)

# Run specific benchmark
benchmark = CLIHelpBenchmark()
result = await runner.run_benchmark(benchmark)

print(f"Benchmark: {result.benchmark_name}")
print(f"Execution Time: {result.execution_time:.3f}s")
print(f"Memory Usage: {result.memory_usage:.2f}MB")
print(f"Success: {result.success}")
```

#### Analyzing Results
```python
from benchmarks.utils import BenchmarkAnalyzer

analyzer = BenchmarkAnalyzer(database)

# Analyze trends for specific benchmark
trends = await analyzer.analyze_trends("cli_help_performance", days=7)
print(f"Trend Direction: {trends['trend_direction']}")
print(f"Mean Execution Time: {trends['mean_execution_time']:.3f}s")

# Get performance summary
summary = await analyzer.get_performance_summary()
for benchmark_name, stats in summary.items():
    print(f"{benchmark_name}: {stats['mean_time']:.3f}s ± {stats['std_dev']:.3f}s")
```

### Testing

The framework includes comprehensive unit tests in [`tests/test_benchmarks_simple.py`](../../tests/test_benchmarks_simple.py):

- Core framework functionality tests
- Database operations tests
- CLI benchmark tests
- Configuration and utilities tests
- Error handling and edge case tests

Run tests with:
```bash
python -m pytest tests/test_benchmarks_simple.py -v
```

### Performance Targets

Current benchmark performance targets:

| Benchmark | Target Time | Current Performance |
|-----------|-------------|-------------------|
| CLI Help | < 1.0s | ~0.4s |
| CLI Version | < 0.5s | ~0.4s |
| CLI List Commands | < 1.0s | ~0.5s |
| CLI Error Handling | < 0.5s | ~0.4s |
| Benchmark Execution | < 5.0s | ~4.7s |

All benchmarks currently meet or exceed their performance targets with 100% success rate.

## Component-Specific Optimizations

### Vector Memory Manager Optimization

#### 1. Indexing Strategy
```python
class OptimizedVectorMemoryManager:
    def __init__(self, embedding_dim, similarity_metric, max_capacity):
        self.embedding_dim = embedding_dim
        self.similarity_metric = similarity_metric
        self.max_capacity = max_capacity
        
        # Use FAISS for fast similarity search
        if similarity_metric == SimilarityMetric.COSINE:
            self.index = faiss.IndexFlatIP(embedding_dim)
        elif similarity_metric == SimilarityMetric.EUCLIDEAN:
            self.index = faiss.IndexFlatL2(embedding_dim)
        
        # Metadata storage
        self.metadata_store = {}
        self.id_to_index = {}
        
    def store(self, vector, metadata):
        """Optimized vector storage with indexing."""
        # Normalize vector for cosine similarity
        if self.similarity_metric == SimilarityMetric.COSINE:
            vector = vector / np.linalg.norm(vector)
        
        # Add to index
        vector_id = metadata.get('id', str(uuid.uuid4()))
        index_position = self.index.ntotal
        
        self.index.add(vector.reshape(1, -1))
        self.metadata_store[vector_id] = metadata
        self.id_to_index[vector_id] = index_position
        
        return vector_id
    
    def similarity_search(self, query_vector, k=10):
        """Optimized similarity search using FAISS."""
        if self.similarity_metric == SimilarityMetric.COSINE:
            query_vector = query_vector / np.linalg.norm(query_vector)
        
        # Fast similarity search
        distances, indices = self.index.search(query_vector.reshape(1, -1), k)
        
        # Retrieve metadata
        results = []
        for i, (distance, index) in enumerate(zip(distances[0], indices[0])):
            if index != -1:  # Valid result
                # Find vector_id by index
                vector_id = next(
                    (vid for vid, idx in self.id_to_index.items() if idx == index),
                    None
                )
                if vector_id:
                    results.append({
                        'id': vector_id,
                        'similarity': float(distance),
                        'metadata': self.metadata_store[vector_id]
                    })
        
        return results
    
    def batch_store(self, vectors, metadata_list):
        """Optimized batch storage."""
        vectors_array = np.array(vectors)
        
        # Normalize for cosine similarity
        if self.similarity_metric == SimilarityMetric.COSINE:
            norms = np.linalg.norm(vectors_array, axis=1, keepdims=True)
            vectors_array = vectors_array / norms
        
        # Batch add to index
        start_index = self.index.ntotal
        self.index.add(vectors_array)
        
        # Store metadata
        vector_ids = []
        for i, metadata in enumerate(metadata_list):
            vector_id = metadata.get('id', str(uuid.uuid4()))
            self.metadata_store[vector_id] = metadata
            self.id_to_index[vector_id] = start_index + i
            vector_ids.append(vector_id)
        
        return vector_ids
```

#### 2. Memory Efficiency Optimization
```python
class MemoryEfficientVectorStore:
    def __init__(self, embedding_dim, compression_ratio=0.5):
        self.embedding_dim = embedding_dim
        self.compression_ratio = compression_ratio
        
        # Use quantized index for memory efficiency
        self.index = faiss.IndexIVFPQ(
            faiss.IndexFlatL2(embedding_dim),
            embedding_dim,
            nlist=100,  # Number of clusters
            m=8,        # Number of subquantizers
            nbits=8     # Bits per subquantizer
        )
        
        # Compressed metadata storage
        self.compressed_metadata = {}
    
    def compress_metadata(self, metadata):
        """Compress metadata to save memory."""
        import gzip
        import pickle
        
        serialized = pickle.dumps(metadata)
        compressed = gzip.compress(serialized)
        return compressed
    
    def decompress_metadata(self, compressed_metadata):
        """Decompress metadata."""
        import gzip
        import pickle
        
        decompressed = gzip.decompress(compressed_metadata)
        metadata = pickle.loads(decompressed)
        return metadata
```

### Delta Evaluation Optimization

#### 1. Vectorized Calculations
```python
class OptimizedDeltaEvaluator:
    def __init__(self, weights=None):
        self.weights = weights or {
            'performance': 0.3,
            'efficiency': 0.25,
            'stability': 0.25,
            'capability': 0.2
        }
        
        # Pre-compute weight vector for vectorized operations
        self.weight_vector = np.array([
            self.weights['performance'],
            self.weights['efficiency'],
            self.weights['stability'],
            self.weights['capability']
        ])
    
    def evaluate_batch(self, metrics_batch):
        """Optimized batch evaluation using vectorized operations."""
        # Convert metrics to numpy array
        metrics_array = np.array([
            [m.performance, m.efficiency, m.stability, m.capability]
            for m in metrics_batch
        ])
        
        # Vectorized weighted sum
        total_deltas = np.dot(metrics_array, self.weight_vector)
        
        # Vectorized confidence calculation
        confidences = np.mean(metrics_array, axis=1)
        
        # Create results
        results = []
        for i, (total_delta, confidence) in enumerate(zip(total_deltas, confidences)):
            results.append(DeltaResult(
                success=True,
                metrics=metrics_batch[i],
                total_delta=float(total_delta),
                confidence=float(confidence),
                should_adapt=total_delta > 0.15
            ))
        
        return results
    
    def evaluate_streaming(self, metrics_stream):
        """Optimized streaming evaluation for real-time processing."""
        # Use sliding window for efficiency
        window_size = 100
        metrics_buffer = []
        
        for metrics in metrics_stream:
            metrics_buffer.append(metrics)
            
            if len(metrics_buffer) >= window_size:
                # Process batch
                results = self.evaluate_batch(metrics_buffer)
                yield from results
                
                # Clear buffer
                metrics_buffer.clear()
        
        # Process remaining metrics
        if metrics_buffer:
            results = self.evaluate_batch(metrics_buffer)
            yield from results
```

#### 2. Caching and Memoization
```python
from functools import lru_cache
import hashlib

class CachedDeltaEvaluator:
    def __init__(self, cache_size=1000):
        self.cache_size = cache_size
        self._evaluation_cache = {}
    
    def _hash_metrics(self, metrics):
        """Create hash key for metrics."""
        metrics_str = f"{metrics.performance}_{metrics.efficiency}_{metrics.stability}_{metrics.capability}"
        return hashlib.md5(metrics_str.encode()).hexdigest()
    
    @lru_cache(maxsize=1000)
    def _cached_evaluate(self, metrics_hash, performance, efficiency, stability, capability):
        """Cached evaluation function."""
        # Perform actual evaluation
        total_delta = (
            performance * self.weights['performance'] +
            efficiency * self.weights['efficiency'] +
            stability * self.weights['stability'] +
            capability * self.weights['capability']
        )
        
        confidence = (performance + efficiency + stability + capability) / 4
        
        return total_delta, confidence
    
    def evaluate(self, metrics):
        """Evaluate with caching."""
        metrics_hash = self._hash_metrics(metrics)
        
        total_delta, confidence = self._cached_evaluate(
            metrics_hash,
            metrics.performance,
            metrics.efficiency,
            metrics.stability,
            metrics.capability
        )
        
        return DeltaResult(
            success=True,
            metrics=metrics,
            total_delta=total_delta,
            confidence=confidence,
            should_adapt=total_delta > 0.15
        )
```

### MCP Orchestration Optimization

#### 1. Connection Pooling
```python
class OptimizedMCPOrchestrator:
    def __init__(self, max_connections=100, connection_timeout=30):
        self.max_connections = max_connections
        self.connection_timeout = connection_timeout
        
        # Connection pool
        self.connection_pool = asyncio.Queue(maxsize=max_connections)
        self.active_connections = {}
        
        # Task queue with priority
        self.task_queue = asyncio.PriorityQueue()
        
        # Worker pool
        self.workers = []
        self.worker_count = min(10, max_connections // 2)
    
    async def initialize_connection_pool(self):
        """Initialize connection pool."""
        for _ in range(self.max_connections):
            connection = await self._create_connection()
            await self.connection_pool.put(connection)
    
    async def get_connection(self):
        """Get connection from pool."""
        try:
            connection = await asyncio.wait_for(
                self.connection_pool.get(),
                timeout=self.connection_timeout
            )
            return connection
        except asyncio.TimeoutError:
            raise ConnectionError("No available connections in pool")
    
    async def return_connection(self, connection):
        """Return connection to pool."""
        if connection.is_healthy():
            await self.connection_pool.put(connection)
        else:
            # Create new connection to replace unhealthy one
            new_connection = await self._create_connection()
            await self.connection_pool.put(new_connection)
    
    async def submit_task_optimized(self, task):
        """Submit task with optimized routing."""
        # Add to priority queue
        priority = task.priority if hasattr(task, 'priority') else 5
        await self.task_queue.put((priority, time.time(), task))
        
        # Return task ID immediately
        return TaskResult(
            task_id=task.id,
            status='queued',
            submitted_at=time.time()
        )
```

#### 2. Batch Processing
```python
class BatchMCPProcessor:
    def __init__(self, batch_size=50, batch_timeout=0.1):
        self.batch_size = batch_size
        self.batch_timeout = batch_timeout
        self.pending_tasks = []
        self.batch_timer = None
    
    async def submit_task(self, task):
        """Submit task for batch processing."""
        self.pending_tasks.append(task)
        
        # Start batch timer if not already running
        if self.batch_timer is None:
            self.batch_timer = asyncio.create_task(self._batch_timeout_handler())
        
        # Process batch if full
        if len(self.pending_tasks) >= self.batch_size:
            await self._process_batch()
        
        return task.id
    
    async def _process_batch(self):
        """Process current batch of tasks."""
        if not self.pending_tasks:
            return
        
        # Cancel timer
        if self.batch_timer:
            self.batch_timer.cancel()
            self.batch_timer = None
        
        # Get current batch
        batch = self.pending_tasks.copy()
        self.pending_tasks.clear()
        
        # Process batch concurrently
        tasks = [self._process_single_task(task) for task in batch]
        results = await asyncio.gather(*tasks, return_exceptions=True)
        
        return results
    
    async def _batch_timeout_handler(self):
        """Handle batch timeout."""
        try:
            await asyncio.sleep(self.batch_timeout)
            await self._process_batch()
        except asyncio.CancelledError:
            pass
```

## Memory Optimization

### Memory Usage Monitoring
```python
import psutil
import gc
from typing import Dict, Any

class MemoryMonitor:
    def __init__(self):
        self.baseline_memory = self._get_memory_usage()
        self.peak_memory = self.baseline_memory
        self.memory_history = []
    
    def _get_memory_usage(self) -> Dict[str, float]:
        """Get current memory usage."""
        process = psutil.Process()
        memory_info = process.memory_info()
        
        return {
            'rss': memory_info.rss / 1024 / 1024,  # MB
            'vms': memory_info.vms / 1024 / 1024,  # MB
            'percent': process.memory_percent(),
            'available': psutil.virtual_memory().available / 1024 / 1024  # MB
        }
    
    def check_memory(self) -> Dict[str, Any]:
        """Check current memory usage and detect issues."""
        current_memory = self._get_memory_usage()
        
        # Update peak memory
        if current_memory['rss'] > self.peak_memory['rss']:
            self.peak_memory = current_memory.copy()
        
        # Add to history
        self.memory_history.append({
            'timestamp': time.time(),
            'memory': current_memory.copy()
        })
        
        # Keep only recent history
        if len(self.memory_history) > 1000:
            self.memory_history = self.memory_history[-500:]
        
        # Detect memory issues
        memory_growth = current_memory['rss'] - self.baseline_memory['rss']
        memory_growth_rate = self._calculate_memory_growth_rate()
        
        return {
            'current': current_memory,
            'peak': self.peak_memory,
            'growth': memory_growth,
            'growth_rate': memory_growth_rate,
            'needs_gc': memory_growth > 100,  # >100MB growth
            'critical': current_memory['percent'] > 80
        }
    
    def _calculate_memory_growth_rate(self) -> float:
        """Calculate memory growth rate over recent history."""
        if len(self.memory_history) < 10:
            return 0.0
        
        recent_history = self.memory_history[-10:]
        start_memory = recent_history[0]['memory']['rss']
        end_memory = recent_history[-1]['memory']['rss']
        time_diff = recent_history[-1]['timestamp'] - recent_history[0]['timestamp']
        
        if time_diff > 0:
            return (end_memory - start_memory) / time_diff  # MB/second
        return 0.0
```

### Memory Pool Management
```python
class MemoryPool:
    def __init__(self, block_size=1024, pool_size=1000):
        self.block_size = block_size
        self.pool_size = pool_size
        self.available_blocks = []
        self.allocated_blocks = set()
        
        # Pre-allocate blocks
        self._initialize_pool()
    
    def _initialize_pool(self):
        """Initialize memory pool with pre-allocated blocks."""
        for _ in range(self.pool_size):
            block = bytearray(self.block_size)
            self.available_blocks.append(block)
    
    def allocate(self, size=None):
        """Allocate memory block from pool."""
        if size and size > self.block_size:
            # Allocate directly for large requests
            return bytearray(size)
        
        if self.available_blocks:
            block = self.available_blocks.pop()
            self.allocated_blocks.add(id(block))
            return block
        else:
            # Pool exhausted, allocate new block
            return bytearray(self.block_size)
    
    def deallocate(self, block):
        """Return memory block to pool."""
        block_id = id(block)
        if block_id in self.allocated_blocks:
            self.allocated_blocks.remove(block_id)
            
            # Clear block and return to pool
            block[:] = b'\x00' * len(block)
            if len(self.available_blocks) < self.pool_size:
                self.available_blocks.append(block)
    
    def get_stats(self):
        """Get memory pool statistics."""
        return {
            'total_blocks': self.pool_size,
            'available_blocks': len(self.available_blocks),
            'allocated_blocks': len(self.allocated_blocks),
            'utilization': len(self.allocated_blocks) / self.pool_size
        }
```

### Garbage Collection Optimization
```python
class OptimizedGarbageCollector:
    def __init__(self, memory_threshold=100, gc_interval=60):
        self.memory_threshold = memory_threshold  # MB
        self.gc_interval = gc_interval  # seconds
        self.last_gc_time = time.time()
        self.memory_monitor = MemoryMonitor()
    
    def should_run_gc(self) -> bool:
        """Determine if garbage collection should run."""
        memory_status = self.memory_monitor.check_memory()
        
        # Run GC if memory growth exceeds threshold
        if memory_status['growth'] > self.memory_threshold:
            return True
        
        # Run GC if memory growth rate is high
        if memory_status['growth_rate'] > 10:  # >10MB/second
            return True
        
        # Run GC at regular intervals
        if time.time() - self.last_gc_time > self.gc_interval:
            return True
        
        return False
    
    def run_optimized_gc(self):
        """Run optimized garbage collection."""
        if not self.should_run_gc():
            return
        
        # Record pre-GC memory
        pre_gc_memory = self.memory_monitor._get_memory_usage()
        
        # Run garbage collection
        collected = gc.collect()
        
        # Record post-GC memory
        post_gc_memory = self.memory_monitor._get_memory_usage()
        memory_freed = pre_gc_memory['rss'] - post_gc_memory['rss']
        
        self.last_gc_time = time.time()
        
        return {
            'objects_collected': collected,
            'memory_freed': memory_freed,
            'pre_gc_memory': pre_gc_memory,
            'post_gc_memory': post_gc_memory
        }
```

## Concurrency and Parallelization

### Async Task Management
```python
class AsyncTaskManager:
    def __init__(self, max_concurrent_tasks=100):
        self.max_concurrent_tasks = max_concurrent_tasks
        self.semaphore = asyncio.Semaphore(max_concurrent_tasks)
        self.active_tasks = set()
        self.completed_tasks = []
        self.failed_tasks = []
    
    async def submit_task(self, coro, task_id=None):
        """Submit async task with concurrency control."""
        task_id = task_id or str(uuid.uuid4())
        
        async def wrapped_task():
            async with self.semaphore:
                try:
                    result = await coro
                    self.completed_tasks.append({
                        'task_id': task_id,
                        'result': result,
                        'completed_at': time.time()
                    })
                    return result
                except Exception as e:
                    self.failed_tasks.append({
                        'task_id': task_id,
                        'error': str(e),
                        'failed_at': time.time()
                    })
                    raise
                finally:
                    self.active_tasks.discard(task_id)
        
        task = asyncio.create_task(wrapped_task())
        self.active_tasks.add(task_id)
        return task
    
    async def wait_for_completion(self, timeout=None):
        """Wait for all active tasks to complete."""
        if not self.active_tasks:
            return
        
        # Get all active task objects
        active_task_objects = [
            task for task in asyncio.all_tasks()
            if hasattr(task, 'get_name') and task.get_name() in self.active_tasks
        ]
        
        if active_task_objects:
            await asyncio.wait(active_task_objects, timeout=timeout)
    
    def get_stats(self):
        """Get task execution statistics."""
        return {
            'active_tasks': len(self.active_tasks),
            'completed_tasks': len(self.completed_tasks),
            'failed_tasks': len(self.failed_tasks),
            'success_rate': len(self.completed_tasks) / (len(self.completed_tasks) + len(self.failed_tasks)) if (len(self.completed_tasks) + len(self.failed_tasks)) > 0 else 0
        }
```

### Thread Pool Optimization
```python
from concurrent.futures import ThreadPoolExecutor, as_completed
import threading

class OptimizedThreadPool:
    def __init__(self, max_workers=None, thread_name_prefix='SAFLA'):
        self.max_workers = max_workers or min(32, (os.cpu_count() or 1) + 4)
        self.thread_name_prefix = thread_name_prefix
        
        # Create thread pool with optimized settings
        self.executor = ThreadPoolExecutor(
            max_workers=self.max_workers,
            thread_name_prefix=thread_name_prefix
        )
        
        # Thread-local storage for performance optimization
        self.thread_local = threading.local()
        
        # Performance tracking
        self.task_stats = {
            'submitted': 0,
            'completed': 0,
            'failed': 0,
            'total_execution_time': 0.0
        }
    
    def submit_cpu_bound_task(self, func, *args, **kwargs):
        """Submit CPU-bound task to thread pool."""
        def wrapped_func():
            start_time = time.time()
            try:
                # Initialize thread-local resources if needed
                if not hasattr(self.thread_local, 'initialized'):
                    self._initialize_thread_local()
                
                result = func(*args, **kwargs)
                
                # Update stats
                execution_time = time.time() - start_time
                self.task_stats['completed'] += 1
                self.task_stats['total_execution_time'] += execution_time
                
                return result
            except Exception as e:
                self.task_stats['failed'] += 1
                raise
        
        self.task_stats['submitted'] += 1
        return self.executor.submit(wrapped_func)
    
    def _initialize_thread_local(self):
        """Initialize thread-local resources."""
        self.thread_local.initialized = True
        # Add any thread-specific initialization here
        # e.g., database connections, caches, etc.
    
    def batch_execute(self, func, args_list, max_workers=None):
        """Execute function with multiple argument sets in parallel."""
        max_workers = max_workers or self.max_workers
        
        with ThreadPoolExecutor(max_workers=max_workers) as executor:
            # Submit all tasks
            future_to_args = {
                executor.submit(func, *args): args
                for args in args_list
            }
            
            # Collect results
            results = []
            for future in as_completed(future_to_args):
                args = future_to_args[future]
                try:
                    result = future.result()
                    results.append({'args': args, 'result': result, 'success': True})
                except Exception as e:
                    results.append({'args': args, 'error': str(e), 'success': False})
            
            return results
    
    def get_performance_stats(self):
        """Get thread pool performance statistics."""
        if self.task_stats['completed'] > 0:
            avg_execution_time = self.task_stats['total_execution_time'] / self.task_stats['completed']
        else:
            avg_execution_time = 0.0
        
        return {
            'max_workers': self.max_workers,
            'submitted_tasks': self.task_stats['submitted'],
            'completed_tasks': self.task_stats['completed'],
            'failed_tasks': self.task_stats['failed'],
            'success_rate': self.task_stats['completed'] / self.task_stats['submitted'] if self.task_stats['submitted'] > 0 else 0,
            'avg_execution_time': avg_execution_time
        }
```

## Caching Strategies

### Multi-Level Caching
```python
from functools import lru_cache
import redis
import pickle

class MultiLevelCache:
    def __init__(self, l1_size=1000, redis_host='localhost', redis_port=6379):
        # L1 Cache: In-memory LRU cache
        self.l1_cache = {}
        self.l1_size = l1_size
        self.l1_access_order = []
        
        # L2 Cache: Redis cache
        try:
            self.redis_client = redis.Redis(host=redis_host, port=redis_port, decode_responses=False)
            self.redis_available = True
        except:
            self.redis_client = None
            self.redis_available = False
        
        # Cache statistics
        self.stats = {
            'l1_hits': 0,
            'l2_hits': 0,
            'misses': 0,
            'evictions': 0
        }
    
    def get(self, key):
        """Get value from cache (L1 -> L2 -> miss)."""
        # Try L1 cache first
        if key in self.l1_cache:
            self._update_l1_access(key)
            self.stats['l1_hits'] += 1
            return self.l1_cache[key]
        
        # Try L2 cache (Redis)
        if self.redis_available:
            try:
                value = self.redis_client.get(key)
                if value is not None:
                    # Deserialize and promote to L1
                    deserialized_value = pickle.loads(value)
                    self._set_l1(key, deserialized_value)
                    self.stats['l2_hits'] += 1
                    return deserialized_value
            except Exception:
                pass
        
        # Cache miss
        self.stats['misses'] += 1
        return None
    
    def set(self, key, value, ttl=3600):
        """Set value in cache (both L1 and L2)."""
        # Set in L1 cache
        self._set_l1(key, value)
        
        # Set in L2 cache (Redis)
        if self.redis_available:
            try:
                serialized_value = pickle.dumps(value)
                self.redis_client.setex(key, ttl, serialized_value)
            except Exception:
                pass
    
    def _set_l1(self, key, value):
        """Set value in L1 cache with LRU eviction."""
        if key in self.l1_cache:
            self._update_l1_access(key)
        else:
            # Check if eviction is needed
            if len(self.l1_cache) >= self.l1_size:
                self._evict_l1()
            
            self.l1_access_order.append(key)
        
        self.l1_cache[key] = value
    
    def _update_l1_access(self, key):
        """Update access order for LRU."""
        self.l1_access_order.remove(key)
        self.l1_access_order.append(key)
    
    def _evict_l1(self):
        """Evict least recently used item from L1."""
        if self.l1_access_order:
            lru_key = self.l1_access_order.pop(0)
            del self.l1_cache[lru_key]
            self.stats['evictions'] += 1
    
    def get_stats(self):
        """Get cache performance statistics."""
        total_requests = self.stats['l1_hits'] + self.stats['l2_hits'] + self.stats['misses']
        
        if total_requests > 0:
            l1_hit_rate = self.stats['l1_hits'] / total_requests
            l2_hit_rate = self.stats['l2_hits'] / total_requests
            overall_hit_rate = (self.stats['l1_hits'] + self.stats['l2_hits']) / total_requests
        else:
            l1_hit_rate = l2_hit_rate = overall_hit_rate = 0.0
        
        return {
            'l1_hits': self.stats['l1_hits'],
            'l2_hits': self.stats['l2_hits'],
            'misses': self.stats['misses'],
            'evictions': self.stats['evictions'],
            'l1_hit_rate': l1_hit_rate,
            'l2_hit_rate': l2_hit_rate,
            'overall_hit_rate': overall_hit_rate,
            'l1_size': len(self.l1_cache),
            'redis_available': self.redis_available
        }
```

### Adaptive Caching
```python
class AdaptiveCache:
    def __init__(self, initial_size=1000, max_size=10000):
        self.cache = {}
        self.access_counts = {}
        self.access_times = {}
        self.current_size = initial_size
        self.max_size = max_size
        
        # Adaptive parameters
        self.hit_rate_threshold = 0.8
        self.resize_factor = 1.5
        self.monitoring_window = 1000  # requests
        self.request_count = 0
        self.hits = 0
    
    def get(self, key):
        """Get value with adaptive behavior tracking."""
        self.request_count += 1
        
        if key in self.cache:
            self.hits += 1
            self.access_counts[key] = self.access_counts.get(key, 0) + 1
            self.access_times[key] = time.time()
            return self.cache[key]
        
        # Check if cache should be resized
        if self.request_count % self.monitoring_window == 0:
            self._adapt_cache_size()
        
        return None
    
    def set(self, key, value):
        """Set value with adaptive eviction."""
        if len(self.cache) >= self.current_size:
            self._adaptive_evict()
        
        self.cache[key] = value
        self.access_counts[key] = 1
        self.access_times[key] = time.time()
    
    def _adapt_cache_size(self):
        """Adapt cache size based on hit rate."""
        hit_rate = self.hits / self.request_count
        
        if hit_rate < self.hit_rate_threshold and self.current_size < self.max_size:
            # Increase cache size
            new_size = min(int(self.current_size * self.resize_factor), self.max_size)
            self.current_size = new_size
        elif hit_rate > 0.95 and self.current_size > 100:
            # Decrease cache size if hit rate is very high
            new_size = max(int(self.current_size / self.resize_factor), 100)
            self.current_size = new_size
        
        # Reset counters
        self.hits = 0
        self.request_count = 0
    
    def _adaptive_evict(self):
        """Evict items based on access patterns."""
        if not self.cache:
            return
        
        # Calculate scores for eviction (lower score = more likely to evict)
        current_time = time.time()
        scores = {}
        
        for key in self.cache:
            access_count = self.access_counts.get(key, 1)
            last_access = self.access_times.get(key, current_time)
            time_since_access = current_time - last_access
            
            # Score based on frequency and recency
            score = access_count / (1 + time_since_access)
            scores[key] = score
        
        # Evict item with lowest score
        evict_key = min(scores.keys(), key=lambda k: scores[k])
        del self.cache[evict_key]
        del self.access_counts[evict_key]
        del self.access_times[evict_key]
```

## Performance Monitoring

### Real-time Performance Metrics
```python
class PerformanceMonitor:
    def __init__(self, window_size=1000):
        self.window_size = window_size
        self.metrics = {
            'latency': [],
            'throughput': [],
            'error_rate': [],
            'memory_usage': [],
            'cpu_usage': []
        }
        self.start_time = time.time()
        self.request_count = 0
        self.error_count = 0
    
    def record_request(self, latency, success=True):
        """Record request metrics."""
        self.request_count += 1
        if not success:
            self.error_count += 1
        
        # Record latency
        self.metrics['latency'].append(latency)
        if len(self.metrics['latency']) > self.window_size:
            self.metrics['latency'].pop(0)
        
        # Calculate throughput
        current_time = time.time()
        time_window = min(current_time - self.start_time, 60)  # 1-minute window
        if time_window > 0:
            throughput = self.request_count / time_window
            self.metrics['throughput'].append(throughput)
            if len(self.metrics['throughput']) > self.window_size:
                self.metrics['throughput'].pop(0)
        
        # Calculate error rate
        error_rate = self.error_count / self.request_count
        self.metrics['error_rate'].append(error_rate)
        if len(self.metrics['error_rate']) > self.window_size:
            self.metrics['error_rate'].pop(0)
        
        # Record system metrics
        self._record_system_metrics()
    
    def _record_system_metrics(self):
        """Record system-level metrics."""
        # Memory usage
        process = psutil.Process()
        memory_percent = process.memory_percent()
        self.metrics['memory_usage'].append(memory_percent)
        if len(self.metrics['memory_usage']) > self.window_size:
            self.metrics['memory_usage'].pop(0)
        
        # CPU usage
        cpu_percent = process.cpu_percent()
        self.metrics['cpu_usage'].append(cpu_percent)
        if len(self.metrics['cpu_usage']) > self.window_size:
            self.metrics['cpu_usage'].pop(0)
    
    def get_current_metrics(self):
        """Get current performance metrics."""
        if not self.metrics['latency']:
            return {}
        
        return {
            'avg_latency': statistics.mean(self.metrics['latency']),
            'p95_latency': statistics.quantiles(self.metrics['latency'], n=20)[18] if len(self.metrics['latency']) > 20 else max(self.metrics['latency']),
            'current_throughput': self.metrics['throughput'][-1] if self.metrics['throughput'] else 0,
            'current_error_rate': self.metrics['error_rate'][-1] if self.metrics['error_rate'] else 0,
            'memory_usage': self.metrics['memory_usage'][-1] if self.metrics['memory_usage'] else 0,
            'cpu_usage': self.metrics['cpu_usage'][-1] if self.metrics['cpu_usage'] else 0,
            'total_requests': self.request_count,
            'total_errors': self.error_count
        }
    
    def get_performance_trends(self):
        """Get performance trends over time."""
        if len(self.metrics['latency']) < 10:
            return {}
        
        # Calculate trends
        recent_latency = self.metrics['latency'][-10:]
        older_latency = self.metrics['latency'][-20:-10] if len(self.metrics['latency']) >= 20 else self.metrics['latency'][:-10]
        
        latency_trend = statistics.mean(recent_latency) - statistics.mean(older_latency) if older_latency else 0
        
        recent_throughput = self.metrics['throughput'][-10:] if len(self.metrics['throughput']) >= 10 else self.metrics['throughput']
        older_throughput = self.metrics['throughput'][-20:-10] if len(self.metrics['throughput']) >= 20 else []
        
        throughput_trend = statistics.mean(recent_throughput) - statistics.mean(older_throughput) if older_throughput else 0
        
        return {
            'latency_trend': latency_trend,
            'throughput_trend': throughput_trend,
            'performance_improving': latency_trend < 0 and throughput_trend > 0
        }
```

## Profiling and Analysis

### Performance Profiler
```python
import cProfile
import pstats
from functools import wraps

class PerformanceProfiler:
    def __init__(self, output_dir='profiles'):
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(exist_ok=True)
        self.profiles = {}
    
    def profile_function(self, func_name=None):
        """Decorator to profile function execution."""
        def decorator(func):
            name = func_name or func.__name__
            
            @wraps(func)
            def wrapper(*args, **kwargs):
                profiler = cProfile.Profile()
                profiler.enable()
                
                try:
                    result = func(*args, **kwargs)
                    return result
                finally:
                    profiler.disable()
                    
                    # Save profile
                    profile_file = self.output_dir / f"{name}_{int(time.time())}.prof"
                    profiler.dump_stats(str(profile_file))
                    
                    # Store in memory for analysis
                    stats = pstats.Stats(profiler)
                    self.profiles[name] = stats
            
            return wrapper
        return decorator
    
    def profile_block(self, block_name):
        """Context manager to profile code blocks."""
        return ProfileBlock(block_name, self)
    
    def analyze_profile(self, func_name, top_n=20):
        """Analyze profile for specific function."""
        if func_name not in self.profiles:
            return None
        
        stats = self.profiles[func_name]
        
        # Get top functions by cumulative time
        stats.sort_stats('cumulative')
        
        # Capture output
        import io
        output = io.StringIO()
        stats.print_stats(top_n, file=output)
        
        return {
            'total_calls': stats.total_calls,
            'total_time': stats.total_tt,
            'top_functions': output.getvalue()
        }
    
    def get_hotspots(self, func_name):
        """Identify performance hotspots."""
        if func_name not in self.profiles:
            return []
        
        stats = self.profiles[func_name]
        
        # Get functions with high cumulative time
        hotspots = []
        for func, (cc, nc, tt, ct, callers) in stats.stats.items():
            if ct > 0.01:  # Functions taking >10ms
                hotspots.append({
                    'function': func,
                    'calls': nc,
                    'total_time': tt,
                    'cumulative_time': ct,
                    'time_per_call': ct / nc if nc > 0 else 0
                })
        
        # Sort by cumulative time
        hotspots.sort(key=lambda x: x['cumulative_time'], reverse=True)
        
        return hotspots[:10]  # Top 10 hotspots

class ProfileBlock:
    def __init__(self, name, profiler):
        self.name = name
        self.profiler = profiler
        self.prof = None
    
    def __enter__(self):
        self.prof = cProfile.Profile()
        self.prof.enable()
        return self
    
    def __exit__(self, exc_type, exc_val, exc_tb):
        self.prof.disable()
        
        # Save profile
        profile_file = self.profiler.output_dir / f"{self.name}_{int(time.time())}.prof"
        self.prof.dump_stats(str(profile_file))
        
        # Store in memory
        stats = pstats.Stats(self.prof)
        self.profiler.profiles[self.name] = stats
```

### Memory Profiler
```python
import tracemalloc
from typing import List, Dict

class MemoryProfiler:
    def __init__(self):
        self.snapshots = []
        self.baseline_snapshot = None
        
    def start_tracing(self):
        """Start memory tracing."""
        tracemalloc.start()
        self.baseline_snapshot = tracemalloc.take_snapshot()
    
    def take_snapshot(self, name=None):
        """Take memory snapshot."""
        if not tracemalloc.is_tracing():
            raise RuntimeError("Memory tracing not started")
        
        snapshot = tracemalloc.take_snapshot()
        self.snapshots.append({
            'name': name or f'snapshot_{len(self.snapshots)}',
            'timestamp': time.time(),
            'snapshot': snapshot
        })
        
        return snapshot
    
    def analyze_memory_growth(self, snapshot1_name=None, snapshot2_name=None):
        """Analyze memory growth between snapshots."""
        if len(self.snapshots) < 2:
            return None
        
        # Get snapshots
        if snapshot1_name:
            snapshot1 = next((s['snapshot'] for s in self.snapshots if s['name'] == snapshot1_name), None)
        else:
            snapshot1 = self.snapshots[-2]['snapshot']
        
        if snapshot2_name:
            snapshot2 = next((s['snapshot'] for s in self.snapshots if s['name'] == snapshot2_name), None)
        else:
            snapshot2 = self.snapshots[-1]['snapshot']
        
        if not snapshot1 or not snapshot2:
            return None
        
        # Compare snapshots
        top_stats = snapshot2.compare_to(snapshot1, 'lineno')
        
        # Analyze top memory consumers
        memory_growth = []
        for stat in top_stats[:20]:  # Top 20
            memory_growth.append({
                'file': stat.traceback.format()[0] if stat.traceback.format() else 'unknown',
                'size_diff': stat.size_diff,
                'count_diff': stat.count_diff,
                'size': stat.size,
                'count': stat.count
            })
        
        return memory_growth
    
    def get_top_memory_consumers(self, snapshot_name=None):
        """Get top memory consumers from snapshot."""
        if not self.snapshots:
            return []
        
        if snapshot_name:
            snapshot_data = next((s for s in self.snapshots if s['name'] == snapshot_name), None)
        else:
            snapshot_data = self.snapshots[-1]
        
        if not snapshot_data:
            return []
        
        snapshot = snapshot_data['snapshot']
        top_stats = snapshot.statistics('lineno')
        
        consumers = []
        for stat in top_stats[:20]:  # Top 20
            consumers.append({
                'file': stat.traceback.format()[0] if stat.traceback.format() else 'unknown',
                'size': stat.size,
                'count': stat.count,
                'average_size': stat.size / stat.count if stat.count > 0 else 0
            })
        
        return consumers
    
    def stop_tracing(self):
        """Stop memory tracing."""
        tracemalloc.stop()
```

This comprehensive performance guide provides the foundation for optimizing SAFLA's performance across all components. Regular benchmarking and monitoring ensure that performance targets are met and maintained over time.