⚡ Performance Optimization - Python

"We don't bow to any king" - Performance Edition

TuskLang provides powerful performance optimization features to ensure your applications run at maximum efficiency.

🚀 Caching Strategies

Memory Caching

from tsk import TSK
from tsk.cache import MemoryCache
import time
Configure TSK with memory cache
tsk = TSK()
cache = MemoryCache(max_size=1000, ttl=300)  # 1000 items, 5 minutes TTL
tsk.set_cache(cache)
Performance-optimized configuration
perf_config = tsk.from_string("""
[performance]
Cache expensive operations
user_profile: @cache("1h", "user_profile", @request.user_id)
expensive_data: @cache("5m", "expensive_calculation")
api_response: @cache("30s", "api_call", @request.endpoint)
Lazy loading for heavy operations
lazy_data: @lazy("heavy_operation")
background_task: @async("background_processing")
expensive_calculation_fujsen = '''
def expensive_calculation(parameters):
    # Simulate expensive operation
    time.sleep(2)
    
    # Complex calculation
    result = sum(i * i for i in range(10000))
    
    return {
        'result': result,
        'timestamp': time.time(),
        'parameters': parameters
    }
'''
user_profile_fujsen = '''
def user_profile(user_id):
    # Get user data with joins
    user = query("""
        SELECT u.id, u.username, u.email, 
               COUNT(p.id) as post_count,
               COUNT(f.id) as follower_count
        FROM users u
        LEFT JOIN posts p ON u.id = p.user_id
        LEFT JOIN followers f ON u.id = f.followed_id
        WHERE u.id = ?
        GROUP BY u.id
    """, user_id)
    
    if not user:
        return None
    
    return {
        'id': user[0][0],
        'username': user[0][1],
        'email': user[0][2],
        'post_count': user[0][3],
        'follower_count': user[0][4]
    }
'''
""")
Test caching performance
def test_caching_performance():
    start_time = time.time()
    
    # First call - should be slow
    result1 = perf_config.execute_fujsen('performance', 'expensive_calculation', {'param': 'test'})
    first_call_time = time.time() - start_time
    
    # Second call - should be fast (cached)
    start_time = time.time()
    result2 = perf_config.execute_fujsen('performance', 'expensive_calculation', {'param': 'test'})
    second_call_time = time.time() - start_time
    
    print(f"First call: {first_call_time:.2f}s")
    print(f"Second call: {second_call_time:.2f}s")
    print(f"Speedup: {first_call_time / second_call_time:.1f}x")

Redis Caching

from tsk.cache import RedisCache
Redis cache configuration
redis_cache = RedisCache(
    host='localhost',
    port=6379,
    db=0,
    password='your_password',
    max_connections=20
)
tsk = TSK()
tsk.set_cache(redis_cache)
Redis-optimized configuration
redis_config = tsk.from_string("""
[redis_cache]
Distributed caching
session_data: @cache("30m", "session", @request.session_id)
user_preferences: @cache("1h", "preferences", @request.user_id)
api_results: @cache("5m", "api", @request.url_hash)
Cache invalidation
invalidate_user_cache_fujsen = '''
def invalidate_user_cache(user_id):
    # Invalidate all user-related caches
    cache_keys = [
        f"user_profile:{user_id}",
        f"user_preferences:{user_id}",
        f"user_posts:{user_id}"
    ]
    
    for key in cache_keys:
        cache.delete(key)
    
    return {'invalidated': True, 'keys': cache_keys}
'''
cache_metrics_fujsen = '''
def cache_metrics():
    # Get cache statistics
    stats = cache.get_stats()
    
    return {
        'hit_rate': stats.get('hit_rate', 0),
        'miss_rate': stats.get('miss_rate', 0),
        'total_requests': stats.get('total_requests', 0),
        'memory_usage': stats.get('memory_usage', 0)
    }
'''
""")

🚀 Database Optimization

Query Optimization

Database optimization configuration
db_optimization = TSK.from_string("""
[database_optimization]
Optimized queries with indexes
fast_user_query_fujsen = '''
def fast_user_query(user_id):
    # Use indexed query
    user = query("""
        SELECT u.id, u.username, u.email, u.created_at,
               COUNT(p.id) as post_count
        FROM users u
        LEFT JOIN posts p ON u.id = p.user_id AND p.published = 1
        WHERE u.id = ?
        GROUP BY u.id
    """, user_id)
    
    if not user:
        return None
    
    return {
        'id': user[0][0],
        'username': user[0][1],
        'email': user[0][2],
        'created_at': user[0][3],
        'post_count': user[0][4]
    }
'''
batch_insert_fujsen = '''
def batch_insert(records):
    # Batch insert for better performance
    if not records:
        return {'inserted': 0}
    
    # Prepare batch data
    placeholders = ','.join(['(?, ?, ?)' for _ in records])
    values = []
    
    for record in records:
        values.extend([record['name'], record['email'], record['status']])
    
    # Execute batch insert
    execute(f"""
        INSERT INTO users (name, email, status) 
        VALUES {placeholders}
    """, *values)
    
    return {'inserted': len(records)}
'''
optimized_search_fujsen = '''
def optimized_search(search_term, limit=20, offset=0):
    # Use full-text search if available
    if has_fulltext_search():
        results = query("""
            SELECT id, username, email, 
                   MATCH(username, email) AGAINST(?) as relevance
            FROM users 
            WHERE MATCH(username, email) AGAINST(?)
            ORDER BY relevance DESC
            LIMIT ? OFFSET ?
        """, search_term, search_term, limit, offset)
    else:
        # Fallback to LIKE with index
        results = query("""
            SELECT id, username, email
            FROM users 
            WHERE username LIKE ? OR email LIKE ?
            ORDER BY username
            LIMIT ? OFFSET ?
        """, f'%{search_term}%', f'%{search_term}%', limit, offset)
    
    return [{
        'id': row[0],
        'username': row[1],
        'email': row[2],
        'relevance': row[3] if len(row) > 3 else None
    } for row in results]
'''
connection_pool_fujsen = '''
def connection_pool_stats():
    # Get connection pool statistics
    pool = get_connection_pool()
    
    return {
        'total_connections': pool.get_total_connections(),
        'active_connections': pool.get_active_connections(),
        'idle_connections': pool.get_idle_connections(),
        'waiting_requests': pool.get_waiting_requests()
    }
'''
""")

Connection Pooling

from tsk.adapters import PostgreSQLAdapter
Optimized PostgreSQL adapter with connection pooling
postgres_pool = PostgreSQLAdapter(
    host='localhost',
    port=5432,
    database='myapp',
    user='postgres',
    password='secret',
    pool_size=20,
    max_overflow=30,
    pool_timeout=30,
    pool_recycle=3600
)
tsk = TSK()
tsk.set_database_adapter(postgres_pool)
Connection pool monitoring
pool_config = tsk.from_string("""
[connection_pool]
monitor_pool_fujsen = '''
def monitor_pool():
    pool_stats = connection_pool_stats()
    
    # Alert if pool is getting full
    if pool_stats['active_connections'] > pool_stats['total_connections'] * 0.8:
        log_warning(f"Connection pool at {pool_stats['active_connections']}/{pool_stats['total_connections']}")
    
    return pool_stats
'''
optimize_queries_fujsen = '''
def optimize_queries():
    # Analyze slow queries
    slow_queries = query("""
        SELECT query, mean_time, calls
        FROM pg_stat_statements
        ORDER BY mean_time DESC
        LIMIT 10
    """)
    
    optimizations = []
    
    for query_text, mean_time, calls in slow_queries:
        if mean_time > 100:  # Queries taking more than 100ms
            optimizations.append({
                'query': query_text[:100] + '...',
                'mean_time': mean_time,
                'calls': calls,
                'suggestion': 'Consider adding index or optimizing query'
            })
    
    return optimizations
'''
""")

🚀 Lazy Loading & Async Processing

Lazy Loading Implementation

Lazy loading configuration
lazy_config = TSK.from_string("""
[lazy_loading]
Lazy load expensive data
expensive_data: @lazy("load_expensive_data")
user_analytics: @lazy("calculate_user_analytics", @request.user_id)
report_data: @lazy("generate_report", @request.report_type)
load_expensive_data_fujsen = '''
def load_expensive_data():
    # Simulate expensive data loading
    time.sleep(3)
    
    # Load large dataset
    data = query("""
        SELECT * FROM large_dataset 
        WHERE created_at > date('now', '-30 days')
        ORDER BY created_at DESC
    """)
    
    return {
        'records': len(data),
        'data': data[:100],  # Limit for performance
        'loaded_at': time.time()
    }
'''
calculate_user_analytics_fujsen = '''
def calculate_user_analytics(user_id):
    # Complex analytics calculation
    user_stats = query("""
        SELECT 
            COUNT(p.id) as total_posts,
            COUNT(CASE WHEN p.published = 1 THEN 1 END) as published_posts,
            AVG(p.view_count) as avg_views,
            COUNT(f.id) as followers,
            COUNT(f2.id) as following
        FROM users u
        LEFT JOIN posts p ON u.id = p.user_id
        LEFT JOIN followers f ON u.id = f.followed_id
        LEFT JOIN followers f2 ON u.id = f2.follower_id
        WHERE u.id = ?
        GROUP BY u.id
    """, user_id)
    
    if not user_stats:
        return None
    
    return {
        'total_posts': user_stats[0][0],
        'published_posts': user_stats[0][1],
        'avg_views': user_stats[0][2],
        'followers': user_stats[0][3],
        'following': user_stats[0][4]
    }
'''
generate_report_fujsen = '''
def generate_report(report_type):
    if report_type == 'user_activity':
        return generate_user_activity_report()
    elif report_type == 'system_metrics':
        return generate_system_metrics_report()
    else:
        raise ValueError(f"Unknown report type: {report_type}")
def generate_user_activity_report():
    # Generate user activity report
    activity_data = query("""
        SELECT 
            DATE(created_at) as date,
            COUNT(*) as new_users,
            COUNT(CASE WHEN active = 1 THEN 1 END) as active_users
        FROM users
        WHERE created_at > date('now', '-30 days')
        GROUP BY DATE(created_at)
        ORDER BY date
    """)
    
    return {
        'type': 'user_activity',
        'data': activity_data,
        'generated_at': time.time()
    }
def generate_system_metrics_report():
    # Generate system metrics report
    metrics = {
        'total_users': query("SELECT COUNT(*) FROM users")[0][0],
        'total_posts': query("SELECT COUNT(*) FROM posts")[0][0],
        'active_sessions': query("SELECT COUNT(*) FROM sessions WHERE expires_at > datetime('now')")[0][0]
    }
    
    return {
        'type': 'system_metrics',
        'metrics': metrics,
        'generated_at': time.time()
    }
'''
""")

Async Processing

import asyncio
from tsk import TSK
Async processing configuration
async_config = TSK.from_string("""
[async_processing]
Async operations
background_task: @async("process_background_task")
email_send: @async("send_email")
data_processing: @async("process_large_dataset")
process_background_task_fujsen = '''
async def process_background_task(task_data):
    # Simulate async background processing
    await asyncio.sleep(2)
    
    # Process task
    result = await process_task_data(task_data)
    
    # Update task status
    await update_task_status(task_data['task_id'], 'completed', result)
    
    return result
async def process_task_data(data):
    # Async data processing
    processed_data = []
    
    for item in data['items']:
        # Simulate async processing
        await asyncio.sleep(0.1)
        processed_data.append({
            'id': item['id'],
            'processed': True,
            'timestamp': time.time()
        })
    
    return processed_data
async def update_task_status(task_id, status, result):
    # Update task status in database
    execute("""
        UPDATE background_tasks 
        SET status = ?, result = ?, completed_at = datetime('now')
        WHERE id = ?
    """, status, json.dumps(result), task_id)
'''
send_email_fujsen = '''
async def send_email(to_email, subject, body):
    # Async email sending
    email_data = {
        'to': to_email,
        'subject': subject,
        'body': body,
        'sent_at': time.time()
    }
    
    # Simulate email sending
    await asyncio.sleep(1)
    
    # Log email
    execute("""
        INSERT INTO email_log (to_email, subject, sent_at)
        VALUES (?, ?, datetime('now'))
    """, to_email, subject)
    
    return {'sent': True, 'email': email_data}
'''
process_large_dataset_fujsen = '''
async def process_large_dataset(dataset_id):
    # Async large dataset processing
    dataset = query("SELECT * FROM datasets WHERE id = ?", dataset_id)
    
    if not dataset:
        raise ValueError("Dataset not found")
    
    # Process in chunks
    chunk_size = 1000
    total_processed = 0
    
    for offset in range(0, dataset[0][2], chunk_size):  # dataset[0][2] = total_records
        chunk = await process_chunk(dataset_id, offset, chunk_size)
        total_processed += len(chunk)
        
        # Update progress
        await update_progress(dataset_id, total_processed, dataset[0][2])
    
    return {'processed': total_processed, 'dataset_id': dataset_id}
async def process_chunk(dataset_id, offset, chunk_size):
    # Process a chunk of data
    records = query("""
        SELECT * FROM dataset_records 
        WHERE dataset_id = ? 
        LIMIT ? OFFSET ?
    """, dataset_id, chunk_size, offset)
    
    processed = []
    for record in records:
        # Process each record
        processed_record = await process_record(record)
        processed.append(processed_record)
    
    return processed
async def process_record(record):
    # Process individual record
    await asyncio.sleep(0.01)  # Simulate processing time
    
    return {
        'id': record[0],
        'processed': True,
        'result': f"Processed record {record[0]}"
    }
'''
""")
Async execution
async def run_async_operations():
    # Execute async operations
    task_result = await async_config.execute_fujsen_async('async_processing', 'process_background_task', {
        'task_id': 123,
        'items': [{'id': i} for i in range(100)]
    })
    
    email_result = await async_config.execute_fujsen_async('async_processing', 'send_email',
        'user@example.com', 'Test Subject', 'Test Body')
    
    return task_result, email_result

🚀 Memory Optimization

Memory Management

Memory optimization configuration
memory_config = TSK.from_string("""
[memory_optimization]
Memory-efficient operations
stream_data: @stream("stream_large_dataset")
chunk_processing: @chunk("process_in_chunks")
memory_monitor: @monitor("memory_usage")
stream_large_dataset_fujsen = '''
def stream_large_dataset(dataset_id, chunk_size=1000):
    # Stream large dataset without loading everything into memory
    offset = 0
    
    while True:
        # Get chunk of data
        chunk = query("""
            SELECT id, name, data 
            FROM large_dataset 
            WHERE dataset_id = ? 
            ORDER BY id 
            LIMIT ? OFFSET ?
        """, dataset_id, chunk_size, offset)
        
        if not chunk:
            break
        
        # Process chunk
        for record in chunk:
            yield {
                'id': record[0],
                'name': record[1],
                'data': record[2]
            }
        
        offset += chunk_size
'''
process_in_chunks_fujsen = '''
def process_in_chunks(data, chunk_size=100):
    # Process data in chunks to manage memory
    results = []
    
    for i in range(0, len(data), chunk_size):
        chunk = data[i:i + chunk_size]
        
        # Process chunk
        chunk_result = process_chunk(chunk)
        results.extend(chunk_result)
        
        # Clear chunk from memory
        del chunk
        del chunk_result
    
    return results
def process_chunk(chunk):
    # Process a chunk of data
    return [{'id': item['id'], 'processed': True} for item in chunk]
'''
memory_usage_fujsen = '''
def memory_usage():
    import psutil
    import gc
    
    # Get current memory usage
    process = psutil.Process()
    memory_info = process.memory_info()
    
    # Force garbage collection
    gc.collect()
    
    return {
        'rss': memory_info.rss,  # Resident Set Size
        'vms': memory_info.vms,  # Virtual Memory Size
        'percent': process.memory_percent(),
        'available': psutil.virtual_memory().available
    }
'''
optimize_memory_fujsen = '''
def optimize_memory():
    import gc
    
    # Force garbage collection
    collected = gc.collect()
    
    # Clear cache if memory usage is high
    memory_stats = memory_usage()
    
    if memory_stats['percent'] > 80:
        # Clear some caches
        cache.clear_old_entries()
        
        # Force another garbage collection
        gc.collect()
    
    return {
        'garbage_collected': collected,
        'memory_after': memory_usage()
    }
'''
""")

🚀 Parallel Processing

Multi-threading and Multi-processing

import threading
import multiprocessing
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
Parallel processing configuration
parallel_config = TSK.from_string("""
[parallel_processing]
Parallel operations
parallel_task: @parallel("execute_parallel_task")
thread_pool: @thread_pool("process_with_threads")
process_pool: @process_pool("process_with_processes")
execute_parallel_task_fujsen = '''
def execute_parallel_task(tasks, max_workers=4):
    # Execute tasks in parallel using ThreadPoolExecutor
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        # Submit all tasks
        future_to_task = {
            executor.submit(process_single_task, task): task 
            for task in tasks
        }
        
        # Collect results
        results = []
        for future in concurrent.futures.as_completed(future_to_task):
            task = future_to_task[future]
            try:
                result = future.result()
                results.append(result)
            except Exception as exc:
                results.append({
                    'task': task,
                    'error': str(exc)
                })
    
    return results
def process_single_task(task):
    # Process individual task
    task_id = task['id']
    data = task['data']
    
    # Simulate processing
    time.sleep(0.1)
    
    return {
        'task_id': task_id,
        'result': f"Processed {len(data)} items",
        'processed_at': time.time()
    }
'''
process_with_threads_fujsen = '''
def process_with_threads(data_list, thread_count=4):
    # Process data using multiple threads
    results = []
    lock = threading.Lock()
    
    def process_chunk(chunk):
        chunk_results = []
        for item in chunk:
            # Process item
            result = process_item(item)
            chunk_results.append(result)
        
        # Thread-safe result collection
        with lock:
            results.extend(chunk_results)
    
    # Split data into chunks
    chunk_size = len(data_list) // thread_count
    chunks = [data_list[i:i + chunk_size] for i in range(0, len(data_list), chunk_size)]
    
    # Create and start threads
    threads = []
    for chunk in chunks:
        thread = threading.Thread(target=process_chunk, args=(chunk,))
        threads.append(thread)
        thread.start()
    
    # Wait for all threads to complete
    for thread in threads:
        thread.join()
    
    return results
def process_item(item):
    # Process individual item
    return {
        'id': item['id'],
        'processed': True,
        'thread': threading.current_thread().name
    }
'''
process_with_processes_fujsen = '''
def process_with_processes(data_list, process_count=4):
    # Process data using multiple processes
    with ProcessPoolExecutor(max_workers=process_count) as executor:
        # Split data into chunks
        chunk_size = len(data_list) // process_count
        chunks = [data_list[i:i + chunk_size] for i in range(0, len(data_list), chunk_size)]
        
        # Submit chunks to processes
        futures = [executor.submit(process_chunk_mp, chunk) for chunk in chunks]
        
        # Collect results
        results = []
        for future in concurrent.futures.as_completed(futures):
            try:
                chunk_results = future.result()
                results.extend(chunk_results)
            except Exception as exc:
                print(f"Process error: {exc}")
    
    return results
def process_chunk_mp(chunk):
    # Process chunk in separate process
    results = []
    for item in chunk:
        result = process_item_mp(item)
        results.append(result)
    return results
def process_item_mp(item):
    # Process item in separate process
    return {
        'id': item['id'],
        'processed': True,
        'process': multiprocessing.current_process().name
    }
'''
""")

🚀 Performance Monitoring

Performance Metrics

Performance monitoring configuration
monitoring_config = TSK.from_string("""
[performance_monitoring]
Performance metrics
response_time: @monitor("response_time")
memory_usage: @monitor("memory_usage")
database_performance: @monitor("database_performance")
response_time_fujsen = '''
def response_time(operation_name):
    start_time = time.time()
    
    def end_operation():
        end_time = time.time()
        duration = end_time - start_time
        
        # Log performance metric
        log_performance_metric(operation_name, duration)
        
        return duration
    
    return end_operation
def log_performance_metric(operation, duration):
    # Store performance metric
    execute("""
        INSERT INTO performance_metrics (operation, duration, timestamp)
        VALUES (?, ?, datetime('now'))
    """, operation, duration)
'''
memory_usage_fujsen = '''
def memory_usage():
    import psutil
    
    process = psutil.Process()
    memory_info = process.memory_info()
    
    return {
        'rss': memory_info.rss,
        'vms': memory_info.vms,
        'percent': process.memory_percent(),
        'cpu_percent': process.cpu_percent()
    }
'''
database_performance_fujsen = '''
def database_performance():
    # Get database performance metrics
    slow_queries = query("""
        SELECT query, mean_time, calls
        FROM pg_stat_statements
        ORDER BY mean_time DESC
        LIMIT 5
    """)
    
    connection_stats = connection_pool_stats()
    
    return {
        'slow_queries': slow_queries,
        'connection_stats': connection_stats,
        'timestamp': time.time()
    }
'''
performance_report_fujsen = '''
def performance_report():
    # Generate comprehensive performance report
    
    # Get recent metrics
    recent_metrics = query("""
        SELECT operation, AVG(duration) as avg_duration, COUNT(*) as calls
        FROM performance_metrics
        WHERE timestamp > datetime('now', '-1 hour')
        GROUP BY operation
        ORDER BY avg_duration DESC
    """)
    
    # Get memory usage
    memory_stats = memory_usage()
    
    # Get database performance
    db_stats = database_performance()
    
    return {
        'metrics': recent_metrics,
        'memory': memory_stats,
        'database': db_stats,
        'generated_at': time.time()
    }
'''
""")

🎯 Performance Best Practices

1. Caching Strategy

- Use appropriate cache TTL for different data types - Implement cache invalidation strategies - Monitor cache hit rates - Use distributed caching for scalability

2. Database Optimization

- Use indexes for frequently queried columns - Implement connection pooling - Use batch operations for bulk data - Monitor slow queries

3. Memory Management

- Process large datasets in chunks - Use generators for streaming data - Implement proper garbage collection - Monitor memory usage

4. Async Processing

- Use async operations for I/O-bound tasks - Implement proper error handling - Use connection pooling for async operations - Monitor async task performance

5. Parallel Processing

- Use threads for I/O-bound tasks - Use processes for CPU-bound tasks - Implement proper synchronization - Monitor resource usage

🚀 Next Steps

1. Implement caching for frequently accessed data 2. Optimize database queries with proper indexing 3. Add lazy loading for expensive operations 4. Implement async processing for I/O operations 5. Monitor performance with comprehensive metrics

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"We don't bow to any king" - TuskLang provides powerful performance optimization features to ensure your applications run at maximum efficiency. Implement caching, optimize queries, and monitor performance to build lightning-fast applications!