Performance Tuning

Optimize plugin performance for production workloads with profiling, benchmarking, connection pooling, memory management, and scaling techniques.

Overview

Performance tuning ensures plugin systems can handle high traffic loads efficiently. Key areas include CPU optimization, memory management, network efficiency, and scaling strategies for maximum throughput with minimal latency.

Key Performance Areas

• CPU Optimization: Efficient processing and multi-threading
• Memory Management: Minimize allocation overhead and garbage collection
• Network Efficiency: Connection pooling and protocol optimization
• I/O Performance: Async operations and buffer management
• Scaling Strategies: Horizontal and vertical scaling patterns

Quick Performance Setup

from pyvider.rpcplugin import plugin_server
from pyvider.rpcplugin.performance import PerformanceConfig
from provide.foundation import logger

async def optimized_server():
    """High-performance server configuration."""

    # Performance-optimized configuration
    perf_config = PerformanceConfig(
        max_connections=1000,
        connection_timeout=5.0,
        keepalive_enabled=True,
        buffer_size=64 * 1024,  # 64KB buffers
        worker_threads=4,       # Match CPU cores
        enable_compression=True
    )

    server = plugin_server(
        services=[OptimizedService()],
        performance_config=perf_config,
        enable_profiling=True,
        metrics_enabled=True
    )

    logger.info("🚀 Starting high-performance server")
    await server.serve()

CPU Optimization

1. Multi-Threading Configuration

Optimize thread usage for CPU-bound operations while maintaining async efficiency.

from pyvider.rpcplugin.performance import CPUConfig

cpu_config = CPUConfig(
    worker_threads=8,           # Match CPU cores
    async_io_threads=4,         # I/O worker threads
    enable_thread_affinity=True, # Pin threads to cores
    cpu_intensive_threshold=0.1  # 100ms threshold
)

# Server with CPU optimization
server = plugin_server(
    services=[CPUIntensiveService()],
    cpu_config=cpu_config
)

2. Async Performance Patterns

Efficient async patterns for high-concurrency scenarios.

import asyncio
from typing import List

class OptimizedService:
    def __init__(self):
        # Pre-create semaphores and pools
        self.processing_semaphore = asyncio.Semaphore(100)
        self.batch_size = 50

    async def process_batch_requests(self, requests: List):
        """Process multiple requests concurrently."""

        async def process_single(request):
            async with self.processing_semaphore:
                return await self.process_request(request)

        # Process in batches to control memory usage
        results = []
        for i in range(0, len(requests), self.batch_size):
            batch = requests[i:i + self.batch_size]
            batch_results = await asyncio.gather(
                *[process_single(req) for req in batch],
                return_exceptions=True
            )
            results.extend(batch_results)

        return results

3. CPU Profiling

Monitor and identify CPU bottlenecks in production.

from pyvider.rpcplugin.profiling import CPUProfiler
import cProfile

# Production-safe profiling
profiler = CPUProfiler(
    sampling_rate=0.01,     # 1% sampling
    profile_duration=60,    # Profile for 1 minute
    output_format="json"
)

async def profile_service_performance():
    async with profiler.profile_context("service_method"):
        result = await service.expensive_operation()
        return result

Memory Optimization

1. Memory Pool Management

Reuse objects and buffers to minimize garbage collection overhead.

from pyvider.rpcplugin.memory import MemoryPool, BufferPool

# Object pooling for frequent allocations
class OptimizedService:
    def __init__(self):
        self.request_pool = MemoryPool(
            create_func=lambda: RequestObject(),
            reset_func=lambda obj: obj.reset(),
            max_size=1000
        )

        self.buffer_pool = BufferPool(
            buffer_size=64 * 1024,  # 64KB buffers
            pool_size=100
        )

    async def process_request(self, data):
        # Reuse pooled objects
        request_obj = self.request_pool.acquire()
        buffer = self.buffer_pool.acquire()

        try:
            request_obj.parse(data)
            result = await self.process_with_buffer(request_obj, buffer)
            return result
        finally:
            # Return objects to pool
            self.request_pool.release(request_obj)
            self.buffer_pool.release(buffer)

2. Garbage Collection Tuning

Optimize garbage collection for server workloads.

import gc
from pyvider.rpcplugin.memory import GCOptimizer

# Configure GC for server workloads
gc_optimizer = GCOptimizer(
    generation_thresholds=(1000, 20, 20),  # More aggressive collection
    gc_frequency="adaptive",               # Adjust based on allocation rate
    disable_gc_during_requests=True       # Prevent GC during critical sections
)

# Apply optimizations
gc_optimizer.apply_server_optimizations()

# Manual GC control during idle periods
async def periodic_gc_cleanup():
    while True:
        await asyncio.sleep(30)  # Every 30 seconds during idle
        if server.is_idle():
            gc.collect(2)  # Full collection during idle

3. Memory Monitoring

Track memory usage patterns and detect leaks.

from pyvider.rpcplugin.monitoring import MemoryMonitor

memory_monitor = MemoryMonitor(
    check_interval=10.0,        # Check every 10 seconds
    warning_threshold=0.8,      # Warn at 80% memory usage
    critical_threshold=0.95,    # Alert at 95% memory usage
    track_object_counts=True    # Track object type counts
)

async def monitor_memory_health():
    async with memory_monitor.monitoring_context():
        stats = memory_monitor.get_current_stats()

        if stats.usage_percent > 0.8:
            logger.warning("High memory usage detected", extra={
                "usage_percent": stats.usage_percent,
                "largest_objects": stats.largest_object_types[:5]
            })

Network Optimization

1. Connection Pooling

Efficient connection reuse and management.

from pyvider.rpcplugin.network import ConnectionPool

# High-performance connection pool
conn_pool = ConnectionPool(
    max_connections=2000,       # High connection limit
    max_idle_connections=500,   # Keep connections alive
    connection_timeout=5.0,     # Quick timeout
    keepalive_time=60.0,        # 1 minute keepalive
    tcp_nodelay=True,          # Disable Nagle algorithm
    tcp_keepalive=True         # Enable TCP keepalive
)

# Client with connection pooling
async with plugin_client(
    connection_pool=conn_pool,
    enable_connection_reuse=True
) as client:
    # Connections are automatically pooled and reused
    results = await asyncio.gather(*[
        client.service.method(i) for i in range(1000)
    ])

2. Protocol Optimization

Optimize gRPC protocol settings for performance.

from pyvider.rpcplugin.protocol import ProtocolConfig

protocol_config = ProtocolConfig(
    # Message size limits
    max_send_message_length=100 * 1024 * 1024,  # 100MB
    max_receive_message_length=100 * 1024 * 1024,

    # Compression
    compression="gzip",                          # Enable compression
    compression_level=6,                         # Balanced compression

    # Flow control
    http2_window_size=16 * 1024 * 1024,         # 16MB window
    http2_max_frame_size=16 * 1024,             # 16KB frames

    # Performance tuning
    keepalive_time=30,                          # 30 second keepalive
    keepalive_timeout=5,                        # 5 second timeout
    max_connection_idle=60,                     # 1 minute idle timeout
    max_connection_age=120                      # 2 minute max age
)

3. Streaming Optimization

Efficient patterns for streaming data.

class StreamOptimizedService:
    def __init__(self):
        self.stream_buffer_size = 64 * 1024  # 64KB chunks
        self.max_concurrent_streams = 100

    async def stream_large_data(self, request_iterator):
        """Efficiently stream large datasets."""

        async def process_chunk(chunk):
            # Process chunk without blocking
            return await self.process_data_chunk(chunk)

        # Control concurrency to prevent memory exhaustion
        semaphore = asyncio.Semaphore(self.max_concurrent_streams)

        async for request in request_iterator:
            async with semaphore:
                chunk = await process_chunk(request.data)
                yield StreamResponse(
                    data=chunk,
                    chunk_size=len(chunk)
                )

Benchmarking and Profiling

1. Performance Benchmarks

Establish baseline performance metrics.

from pyvider.rpcplugin.benchmark import PerformanceBenchmark
import asyncio
import time

async def benchmark_plugin_performance():
    """Comprehensive performance benchmark."""

    benchmark = PerformanceBenchmark()

    # Throughput test
    throughput_results = await benchmark.measure_throughput(
        test_func=lambda: client.service.simple_method(),
        duration=60.0,        # 1 minute test
        concurrent_clients=50 # 50 concurrent clients
    )

    # Latency test
    latency_results = await benchmark.measure_latency(
        test_func=lambda: client.service.simple_method(),
        num_samples=10000     # 10,000 samples
    )

    # Memory usage test
    memory_results = await benchmark.measure_memory_usage(
        test_func=lambda: client.service.memory_intensive_method(),
        num_iterations=1000
    )

    # Generate performance report
    report = benchmark.generate_report({
        "throughput": throughput_results,
        "latency": latency_results, 
        "memory": memory_results
    })

    logger.info("Performance benchmark completed", extra=report)
    return report

2. Production Profiling

Safe profiling in production environments.

from pyvider.rpcplugin.profiling import ProductionProfiler

# Low-overhead profiling for production
profiler = ProductionProfiler(
    sampling_rate=0.001,    # 0.1% sampling
    profile_duration=300,   # 5 minute windows
    auto_upload=True,       # Upload to monitoring system
    retention_days=7        # Keep profiles for 7 days
)

# Profile critical paths
@profiler.profile_method("critical_service_method")
async def critical_service_method(self, request):
    # Method implementation
    result = await self.process_request(request)
    return result

Scaling Strategies

1. Horizontal Scaling

Scale across multiple processes and servers.

from pyvider.rpcplugin.scaling import ProcessPool, LoadBalancer

# Multi-process plugin server
process_pool = ProcessPool(
    num_processes=8,        # One per CPU core
    process_recycling=1000, # Restart after 1000 requests
    shared_memory=True,     # Use shared memory for data
    load_balancing="round_robin"
)

# Load balancer for multiple plugin instances
load_balancer = LoadBalancer(
    backend_servers=[
        "plugin-server-1:50051",
        "plugin-server-2:50051", 
        "plugin-server-3:50051"
    ],
    health_check_interval=5.0,
    load_balancing_algorithm="least_connections"
)

2. Vertical Scaling

Optimize resource usage on single machines.

from pyvider.rpcplugin.scaling import ResourceOptimizer

resource_optimizer = ResourceOptimizer(
    cpu_cores="auto",           # Use all available cores
    memory_limit="80%",         # Use 80% of available RAM
    disk_cache_size="20%",      # 20% of RAM for disk cache
    network_buffers="adaptive"  # Adaptive network buffer sizing
)

# Apply system-level optimizations
resource_optimizer.apply_optimizations()

# Server with vertical scaling optimizations
server = plugin_server(
    services=[ScalableService()],
    resource_optimizer=resource_optimizer,
    enable_auto_scaling=True
)

3. Auto-Scaling

Automatically adjust resources based on load.

from pyvider.rpcplugin.scaling import AutoScaler

auto_scaler = AutoScaler(
    min_instances=2,          # Minimum plugin instances
    max_instances=20,         # Maximum plugin instances
    target_cpu_utilization=70, # Scale at 70% CPU
    target_memory_utilization=80, # Scale at 80% memory
    scale_up_threshold=2,     # Scale up after 2 minutes
    scale_down_threshold=10   # Scale down after 10 minutes
)

# Enable auto-scaling
await auto_scaler.start_monitoring()

Configuration Tuning

Development vs Production

# Development configuration - optimized for debugging
dev_config = PerformanceConfig(
    max_connections=10,
    enable_profiling=True,
    debug_mode=True,
    metrics_detailed=True
)

# Production configuration - optimized for throughput
prod_config = PerformanceConfig(
    max_connections=5000,
    enable_profiling=False,    # Disable in production
    compression_enabled=True,
    connection_pooling=True,
    batch_processing=True,
    metrics_sampling=0.01      # 1% sampling
)

# Load balancer configuration
lb_config = PerformanceConfig(
    max_connections=10000,
    enable_session_affinity=True,
    health_check_enabled=True,
    circuit_breaker_enabled=True
)

Monitoring and Metrics

Performance Metrics

from pyvider.rpcplugin.metrics import PerformanceMetrics
from provide.foundation import logger

# Track key performance indicators
metrics = PerformanceMetrics()

async def track_performance():
    while True:
        current_stats = metrics.get_current_stats()

        # Log performance metrics
        logger.info("Performance metrics", extra={
            "requests_per_second": current_stats.requests_per_second,
            "average_latency_ms": current_stats.average_latency_ms,
            "memory_usage_mb": current_stats.memory_usage_mb,
            "cpu_utilization_percent": current_stats.cpu_utilization,
            "active_connections": current_stats.active_connections
        })

        # Alert on performance degradation
        if current_stats.requests_per_second < expected_rps * 0.8:
            logger.warning("Performance degradation detected")

        await asyncio.sleep(30)  # Report every 30 seconds

Health Checks

from pyvider.rpcplugin.health import PerformanceHealthCheck

health_check = PerformanceHealthCheck(
    max_response_time=100,      # 100ms max response
    max_cpu_usage=80,           # 80% max CPU
    max_memory_usage=85,        # 85% max memory
    min_throughput=1000         # 1000 RPS minimum
)

async def performance_health_monitor():
    health_status = await health_check.check_health()

    if not health_status.is_healthy:
        logger.error("Performance health check failed", extra={
            "failed_checks": health_status.failed_checks,
            "recommendations": health_status.recommendations
        })

Best Practices

1. Profile Before Optimizing: Measure to identify actual bottlenecks
2. Optimize Hot Paths: Focus on code paths that run most frequently
3. Use Connection Pooling: Reuse connections to reduce setup overhead
4. Control Concurrency: Limit concurrent operations to prevent resource exhaustion
5. Monitor Continuously: Track performance metrics in production
6. Test Under Load: Use realistic load testing for performance validation
7. Cache Strategically: Cache expensive computations and frequently accessed data
8. Batch Operations: Group operations to reduce per-request overhead

Troubleshooting Performance Issues

High Latency

# Identify latency sources
if average_latency > target_latency:
    logger.warning("High latency detected")

    # Check common causes
    latency_breakdown = profiler.get_latency_breakdown()
    for component, latency in latency_breakdown.items():
        if latency > threshold:
            logger.info(f"High latency in {component}: {latency}ms")

Memory Leaks

# Monitor memory growth patterns
previous_memory = current_memory = get_memory_usage()

async def detect_memory_leaks():
    global previous_memory, current_memory

    while True:
        current_memory = get_memory_usage()

        # Check for steady growth
        if current_memory > previous_memory * 1.1:  # 10% growth
            logger.warning("Potential memory leak detected", extra={
                "previous_mb": previous_memory,
                "current_mb": current_memory,
                "growth_percent": (current_memory/previous_memory - 1) * 100
            })

        previous_memory = current_memory
        await asyncio.sleep(300)  # Check every 5 minutes

CPU Bottlenecks

# Identify CPU-intensive operations
cpu_profiler = CPUProfiler()
hot_spots = cpu_profiler.get_hot_spots()

for function_name, cpu_time in hot_spots[:10]:  # Top 10 hot spots
    logger.info(f"CPU hot spot: {function_name} ({cpu_time:.2f}s)")

Next Steps

• Custom Protocols - Advanced protocol development
• Lifecycle Management - Plugin lifecycle optimization
• Middleware - Performance middleware patterns
• Server Configuration - Server-side performance tuning