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Feed Processor Optimization System

The Feed Processing System includes an advanced performance optimization system that automatically tunes processing parameters based on system load and performance metrics. This guide explains how the optimization system works and how to configure it for your needs.

Table of Contents

Overview

The optimization system dynamically adjusts processing parameters to maintain optimal performance while preventing system overload. It monitors:

  • CPU usage and load
  • Memory utilization
  • I/O wait times
  • Processing throughput
  • Error rates

Based on these metrics, it automatically adjusts:

  • Batch processing sizes
  • Number of processing threads
  • Processing intervals

Key Features

1. Dynamic Batch Sizing

  • Automatically adjusts batch sizes based on system performance
  • Prevents memory overload during high-volume periods
  • Increases throughput during low-load periods
  • Configurable minimum and maximum bounds

2. Adaptive Thread Pool

  • Dynamically adjusts thread count based on:
    • CPU utilization
    • I/O wait times
    • Processing queue size
  • Prevents thread starvation and oversubscription
  • Optimizes for both CPU-bound and I/O-bound workloads

3. Performance Monitoring

  • Real-time metrics collection
  • Grafana dashboard integration
  • Historical performance tracking
  • Trend analysis for optimization decisions

Configuration

Basic Configuration

from feed_processor.config.processor_config import ProcessorConfig

config = ProcessorConfig(
    # Enable/disable dynamic optimization
    enable_dynamic_optimization=True,

    # Base processing parameters
    batch_size=100,
    min_batch_size=10,
    max_batch_size=500,

    # Thread pool configuration
    concurrent_processors=4,
    min_processors=2,
    max_processors=16,

    # Performance targets
    target_cpu_usage=70.0
)

Advanced Configuration

For more fine-grained control, you can adjust these additional parameters:

config = ProcessorConfig(
    # Processing timeouts
    processing_timeout=300,  # seconds

    # Polling intervals
    poll_interval=60,  # seconds

    # Metrics configuration
    metrics_port=8000
)

Monitoring

Available Metrics

  1. Batch Processing Metrics

    • feed_processor_batch_size: Current batch size
    • feed_processor_items_processed_total: Total processed items
    • feed_processor_processing_duration_seconds: Processing time histogram

  2. Thread Pool Metrics

    • feed_processor_thread_count: Active processing threads
    • feed_processor_thread_busy_ratio: Thread utilization

  3. System Load Metrics

    • CPU usage
    • Memory utilization
    • I/O wait times

Grafana Dashboard

The system includes a pre-configured Grafana dashboard at monitoring/dashboards/optimization.json with panels for:

  • Processing batch size trends
  • Thread count monitoring
  • Item processing rate
  • Average processing time

Best Practices

  1. Initial Configuration

    • Start with default settings
    • Monitor system performance for 24-48 hours
    • Adjust bounds based on observed patterns

  2. Resource Planning

    • Allow 30% CPU headroom for spikes
    • Monitor memory usage trends
    • Consider I/O patterns in thread configuration

  3. Monitoring

    • Regular review of performance metrics
    • Set up alerts for sustained high resource usage
    • Track error rates relative to optimization changes

Troubleshooting

Common Issues

  1. High Memory Usage

    • Decrease max_batch_size
    • Increase processing frequency
    • Check for memory leaks

  2. Processing Delays

    • Increase min_batch_size
    • Adjust target_cpu_usage
    • Check I/O bottlenecks

  3. Error Rate Spikes

    • Review error patterns
    • Adjust retry policies
    • Check external service health

Performance Tuning

  1. CPU-Bound Workloads

    config = ProcessorConfig(
    target_cpu_usage=60.0,
    max_processors=cpu_count,
    batch_size=100
)

  2. I/O-Bound Workloads

    config = ProcessorConfig(
    target_cpu_usage=80.0,
    max_processors=cpu_count * 2,
    batch_size=200
)

  3. Memory-Constrained Systems

    config = ProcessorConfig(
    max_batch_size=100,
    max_processors=4,
    poll_interval=120
)

Technical Details

Optimization Algorithm

The system uses a multi-factor optimization approach:

  1. Batch Size Calculation

    optimal_size = current_size * cpu_factor * error_factor * throughput_trend

  2. Thread Count Optimization

    optimal_threads = max(1, min(cpu_count * thread_factor, cpu_count * 2))

  3. Performance Metrics

    throughput = items_processed / processing_time
error_rate = items_failed / items_processed

Implementation Details

The optimization system is implemented in three main components:

  1. PerformanceOptimizer: Handles metric collection and parameter calculation
  2. ProcessorConfig: Stores configuration and bounds
  3. FeedProcessor: Integrates optimization into processing workflow

For more details, see the source code:

  • feed_processor/core/optimization.py
  • feed_processor/core/processor.py
  • feed_processor/config/processor_config.py