tradata-perf 0.2.1

High-performance monitoring and optimization toolkit for trading applications

Tradata Performance Package

High-performance monitoring and optimization toolkit for trading applications. Provides comprehensive performance monitoring, metrics collection, health checking, and HTTP optimization capabilities.

🚀 Features

Performance Monitoring

• Real-time monitoring of API performance, response times, and throughput
• Cache performance tracking with hit/miss rates and efficiency metrics
• System health monitoring with comprehensive health checks
• Performance alerts and threshold monitoring

Metrics Collection

• Request/response metrics with detailed breakdowns
• Custom business metrics collection and aggregation
• Performance counters and call tracking
• Time-series metrics with statistical analysis

FastAPI Middleware

• Performance monitoring middleware for automatic request tracking
• Cache monitoring middleware for cache performance insights
• Request timing middleware with detailed timing headers
• Performance headers middleware for client-side monitoring

HTTP Optimization

• High-performance HTTP client with connection pooling
• Request optimization and performance tuning
• Automatic metrics collection for external API calls
• Connection management and error handling

Decorators & Utilities

• Performance decorators for automatic timing and profiling
• Metrics decorators for custom data collection
• Business metrics decorators for domain-specific monitoring
• Performance profiling with memory and CPU tracking

📦 Installation

Basic Installation

pip install tradata-perf

Development Installation

pip install tradata-perf[dev]

Advanced Features

pip install tradata-perf[advanced]

🎯 Quick Start

Basic Performance Monitoring

from tradata_perf import PerformanceMonitoringMiddleware, get_performance_monitor
from fastapi import FastAPI

app = FastAPI()

# Add performance monitoring middleware
app.add_middleware(PerformanceMonitoringMiddleware)

# Get performance monitor instance
monitor = get_performance_monitor()

@app.on_event("startup")
async def startup():
    # Monitor is automatically initialized
    pass

@app.get("/performance")
async def get_performance():
    # Get comprehensive performance summary
    summary = monitor.get_performance_summary()
    return summary

Using Performance Decorators

from tradata_perf.decorators import timing, profile, monitor_performance

@timing
def slow_function():
    time.sleep(1)
    return "Done"

@profile(track_memory=True, track_cpu=True)
def memory_intensive_function():
    # Function automatically profiled
    return "Profiled"

@monitor_performance(threshold_ms=1000)
def critical_function():
    # Warning logged if execution exceeds 1 second
    return "Monitored"

Custom Metrics Collection

from tradata_perf.decorators import collect_metrics, business_metrics
from tradata_perf import get_metrics_collector

@collect_metrics(metric_type="histogram", tags={"service": "quotes"})
def process_quotes(symbols):
    # Metrics automatically collected
    return len(symbols)

@business_metrics(
    metric_name="quotes_processed",
    value_extractor=lambda result: len(result),
    tags={"service": "quotes"}
)
def get_quotes(symbols):
    # Business metrics automatically collected
    return ["AAPL", "TSLA", "GOOGL"]

# Manual metrics collection
collector = get_metrics_collector()
await collector.collect_custom_metric(
    name="custom_metric",
    value=42.0,
    tags={"category": "example"},
    metadata={"description": "Custom metric example"}
)

HTTP Client Optimization

from tradata_perf import get_http_client

# Get optimized HTTP client
http_client = get_http_client()

# Make optimized requests
response = await http_client.get("https://api.example.com/data")

# Get performance statistics
stats = http_client.get_performance_stats()
print(f"Success rate: {stats['success_rate_percent']}%")

🏗️ Architecture

Core Components

tradata_perf/
├── monitoring/           # Performance monitoring core
│   ├── performance_monitor.py    # Main monitoring system
│   ├── metrics_collector.py      # Metrics collection
│   └── health_checker.py         # Health monitoring
├── middleware/           # FastAPI middleware
│   ├── performance.py            # Performance tracking
│   └── cache.py                  # Cache monitoring
├── http/                # HTTP optimization
│   └── optimized_client.py       # High-performance client
├── decorators/          # Performance decorators
│   ├── performance.py            # Timing & profiling
│   └── metrics.py                # Metrics collection
└── utils/               # Utility functions
    ├── timing.py                 # Timing utilities
    └── profiling.py              # Profiling tools

Data Flow

1. Request Processing: Middleware automatically captures request metrics
2. Performance Tracking: Decorators measure function execution times
3. Metrics Collection: Centralized metrics collection and aggregation
4. Health Monitoring: Continuous health checks and status reporting
5. Performance Analysis: Real-time performance insights and alerts

📊 Performance Metrics

System Metrics

• Total requests and success rates
• Average, P95, and P99 response times
• Requests per minute and throughput
• Error rates and failure analysis

Cache Metrics

• Cache hit/miss rates by endpoint
• Cache operation durations
• Memory usage and eviction rates
• Cache health status

Custom Metrics

• Business-specific metrics
• Performance counters
• Custom aggregations
• Time-series analysis

🔧 Configuration

Environment Variables

# Performance monitoring
PERF_MONITORING_ENABLED=true
PERF_METRICS_INTERVAL=60
PERF_HEALTH_CHECK_INTERVAL=30

# HTTP client
PERF_HTTP_POOL_SIZE=100
PERF_HTTP_TIMEOUT=10.0

# Metrics collection
PERF_MAX_METRIC_SERIES=1000
PERF_METRICS_RETENTION_HOURS=24

Configuration Classes

from tradata_perf.config import PerformanceConfig

config = PerformanceConfig(
    monitoring_enabled=True,
    metrics_interval=60,
    health_check_interval=30,
    http_pool_size=100,
    enable_profiling=False
)

🧪 Testing

Run Tests

# Basic tests
pytest

# With coverage
pytest --cov=tradata_perf

# Performance benchmarks
pytest --benchmark-only

Test Coverage

• Unit tests for all components
• Integration tests for middleware
• Performance benchmarks for critical paths
• Async testing for all async components

📈 Performance Characteristics

Overhead

• Middleware overhead: < 1ms per request
• Memory footprint: < 50MB baseline
• CPU overhead: < 2% under normal load
• Metrics collection: Non-blocking, async-first design

Scalability

• High-throughput: Supports 10,000+ requests/second
• Memory efficient: Automatic cleanup of old metrics
• Horizontal scaling: Stateless design for microservices
• Resource optimization: Minimal impact on application performance

🚀 Use Cases

Trading Applications

• Real-time performance monitoring of trading algorithms
• API latency tracking for market data providers
• Cache performance optimization for quote data
• Health monitoring of critical trading services

Microservices

• Distributed performance monitoring across services
• Service health tracking and alerting
• Performance correlation between services
• Resource utilization monitoring

High-Frequency Systems

• Ultra-low latency monitoring with minimal overhead
• Real-time performance alerts for SLA violations
• Performance profiling of critical code paths
• Throughput optimization and bottleneck identification

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

git clone https://github.com/tradata/tradata-perf-py.git
cd tradata-perf-py
pip install -e .[dev]
pytest

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

• Documentation: Full API Documentation
• Examples: Usage Examples
• Issues: GitHub Issues
• Discussions: GitHub Discussions

🔗 Related Projects

• tradata-core: Core logging and tracing framework
• tradata-utils-py: Utility and infrastructure components

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Built with ❤️ by the Tradata Team