PyPatternGuard - Performance Pattern Detection Engine

A specialized code pattern detection system designed for performance engineers to identify code patterns that impact application performance. PyPatternGuard detects inefficient algorithms, resource-intensive operations, and performance anti-patterns before they reach production.

Features

Algorithm Complexity Analysis: Detects Big O notation complexity for functions
Memory Leak Detection: Identifies circular references and missing resource cleanup
Database Pattern Analysis: Finds N+1 queries and inefficient database operations
Concurrency Analysis: Detects race conditions and thread safety issues
Performance Regression Tracking: Compares performance metrics across code versions

Installation

From Source

# Clone the repository
git clone <repository-url>
cd pypatternguard

# Install in development mode
pip install -e .

# Or install with development dependencies
pip install -e ".[dev]"

Requirements

Python 3.8 or higher
No external dependencies for core functionality
pytest and pytest-json-report for running tests

Usage

Command Line Interface

Analyze a single file:

pypatternguard path/to/file.py

Analyze an entire directory:

pypatternguard path/to/project/

Analysis Options

# Run specific analysis type
pypatternguard path/to/code --analyze complexity
pypatternguard path/to/code --analyze memory
pypatternguard path/to/code --analyze database
pypatternguard path/to/code --analyze concurrency
pypatternguard path/to/code --analyze regression

# Filter by severity
pypatternguard path/to/code --severity high

# Output in JSON format
pypatternguard path/to/code --output json

# Update performance baseline
pypatternguard path/to/project --analyze regression --baseline

Python API

from pypatternguard import (
    ComplexityAnalyzer,
    MemoryLeakDetector,
    DatabasePatternAnalyzer,
    ConcurrencyAnalyzer,
    PerformanceRegressionTracker
)

# Analyze complexity
analyzer = ComplexityAnalyzer()
results = analyzer.analyze_file("example.py")
for result in results:
    print(f"{result.function_name}: {result.time_complexity}")

# Detect memory leaks
detector = MemoryLeakDetector()
issues = detector.analyze_file("example.py")
for issue in issues:
    print(f"{issue.leak_type}: {issue.description}")

# Track performance regressions
tracker = PerformanceRegressionTracker()
regressions = tracker.analyze_codebase("./src")
for regression in regressions:
    print(f"{regression.function_name}: {regression.description}")

Analysis Types

Complexity Analysis

Detects algorithmic complexity patterns:

O(1) - Constant time
O(log n) - Logarithmic
O(n) - Linear
O(n log n) - Linearithmic
O(n²) - Quadratic
O(n³) - Cubic
O(2ⁿ) - Exponential
O(n!) - Factorial

Memory Leak Detection

Identifies:

Circular references between objects
Missing cleanup in __del__ methods
Unbounded global cache growth
Generator exhaustion issues
Event listener leaks

Database Pattern Analysis

Detects:

N+1 query problems
Missing bulk operations
Inefficient pagination
Missing index usage
Excessive joins
Unbounded result sets

Supports Django, SQLAlchemy, Peewee, and generic SQL patterns.

Concurrency Analysis

Finds:

Race conditions in shared state access
Potential deadlocks from lock ordering
Missing synchronization
Blocking operations in async contexts
Thread-unsafe operations

Performance Regression Tracking

Monitors:

Complexity increases across versions
New inefficiencies introduced
Removed optimizations
Resource usage increases
Concurrency degradation

Examples

Example 1: Detecting O(n²) Complexity

# Input code
def find_duplicates(items):
    duplicates = []
    for i in range(len(items)):
        for j in range(i + 1, len(items)):
            if items[i] == items[j]:
                duplicates.append(items[i])
    return duplicates

# PyPatternGuard output:
# find_duplicates: O(n²) time complexity
# Recommendation: Consider using a set or dictionary for O(n) solution

Example 2: Finding N+1 Queries

# Input code
def get_user_orders(users):
    for user in users:
        orders = Order.objects.filter(user=user).all()
        print(f"{user.name}: {len(orders)}")

# PyPatternGuard output:
# N+1 Query Problem detected at line 3
# Recommendation: Use select_related() or prefetch_related()

Example 3: Detecting Race Conditions

# Input code
counter = 0

def increment():
    global counter
    counter += 1  # Race condition!

# PyPatternGuard output:
# Race Condition: Unprotected shared state mutation
# Recommendation: Use threading.Lock or atomic operations

Running Tests

Install test dependencies:

pip install pytest pytest-json-report

Run all tests:

pytest

Run with coverage:

pytest --cov=pypatternguard

Generate JSON test report:

pytest --json-report --json-report-file=pytest_results.json

Configuration

PyPatternGuard stores performance baselines in .performance_baseline.json in your project root. This file is used for regression tracking and should be committed to version control.

Contributing

Fork the repository
Create a feature branch
Add tests for new functionality
Ensure all tests pass
Submit a pull request

License

This project is licensed under the MIT License.

Support

For issues and feature requests, please use the GitHub issue tracker.

code-refactor/code_pattern_detector_performance_engineer

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