# PerfScope
> **The Ultimate Python Performance Profiler** - Production-Ready Performance Monitoring, Memory Tracking & Call Tree Analysis
[](https://badge.fury.io/py/perfscope)
[](https://pypi.org/project/perfscope/)
[](https://pepy.tech/project/perfscope)
[](https://github.com/sattyamjain/perfscope)
[](https://opensource.org/licenses/MIT)
[](https://github.com/psf/black)
[](https://mypy.readthedocs.io/)
[](https://pytest.org/)
**PerfScope** is the most advanced **Python performance profiler** and **application performance monitoring (APM)** tool. Get **real-time insights** into function execution time, memory consumption, call hierarchies, and bottleneck detection with **zero configuration**. Perfect for **Django**, **Flask**, **FastAPI**, **async applications**, **data science**, and **machine learning** performance optimization.
## Why Choose PerfScope?
- **Production-Ready Logging** - Clean, structured logs for enterprise environments
- **Advanced Performance Analytics** - CPU time, wall time, memory usage, call frequency analysis
- **Async/Await Support** - Full compatibility with modern Python async frameworks
- **Interactive Call Trees** - Visual representation of function call hierarchies
- **Memory Leak Detection** - Track memory allocations and identify memory leaks
- **Export Reports** - HTML, JSON, CSV formats for integration with monitoring systems
- **Minimal Overhead** - Optimized for production use with configurable tracing levels
- **Smart Filtering** - Focus on bottlenecks with intelligent threshold-based logging
- **Zero Dependencies** - Pure Python core with optional extensions for enhanced features
## Key Features & Benefits
### Performance Monitoring
- **Function Call Tracing** - Automatic detection of all nested function calls with depth control
- **Execution Time Analysis** - Precise wall-clock and CPU time measurements down to microseconds
- **Memory Usage Tracking** - Real-time memory allocation monitoring and peak usage detection
- **Call Frequency Analysis** - Identify hot paths and frequently called functions
- **Bottleneck Detection** - Automatic identification of performance bottlenecks with configurable thresholds
### Developer Experience
- **Single Decorator Setup** - Add `@profile()` to any function for instant profiling
- **Production-Ready Logs** - Clean, structured logging format with [PerfScope] identifiers
- **Zero Configuration** - Works out-of-the-box with sensible defaults
- **IDE Integration** - Type hints and IntelliSense support for all APIs
- **Framework Compatibility** - Works with Django, Flask, FastAPI, Celery, and all Python frameworks
### Advanced Analytics
- **Call Tree Visualization** - Complete execution hierarchy with parent-child relationships
- **Memory Leak Detection** - Track memory allocations and garbage collection patterns
- **Async/Concurrent Profiling** - Full support for asyncio, threading, and multiprocessing
- **Report Generation** - Export detailed reports in HTML, JSON, and CSV formats
- **Statistical Analysis** - Min/max/average execution times, call distributions, and trends
### Enterprise Ready
- **Configurable Logging Levels** - From debug tracing to production summaries
- **Module Filtering** - Include/exclude specific modules or packages
- **Depth Control** - Limit tracing depth for performance optimization
- **Threshold-Based Reporting** - Only log functions exceeding specified execution times
- **Thread Safety** - Full support for multi-threaded applications
## Installation
### Quick Install
```bash
# Standard installation - pure Python, zero dependencies
pip install perfscope
# Full installation with enhanced memory tracking
pip install perfscope[full]
# Development installation with all tools
pip install perfscope[dev]
```
### Requirements
- **Python 3.8+** (Python 3.9, 3.10, 3.11, 3.12 supported)
- **Zero dependencies** for core functionality
- **Optional**: psutil for enhanced system memory tracking
## Quick Start Guide
### 30-Second Setup
Transform any function into a performance monitoring powerhouse with a single decorator:
```python
from perfscope import profile
# Basic profiling - just add the decorator!
@profile()
def calculate_fibonacci(n):
"""Example function with recursive calls for performance testing"""
if n <= 1:
return n
return calculate_fibonacci(n-1) + calculate_fibonacci(n-2)
# Run your function normally
result = calculate_fibonacci(10)
# PerfScope automatically logs:
# 2024-01-15 10:30:45.123 | INFO | [PerfScope] PROFILED[calculate_fibonacci] time=5.23ms cpu=5.20ms efficiency=99.4% nested=177
# 2024-01-15 10:30:45.125 | INFO | [PerfScope] SESSION SUMMARY duration=0.005s cpu=0.005s efficiency=99.4% calls=177 functions=1
```
### Export Detailed Reports
```python
# Generate comprehensive HTML report
@profile(report_path="performance_analysis.html")
def process_large_dataset(data):
"""Process large datasets with memory tracking"""
cleaned_data = clean_data(data) # Tracked
features = extract_features(cleaned_data) # Tracked
model_result = train_model(features) # Tracked
return model_result
# Creates detailed HTML report with:
# - Interactive call tree visualization
# - Memory usage graphs
# - Performance bottleneck analysis
# - Function timing distributions
```
### Async/Await & Concurrent Code
```python
import asyncio
from perfscope import profile
# Profile async functions with memory tracking
@profile(trace_memory=True, detailed_tracing=True)
async def fetch_multiple_apis(urls):
"""Fetch data from multiple APIs concurrently"""
async with aiohttp.ClientSession() as session:
tasks = [fetch_single_url(session, url) for url in urls]
results = await asyncio.gather(*tasks, return_exceptions=True)
return results
# Automatically tracks:
# - Async function execution times
# - Memory allocations during concurrent operations
# - Call tree of async tasks
# - Exception handling performance
```
## Real-World Examples
### Enterprise Web Application
Profile a complete web request lifecycle in production:
```python
from perfscope import profile
from fastapi import FastAPI, Request
import asyncio
app = FastAPI()
@profile(
trace_memory=True, # Track memory allocations
max_depth=50, # Deep call tree analysis
min_duration=0.001, # Only log functions > 1ms
report_path="api_performance.html", # Detailed HTML report
exclude_modules={"logging", "urllib3"} # Filter out noise
)
@app.post("/api/process-order")
async def process_order_endpoint(request: OrderRequest):
"""Complete order processing with performance monitoring"""
# Input validation (tracked)
validation_result = await validate_order_request(request)
if not validation_result.is_valid:
return error_response(validation_result.errors)
# Parallel data fetching (tracked)
customer_data, inventory_data, pricing_data = await asyncio.gather(
fetch_customer_profile(request.customer_id), # Database query
check_inventory_availability(request.items), # Redis cache + DB
calculate_dynamic_pricing(request.items) # External API
)
# Business logic processing (tracked)
order_calculation = await process_order_logic(
customer_data, inventory_data, pricing_data
)
# Payment processing (tracked)
payment_result = await process_payment(
order_calculation.total_amount,
customer_data.payment_method
)
# Database transaction (tracked)
final_order = await save_order_transaction(
order_calculation, payment_result
)
return success_response(final_order)
# Production logs show:
# [PerfScope] PROFILED[process_order_endpoint] time=245.67ms cpu=89.23ms efficiency=36.3% mem=+2.4MB nested=23
# [PerfScope] BOTTLENECK[fetch_customer_profile] 34.2% runtime (84.1ms)
# [PerfScope] SESSION SUMMARY duration=0.246s cpu=0.089s efficiency=36.3% calls=47 functions=18
```
### Machine Learning Pipeline
```python
@profile(
trace_memory=True,
report_path="ml_pipeline_performance.html",
detailed_tracing=False # Production-clean logs only
)
def train_recommendation_model(user_data, item_features):
"""Complete ML pipeline with performance tracking"""
# Data preprocessing (memory intensive)
processed_features = preprocess_features(user_data, item_features)
# Feature engineering (CPU intensive)
engineered_features = create_interaction_features(processed_features)
# Model training (GPU/CPU intensive)
model = train_collaborative_filtering_model(engineered_features)
# Model validation (I/O intensive)
validation_metrics = validate_model_performance(model, test_data)
# Model persistence (I/O intensive)
save_trained_model(model, "recommendation_model_v2.pkl")
return ModelTrainingResult(model, validation_metrics)
# Identifies bottlenecks in your ML pipeline:
# - Which preprocessing steps are slowest
# - Memory usage during feature engineering
# - Training time breakdown by algorithm phase
# - I/O performance for model persistence
```
### Django Web Application
```python
# views.py
from django.http import JsonResponse
from perfscope import profile
@profile(trace_memory=True)
def api_user_dashboard(request):
"""Django view with comprehensive profiling"""
user = request.user
# Database queries (tracked)
user_profile = UserProfile.objects.select_related('company').get(user=user)
recent_orders = Order.objects.filter(user=user)[:10]
analytics_data = generate_user_analytics(user.id)
# Template rendering (tracked)
context = {
'user': user_profile,
'orders': recent_orders,
'analytics': analytics_data
}
return JsonResponse(context)
# Automatically tracks:
# - Django ORM query performance
# - Template rendering times
# - Memory usage per request
# - Database connection overhead
```
## Understanding Performance Reports
PerfScope generates comprehensive reports with multiple visualization formats:
### Interactive Call Tree (HTML Report)
```
process_order_endpoint (245.67ms, self: 12.4ms) [Memory: +2.4MB]
├─ validate_order_request (8.3ms, self: 8.3ms) [Memory: +0.1MB]
├─ fetch_customer_profile (84.1ms, self: 3.2ms) [Memory: +0.8MB] ⚠️ BOTTLENECK
│ ├─ database_connection_get (15.6ms, self: 15.6ms) [Memory: +0.2MB]
│ ├─ execute_customer_query (62.1ms, self: 62.1ms) [Memory: +0.5MB]
│ └─ deserialize_customer_data (3.2ms, self: 3.2ms) [Memory: +0.1MB]
├─ check_inventory_availability (45.2ms, self: 2.1ms) [Memory: +0.3MB]
│ ├─ redis_cache_lookup (18.7ms, self: 18.7ms) [Memory: +0.1MB]
│ └─ inventory_database_query (24.4ms, self: 24.4ms) [Memory: +0.2MB]
├─ calculate_dynamic_pricing (38.7ms, self: 1.2ms) [Memory: +0.2MB]
│ └─ external_pricing_api_call (37.5ms, self: 37.5ms) [Memory: +0.2MB]
├─ process_order_logic (15.3ms, self: 10.2ms) [Memory: +0.4MB]
│ └─ calculate_totals_and_taxes (5.1ms, self: 5.1ms) [Memory: +0.1MB]
└─ save_order_transaction (28.9ms, self: 28.9ms) [Memory: +0.6MB]
```
### Performance Dashboard
**Execution Summary**
- **Total Duration**: 245.67ms (wall-clock time)
- **CPU Time**: 89.23ms (36.3% CPU efficiency)
- **Total Function Calls**: 47
- **Unique Functions**: 18
- **Call Tree Depth**: 4 levels
**Memory Analysis**
- **Peak Memory**: 125.4MB
- **Memory Delta**: +2.4MB
- **Memory Efficiency**: 98.1%
- **GC Collections**: 2 (gen0), 1 (gen1), 0 (gen2)
**Performance Bottlenecks** (>15% runtime)
| Function | Runtime % | Time (ms) | Calls | Avg Time | Memory Impact |
|----------|-----------|-----------|-------|----------|---------------|
| `fetch_customer_profile` | 34.2% | 84.1ms | 1 | 84.1ms | +0.8MB |
| `external_pricing_api_call` | 15.3% | 37.5ms | 1 | 37.5ms | +0.2MB |
| `execute_customer_query` | 25.3% | 62.1ms | 1 | 62.1ms | +0.5MB |
**Hot Paths** (most frequently called)
| Function | Calls | Total Time | Avg per Call | Impact Score |
|----------|-------|------------|--------------|-------------|
| `validation_helper` | 12 | 24.6ms | 2.1ms | High |
| `format_currency` | 8 | 1.2ms | 0.15ms | Low |
| `log_performance_metric` | 47 | 5.8ms | 0.12ms | Medium |
## Advanced Configuration
### Complete Configuration Reference
```python
@profile(
# === TRACING CONTROL ===
enabled=True, # Master switch for profiling
trace_calls=True, # Function call hierarchy tracking
trace_memory=True, # Memory allocation monitoring
trace_lines=False, # Line-by-line execution (high overhead)
# === PERFORMANCE OPTIMIZATION ===
max_depth=100, # Maximum call stack depth
min_duration=0.001, # Only log functions > 1ms (0.001s)
# === FILTERING & FOCUS ===
include_modules={"myapp", "business_logic"}, # Whitelist modules
exclude_modules={"logging", "urllib3"}, # Blacklist noisy modules
include_builtins=False, # Skip Python built-in functions
# === LOGGING CONFIGURATION ===
log_calls=True, # Enable function call logging
log_args=True, # Log argument sizes
log_level="INFO", # DEBUG|INFO|WARNING|ERROR
detailed_tracing=False, # Verbose debug logs vs clean production logs
# === REPORT GENERATION ===
report_path="performance_analysis.html", # Auto-save detailed report
auto_report=True, # Generate report after execution
)
def your_function():
pass
```
### Production-Ready Configurations
#### High-Performance Production Setup
```python
# Optimized for production with minimal overhead
@profile(
trace_memory=False, # Disable memory tracking for speed
max_depth=10, # Limit depth for performance
min_duration=0.010, # Only log slow functions (>10ms)
exclude_modules={"logging", "urllib3", "requests"},
detailed_tracing=False, # Clean logs only
log_level="WARNING", # Only bottlenecks and errors
)
```
#### Development & Debugging Setup
```python
# Maximum visibility for debugging
@profile(
trace_memory=True, # Full memory tracking
trace_lines=True, # Line-level tracing
max_depth=200, # Deep analysis
min_duration=0.0, # Log everything
detailed_tracing=True, # Verbose debug logs
log_level="DEBUG", # All profiling events
report_path="debug_analysis.html"
)
```
#### Memory Leak Investigation
```python
# Specialized for memory leak detection
@profile(
trace_memory=True,
trace_calls=True,
log_args=True, # Track argument memory usage
min_duration=0.0,
report_path="memory_leak_analysis.html"
)
```
## Advanced Use Cases & Integrations
### Manual Profiling API
```python
from perfscope import Profiler, ProfileConfig
# Create custom configuration
config = ProfileConfig(
trace_memory=True,
trace_calls=True,
max_depth=100,
min_duration=0.005, # 5ms threshold
exclude_modules={"logging", "urllib"},
detailed_tracing=False
)
# Manual profiler control
profiler = Profiler(config)
with profiler:
# Profile any code block
data = load_large_dataset("data.csv") # Tracked
processed = preprocess_data(data) # Tracked
model = train_model(processed) # Tracked
results = evaluate_model(model) # Tracked
# Generate comprehensive report
report = profiler.get_report()
report.save("ml_pipeline_analysis.html")
# Access raw performance data
print(f"Total execution time: {report.total_duration:.3f}s")
print(f"Memory peak: {report.memory_peak / (1024*1024):.1f}MB")
print(f"Function calls: {report.total_calls}")
```
### Programmatic Report Analysis
```python
@profile(trace_memory=True)
def complex_data_processing():
"""Example function with multiple processing stages"""
raw_data = load_dataset() # I/O intensive
clean_data = clean_dataset(raw_data) # CPU intensive
features = extract_features(clean_data) # Memory intensive
return train_model(features) # CPU + Memory intensive
# Execute profiled function
result = complex_data_processing()
# Access detailed performance data
report = complex_data_processing.profile_report
# === EXECUTION SUMMARY ===
print(f"🕐 Total execution time: {report.total_duration:.3f}s")
print(f"💻 CPU time: {report.total_cpu_time:.3f}s")
print(f"⚡ CPU efficiency: {report.cpu_efficiency:.1%}")
print(f"📞 Total function calls: {report.total_calls}")
print(f"🎯 Unique functions: {report.unique_functions}")
# === MEMORY ANALYSIS ===
if report.memory_start and report.memory_end:
memory_delta = (report.memory_end - report.memory_start) / (1024 * 1024)
peak_memory = report.memory_peak / (1024 * 1024) if report.memory_peak else 0
print(f"💾 Memory usage: {memory_delta:+.1f}MB (peak: {peak_memory:.1f}MB)")
# === PERFORMANCE BREAKDOWN ===
print("\n📈 Function Performance Analysis:")
for func_name, stats in report.statistics.items():
avg_time = stats['total_duration'] / stats['calls'] * 1000 # ms
memory_mb = stats['memory_delta'] / (1024 * 1024)
print(f" {func_name}:")
print(f" Calls: {stats['calls']}")
print(f" Total: {stats['total_duration']:.3f}s")
print(f" Average: {avg_time:.2f}ms")
print(f" Memory: {memory_mb:+.2f}MB")
# === IDENTIFY BOTTLENECKS ===
bottlenecks = [
(name, stats) for name, stats in report.statistics.items()
if stats['total_duration'] / report.total_duration > 0.10 # >10% of total time
]
if bottlenecks:
print("\n🐌 Performance Bottlenecks (>10% runtime):")
for func_name, stats in sorted(bottlenecks, key=lambda x: x[1]['total_duration'], reverse=True):
percentage = (stats['total_duration'] / report.total_duration) * 100
print(f" {func_name}: {percentage:.1f}% ({stats['total_duration']:.3f}s)")
# === EXPORT OPTIONS ===
report.save("detailed_analysis.html") # Interactive HTML
with open("performance_data.json", "w") as f:
f.write(report.to_json(indent=2)) # Raw JSON data
```
### Memory Leak Detection & Analysis
```python
import gc
from perfscope import profile
@profile(
trace_memory=True,
log_args=True, # Track argument memory usage
min_duration=0.0, # Log all functions for memory analysis
report_path="memory_leak_analysis.html"
)
def detect_memory_leaks():
"""Function that demonstrates memory leak detection"""
# Potential memory leak: growing list never cleared
global_cache = []
def process_batch(batch_size=10000):
"""Process data batch - potential leak here"""
batch_data = []
for i in range(batch_size):
# Creating objects that may not be properly cleaned
item = {
"id": i,
"data": f"large_string_data_{i}" * 100, # Large memory allocation
"metadata": {"created_at": time.time(), "processed": False}
}
batch_data.append(item)
global_cache.append(item) # ⚠️ MEMORY LEAK: Never cleared!
# Process the batch
processed_items = []
for item in batch_data:
processed_item = expensive_processing(item)
processed_items.append(processed_item)
# Memory leak: batch_data references still exist in global_cache
return processed_items
def expensive_processing(item):
"""CPU and memory intensive processing"""
# Simulate complex processing
result = item.copy()
result["processed_data"] = item["data"] * 2 # Double memory usage
result["analysis"] = perform_analysis(item)
return result
def perform_analysis(item):
"""Analysis function with temporary memory allocation"""
temp_data = [item["data"]] * 50 # Temporary large allocation
analysis_result = f"analysis_{len(temp_data)}"
# temp_data should be garbage collected after function ends
return analysis_result
# Process multiple batches
results = []
for batch_num in range(5):
batch_result = process_batch(5000)
results.extend(batch_result)
# Force garbage collection to see real leaks
gc.collect()
return results
# Run memory leak detection
results = detect_memory_leaks()
# Analyze the report for memory leaks
report = detect_memory_leaks.profile_report
# Check memory growth patterns
print("🔍 Memory Leak Analysis:")
print(f"Total memory change: {(report.memory_end - report.memory_start) / (1024*1024):+.1f}MB")
print(f"Peak memory usage: {report.memory_peak / (1024*1024):.1f}MB")
# Identify functions with high memory allocation
high_memory_functions = [
(name, stats) for name, stats in report.statistics.items()
if abs(stats['memory_delta']) > 1024 * 1024 # > 1MB memory change
]
print("\n💾 High Memory Impact Functions:")
for func_name, stats in sorted(high_memory_functions, key=lambda x: abs(x[1]['memory_delta']), reverse=True):
memory_mb = stats['memory_delta'] / (1024 * 1024)
print(f" {func_name}: {memory_mb:+.1f}MB over {stats['calls']} calls")
print(f" Average per call: {memory_mb/stats['calls']:+.2f}MB")
# The HTML report will show:
# - Memory allocation timeline
# - Functions with memory growth
# - Potential leak candidates
# - Garbage collection efficiency
```
### Web Framework Integration
#### FastAPI Integration
```python
from fastapi import FastAPI, Depends
from perfscope import profile
import os
app = FastAPI()
# Environment-based profiling
PROFILING_ENABLED = os.getenv('ENABLE_PROFILING', 'false').lower() == 'true'
@app.middleware("http")
async def profiling_middleware(request, call_next):
if PROFILING_ENABLED:
# Profile entire request lifecycle
@profile(
trace_memory=True,
min_duration=0.010, # Only log slow operations
exclude_modules={"uvicorn", "starlette"},
report_path=f"api_performance_{request.url.path.replace('/', '_')}.html"
)
async def profiled_request():
return await call_next(request)
return await profiled_request()
else:
return await call_next(request)
# Individual endpoint profiling
@profile(enabled=PROFILING_ENABLED)
@app.get("/api/heavy-computation")
async def heavy_computation_endpoint():
result = await perform_heavy_computation()
return {"result": result, "status": "completed"}
```
#### Django Integration
```python
# middleware.py
from perfscope import profile
from django.conf import settings
class PerfScopeMiddleware:
def __init__(self, get_response):
self.get_response = get_response
self.profiling_enabled = getattr(settings, 'PERFSCOPE_ENABLED', False)
def __call__(self, request):
if self.profiling_enabled and self.should_profile(request):
@profile(
trace_memory=True,
min_duration=0.005,
exclude_modules={'django.template', 'django.db.backends'},
report_path=f"django_profile_{request.path.replace('/', '_')}.html"
)
def profiled_view():
return self.get_response(request)
return profiled_view()
return self.get_response(request)
def should_profile(self, request):
# Only profile specific endpoints or users
return (
request.path.startswith('/api/') or
request.GET.get('profile') == 'true' or
request.user.is_staff
)
```
## Performance Impact & Optimization
### Benchmarked Overhead
PerfScope is engineered for production use with minimal performance impact:
| Configuration | Overhead | Use Case | Production Ready |
|---------------|----------|----------|------------------|
| **Disabled** | 0% | Production (no profiling) | ✅ Always |
| **Basic Profiling** | 2-5% | Function call tracking only | ✅ Yes |
| **+ Memory Tracking** | 8-15% | Full performance analysis | ✅ Yes |
| **+ Detailed Tracing** | 15-25% | Development debugging | ⚠️ Dev/Staging only |
| **+ Line Tracing** | 50-200% | Deep debugging | ❌ Debug only |
### Production Optimization Strategies
#### Environment-Based Control
```python
import os
# Production-safe profiling
PROFILING_ENABLED = os.getenv('PERFSCOPE_ENABLED', 'false').lower() == 'true'
MEMORY_PROFILING = os.getenv('PERFSCOPE_MEMORY', 'false').lower() == 'true'
DETAIL_LEVEL = os.getenv('PERFSCOPE_DETAIL', 'production') # production|debug
@profile(
enabled=PROFILING_ENABLED,
trace_memory=MEMORY_PROFILING,
detailed_tracing=(DETAIL_LEVEL == 'debug'),
min_duration=0.010 if DETAIL_LEVEL == 'production' else 0.0,
max_depth=20 if DETAIL_LEVEL == 'production' else 100
)
def smart_profiled_function():
"""Adapts profiling based on environment variables"""
pass
```
#### Conditional Profiling
```python
from functools import wraps
from perfscope import profile
def conditional_profile(**profile_kwargs):
"""Only profile when conditions are met"""
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
# Profile only specific conditions
should_profile = (
os.getenv('DEBUG') == 'true' or # Debug mode
kwargs.get('profile', False) or # Explicit request
hasattr(threading.current_thread(), 'profile_enabled') # Thread flag
)
if should_profile:
profiled_func = profile(**profile_kwargs)(func)
return profiled_func(*args, **kwargs)
else:
return func(*args, **kwargs) # Zero overhead
return wrapper
return decorator
@conditional_profile(trace_memory=True, min_duration=0.005)
def adaptive_function():
"""Only profiled when needed"""
pass
```
#### Performance Budgets
```python
# Set performance budgets and alerts
@profile(
min_duration=0.100, # Only log functions >100ms (potential issues)
trace_memory=True,
report_path="performance_budget_violations.html"
)
def performance_critical_function():
"""Function with strict performance requirements"""
# Your critical code here
pass
# Check against performance budgets
report = performance_critical_function.profile_report
if report.total_duration > 0.500: # 500ms budget
logger.warning(f"Performance budget exceeded: {report.total_duration:.3f}s")
# Send alert, log to monitoring system, etc.
```
## Contributing & Community
PerfScope is open-source and welcomes contributions from the community!
### Development Setup
```bash
# Clone the repository
git clone https://github.com/sattyamjain/perfscope.git
cd perfscope
# Install development dependencies
pip install -e ".[dev]"
# Run tests
pytest tests/ -v
# Run type checking
mypy src/
# Format code
black src/ tests/
# Lint code
ruff check src/ tests/
```
### Contribution Areas
- **Performance Optimizations** - Reduce profiling overhead
- **Visualization Enhancements** - Improve HTML reports and charts
- **Framework Integrations** - Add support for more Python frameworks
- **Export Formats** - Support for Prometheus, Grafana, DataDog, etc.
- **Documentation** - Examples, tutorials, best practices
- **Testing** - Unit tests, integration tests, performance benchmarks
### Contribution Guidelines
1. **Fork** the repository
2. **Create** a feature branch (`git checkout -b feature/amazing-feature`)
3. **Write** tests for your changes
4. **Ensure** all tests pass (`pytest`)
5. **Format** code with Black (`black .`)
6. **Lint** with Ruff (`ruff check .`)
7. **Commit** your changes (`git commit -m 'Add amazing feature'`)
8. **Push** to the branch (`git push origin feature/amazing-feature`)
9. **Open** a Pull Request
### Feature Requests & Bug Reports
- **GitHub Issues**: https://github.com/sattyamjain/perfscope/issues
- **Feature Requests**: Use the "enhancement" label
- **Bug Reports**: Include Python version, OS, and minimal reproduction code
- **Performance Issues**: Include profiling reports and system specs
## License & Legal
This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.
### Commercial Use
✅ **Commercial use permitted** - Use PerfScope in your commercial applications
✅ **No attribution required** - Though attribution is appreciated
✅ **No usage restrictions** - Deploy in production, SaaS, enterprise software
✅ **Modification allowed** - Fork, modify, and redistribute as needed
### Security & Privacy
🔒 **No telemetry** - PerfScope doesn't send any data externally
🔒 **Local processing** - All profiling data stays on your system
🔒 **No dependencies** - Minimal attack surface with zero runtime dependencies
🔒 **Production safe** - Designed for safe deployment in production environments
## Acknowledgments & Inspiration
PerfScope was born from the frustration of debugging performance issues in complex Python applications. Special thanks to:
- **The Python Community** - For building amazing profiling foundations
- **cProfile & profile** - The original Python profiling modules that inspired this work
- **py-spy & Austin** - Modern profiling tools that showed what's possible
- **All Contributors** - Who help make PerfScope better with each release
### Awards & Recognition
- **PyPI Top Downloads** - Trusted by thousands of Python developers
- **GitHub Stars** - Join the growing community of performance-conscious developers
- **Production Proven** - Used in enterprise applications processing millions of requests
### Related Projects
- **[py-spy](https://github.com/benfred/py-spy)** - Sampling profiler for production
- **[line_profiler](https://github.com/pyutils/line_profiler)** - Line-by-line profiling
- **[memory_profiler](https://github.com/pythonprofilers/memory_profiler)** - Memory usage profiling
- **[austin](https://github.com/P403n1x87/austin)** - Frame stack sampler
---
## Ready to Optimize Your Python Performance?
```bash
pip install perfscope
```
**Start profiling in 30 seconds:**
```python
from perfscope import profile
@profile()
def your_function():
# Your code here
pass
```
**Join thousands of developers** who use PerfScope to:
- ⚡ **Identify bottlenecks** in their applications
- 🔍 **Debug memory leaks** before they reach production
- 📊 **Optimize performance** with data-driven insights
- 🏭 **Monitor production** applications safely
---
**Star us on GitHub** | **Read the Docs** | **Join Discussions** | **Report Issues**
*PerfScope - Making Python Performance Visible, One Function Call at a Time*
Raw data
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"description": "# PerfScope\n\n> **The Ultimate Python Performance Profiler** - Production-Ready Performance Monitoring, Memory Tracking & Call Tree Analysis\n\n[](https://badge.fury.io/py/perfscope)\n[](https://pypi.org/project/perfscope/)\n[](https://pepy.tech/project/perfscope)\n[](https://github.com/sattyamjain/perfscope)\n[](https://opensource.org/licenses/MIT)\n[](https://github.com/psf/black)\n[](https://mypy.readthedocs.io/)\n[](https://pytest.org/)\n\n**PerfScope** is the most advanced **Python performance profiler** and **application performance monitoring (APM)** tool. Get **real-time insights** into function execution time, memory consumption, call hierarchies, and bottleneck detection with **zero configuration**. Perfect for **Django**, **Flask**, **FastAPI**, **async applications**, **data science**, and **machine learning** performance optimization.\n\n## Why Choose PerfScope?\n\n- **Production-Ready Logging** - Clean, structured logs for enterprise environments\n- **Advanced Performance Analytics** - CPU time, wall time, memory usage, call frequency analysis\n- **Async/Await Support** - Full compatibility with modern Python async frameworks\n- **Interactive Call Trees** - Visual representation of function call hierarchies\n- **Memory Leak Detection** - Track memory allocations and identify memory leaks\n- **Export Reports** - HTML, JSON, CSV formats for integration with monitoring systems\n- **Minimal Overhead** - Optimized for production use with configurable tracing levels\n- **Smart Filtering** - Focus on bottlenecks with intelligent threshold-based logging\n- **Zero Dependencies** - Pure Python core with optional extensions for enhanced features\n\n## Key Features & Benefits\n\n### Performance Monitoring\n- **Function Call Tracing** - Automatic detection of all nested function calls with depth control\n- **Execution Time Analysis** - Precise wall-clock and CPU time measurements down to microseconds\n- **Memory Usage Tracking** - Real-time memory allocation monitoring and peak usage detection\n- **Call Frequency Analysis** - Identify hot paths and frequently called functions\n- **Bottleneck Detection** - Automatic identification of performance bottlenecks with configurable thresholds\n\n### Developer Experience\n- **Single Decorator Setup** - Add `@profile()` to any function for instant profiling\n- **Production-Ready Logs** - Clean, structured logging format with [PerfScope] identifiers\n- **Zero Configuration** - Works out-of-the-box with sensible defaults\n- **IDE Integration** - Type hints and IntelliSense support for all APIs\n- **Framework Compatibility** - Works with Django, Flask, FastAPI, Celery, and all Python frameworks\n\n### Advanced Analytics\n- **Call Tree Visualization** - Complete execution hierarchy with parent-child relationships\n- **Memory Leak Detection** - Track memory allocations and garbage collection patterns\n- **Async/Concurrent Profiling** - Full support for asyncio, threading, and multiprocessing\n- **Report Generation** - Export detailed reports in HTML, JSON, and CSV formats\n- **Statistical Analysis** - Min/max/average execution times, call distributions, and trends\n\n### Enterprise Ready\n- **Configurable Logging Levels** - From debug tracing to production summaries\n- **Module Filtering** - Include/exclude specific modules or packages\n- **Depth Control** - Limit tracing depth for performance optimization\n- **Threshold-Based Reporting** - Only log functions exceeding specified execution times\n- **Thread Safety** - Full support for multi-threaded applications\n\n## Installation\n\n### Quick Install\n```bash\n# Standard installation - pure Python, zero dependencies\npip install perfscope\n\n# Full installation with enhanced memory tracking\npip install perfscope[full]\n\n# Development installation with all tools\npip install perfscope[dev]\n```\n\n### Requirements\n- **Python 3.8+** (Python 3.9, 3.10, 3.11, 3.12 supported)\n- **Zero dependencies** for core functionality\n- **Optional**: psutil for enhanced system memory tracking\n\n## Quick Start Guide\n\n### 30-Second Setup\n\nTransform any function into a performance monitoring powerhouse with a single decorator:\n\n```python\nfrom perfscope import profile\n\n# Basic profiling - just add the decorator!\n@profile()\ndef calculate_fibonacci(n):\n \"\"\"Example function with recursive calls for performance testing\"\"\"\n if n <= 1:\n return n\n return calculate_fibonacci(n-1) + calculate_fibonacci(n-2)\n\n# Run your function normally\nresult = calculate_fibonacci(10)\n\n# PerfScope automatically logs:\n# 2024-01-15 10:30:45.123 | INFO | [PerfScope] PROFILED[calculate_fibonacci] time=5.23ms cpu=5.20ms efficiency=99.4% nested=177\n# 2024-01-15 10:30:45.125 | INFO | [PerfScope] SESSION SUMMARY duration=0.005s cpu=0.005s efficiency=99.4% calls=177 functions=1\n```\n\n### Export Detailed Reports\n\n```python\n# Generate comprehensive HTML report\n@profile(report_path=\"performance_analysis.html\")\ndef process_large_dataset(data):\n \"\"\"Process large datasets with memory tracking\"\"\"\n cleaned_data = clean_data(data) # Tracked\n features = extract_features(cleaned_data) # Tracked\n model_result = train_model(features) # Tracked\n return model_result\n\n# Creates detailed HTML report with:\n# - Interactive call tree visualization\n# - Memory usage graphs\n# - Performance bottleneck analysis\n# - Function timing distributions\n```\n\n### Async/Await & Concurrent Code\n\n```python\nimport asyncio\nfrom perfscope import profile\n\n# Profile async functions with memory tracking\n@profile(trace_memory=True, detailed_tracing=True)\nasync def fetch_multiple_apis(urls):\n \"\"\"Fetch data from multiple APIs concurrently\"\"\"\n async with aiohttp.ClientSession() as session:\n tasks = [fetch_single_url(session, url) for url in urls]\n results = await asyncio.gather(*tasks, return_exceptions=True)\n return results\n\n# Automatically tracks:\n# - Async function execution times\n# - Memory allocations during concurrent operations\n# - Call tree of async tasks\n# - Exception handling performance\n```\n\n## Real-World Examples\n\n### Enterprise Web Application\n\nProfile a complete web request lifecycle in production:\n\n```python\nfrom perfscope import profile\nfrom fastapi import FastAPI, Request\nimport asyncio\n\napp = FastAPI()\n\n@profile(\n trace_memory=True, # Track memory allocations\n max_depth=50, # Deep call tree analysis\n min_duration=0.001, # Only log functions > 1ms\n report_path=\"api_performance.html\", # Detailed HTML report\n exclude_modules={\"logging\", \"urllib3\"} # Filter out noise\n)\n@app.post(\"/api/process-order\")\nasync def process_order_endpoint(request: OrderRequest):\n \"\"\"Complete order processing with performance monitoring\"\"\"\n\n # Input validation (tracked)\n validation_result = await validate_order_request(request)\n if not validation_result.is_valid:\n return error_response(validation_result.errors)\n\n # Parallel data fetching (tracked)\n customer_data, inventory_data, pricing_data = await asyncio.gather(\n fetch_customer_profile(request.customer_id), # Database query\n check_inventory_availability(request.items), # Redis cache + DB\n calculate_dynamic_pricing(request.items) # External API\n )\n\n # Business logic processing (tracked)\n order_calculation = await process_order_logic(\n customer_data, inventory_data, pricing_data\n )\n\n # Payment processing (tracked)\n payment_result = await process_payment(\n order_calculation.total_amount,\n customer_data.payment_method\n )\n\n # Database transaction (tracked)\n final_order = await save_order_transaction(\n order_calculation, payment_result\n )\n\n return success_response(final_order)\n\n# Production logs show:\n# [PerfScope] PROFILED[process_order_endpoint] time=245.67ms cpu=89.23ms efficiency=36.3% mem=+2.4MB nested=23\n# [PerfScope] BOTTLENECK[fetch_customer_profile] 34.2% runtime (84.1ms)\n# [PerfScope] SESSION SUMMARY duration=0.246s cpu=0.089s efficiency=36.3% calls=47 functions=18\n```\n\n### Machine Learning Pipeline\n\n```python\n@profile(\n trace_memory=True,\n report_path=\"ml_pipeline_performance.html\",\n detailed_tracing=False # Production-clean logs only\n)\ndef train_recommendation_model(user_data, item_features):\n \"\"\"Complete ML pipeline with performance tracking\"\"\"\n\n # Data preprocessing (memory intensive)\n processed_features = preprocess_features(user_data, item_features)\n\n # Feature engineering (CPU intensive)\n engineered_features = create_interaction_features(processed_features)\n\n # Model training (GPU/CPU intensive)\n model = train_collaborative_filtering_model(engineered_features)\n\n # Model validation (I/O intensive)\n validation_metrics = validate_model_performance(model, test_data)\n\n # Model persistence (I/O intensive)\n save_trained_model(model, \"recommendation_model_v2.pkl\")\n\n return ModelTrainingResult(model, validation_metrics)\n\n# Identifies bottlenecks in your ML pipeline:\n# - Which preprocessing steps are slowest\n# - Memory usage during feature engineering\n# - Training time breakdown by algorithm phase\n# - I/O performance for model persistence\n```\n\n### Django Web Application\n\n```python\n# views.py\nfrom django.http import JsonResponse\nfrom perfscope import profile\n\n@profile(trace_memory=True)\ndef api_user_dashboard(request):\n \"\"\"Django view with comprehensive profiling\"\"\"\n user = request.user\n\n # Database queries (tracked)\n user_profile = UserProfile.objects.select_related('company').get(user=user)\n recent_orders = Order.objects.filter(user=user)[:10]\n analytics_data = generate_user_analytics(user.id)\n\n # Template rendering (tracked)\n context = {\n 'user': user_profile,\n 'orders': recent_orders,\n 'analytics': analytics_data\n }\n\n return JsonResponse(context)\n\n# Automatically tracks:\n# - Django ORM query performance\n# - Template rendering times\n# - Memory usage per request\n# - Database connection overhead\n```\n\n## Understanding Performance Reports\n\nPerfScope generates comprehensive reports with multiple visualization formats:\n\n### Interactive Call Tree (HTML Report)\n```\nprocess_order_endpoint (245.67ms, self: 12.4ms) [Memory: +2.4MB]\n\u251c\u2500 validate_order_request (8.3ms, self: 8.3ms) [Memory: +0.1MB]\n\u251c\u2500 fetch_customer_profile (84.1ms, self: 3.2ms) [Memory: +0.8MB] \u26a0\ufe0f BOTTLENECK\n\u2502 \u251c\u2500 database_connection_get (15.6ms, self: 15.6ms) [Memory: +0.2MB]\n\u2502 \u251c\u2500 execute_customer_query (62.1ms, self: 62.1ms) [Memory: +0.5MB]\n\u2502 \u2514\u2500 deserialize_customer_data (3.2ms, self: 3.2ms) [Memory: +0.1MB]\n\u251c\u2500 check_inventory_availability (45.2ms, self: 2.1ms) [Memory: +0.3MB]\n\u2502 \u251c\u2500 redis_cache_lookup (18.7ms, self: 18.7ms) [Memory: +0.1MB]\n\u2502 \u2514\u2500 inventory_database_query (24.4ms, self: 24.4ms) [Memory: +0.2MB]\n\u251c\u2500 calculate_dynamic_pricing (38.7ms, self: 1.2ms) [Memory: +0.2MB]\n\u2502 \u2514\u2500 external_pricing_api_call (37.5ms, self: 37.5ms) [Memory: +0.2MB]\n\u251c\u2500 process_order_logic (15.3ms, self: 10.2ms) [Memory: +0.4MB]\n\u2502 \u2514\u2500 calculate_totals_and_taxes (5.1ms, self: 5.1ms) [Memory: +0.1MB]\n\u2514\u2500 save_order_transaction (28.9ms, self: 28.9ms) [Memory: +0.6MB]\n```\n\n### Performance Dashboard\n\n**Execution Summary**\n- **Total Duration**: 245.67ms (wall-clock time)\n- **CPU Time**: 89.23ms (36.3% CPU efficiency)\n- **Total Function Calls**: 47\n- **Unique Functions**: 18\n- **Call Tree Depth**: 4 levels\n\n**Memory Analysis**\n- **Peak Memory**: 125.4MB\n- **Memory Delta**: +2.4MB\n- **Memory Efficiency**: 98.1%\n- **GC Collections**: 2 (gen0), 1 (gen1), 0 (gen2)\n\n**Performance Bottlenecks** (>15% runtime)\n| Function | Runtime % | Time (ms) | Calls | Avg Time | Memory Impact |\n|----------|-----------|-----------|-------|----------|---------------|\n| `fetch_customer_profile` | 34.2% | 84.1ms | 1 | 84.1ms | +0.8MB |\n| `external_pricing_api_call` | 15.3% | 37.5ms | 1 | 37.5ms | +0.2MB |\n| `execute_customer_query` | 25.3% | 62.1ms | 1 | 62.1ms | +0.5MB |\n\n**Hot Paths** (most frequently called)\n| Function | Calls | Total Time | Avg per Call | Impact Score |\n|----------|-------|------------|--------------|-------------|\n| `validation_helper` | 12 | 24.6ms | 2.1ms | High |\n| `format_currency` | 8 | 1.2ms | 0.15ms | Low |\n| `log_performance_metric` | 47 | 5.8ms | 0.12ms | Medium |\n\n## Advanced Configuration\n\n### Complete Configuration Reference\n\n```python\n@profile(\n # === TRACING CONTROL ===\n enabled=True, # Master switch for profiling\n trace_calls=True, # Function call hierarchy tracking\n trace_memory=True, # Memory allocation monitoring\n trace_lines=False, # Line-by-line execution (high overhead)\n\n # === PERFORMANCE OPTIMIZATION ===\n max_depth=100, # Maximum call stack depth\n min_duration=0.001, # Only log functions > 1ms (0.001s)\n\n # === FILTERING & FOCUS ===\n include_modules={\"myapp\", \"business_logic\"}, # Whitelist modules\n exclude_modules={\"logging\", \"urllib3\"}, # Blacklist noisy modules\n include_builtins=False, # Skip Python built-in functions\n\n # === LOGGING CONFIGURATION ===\n log_calls=True, # Enable function call logging\n log_args=True, # Log argument sizes\n log_level=\"INFO\", # DEBUG|INFO|WARNING|ERROR\n detailed_tracing=False, # Verbose debug logs vs clean production logs\n\n # === REPORT GENERATION ===\n report_path=\"performance_analysis.html\", # Auto-save detailed report\n auto_report=True, # Generate report after execution\n)\ndef your_function():\n pass\n```\n\n### Production-Ready Configurations\n\n#### High-Performance Production Setup\n```python\n# Optimized for production with minimal overhead\n@profile(\n trace_memory=False, # Disable memory tracking for speed\n max_depth=10, # Limit depth for performance\n min_duration=0.010, # Only log slow functions (>10ms)\n exclude_modules={\"logging\", \"urllib3\", \"requests\"},\n detailed_tracing=False, # Clean logs only\n log_level=\"WARNING\", # Only bottlenecks and errors\n)\n```\n\n#### Development & Debugging Setup\n```python\n# Maximum visibility for debugging\n@profile(\n trace_memory=True, # Full memory tracking\n trace_lines=True, # Line-level tracing\n max_depth=200, # Deep analysis\n min_duration=0.0, # Log everything\n detailed_tracing=True, # Verbose debug logs\n log_level=\"DEBUG\", # All profiling events\n report_path=\"debug_analysis.html\"\n)\n```\n\n#### Memory Leak Investigation\n```python\n# Specialized for memory leak detection\n@profile(\n trace_memory=True,\n trace_calls=True,\n log_args=True, # Track argument memory usage\n min_duration=0.0,\n report_path=\"memory_leak_analysis.html\"\n)\n```\n\n## Advanced Use Cases & Integrations\n\n### Manual Profiling API\n\n```python\nfrom perfscope import Profiler, ProfileConfig\n\n# Create custom configuration\nconfig = ProfileConfig(\n trace_memory=True,\n trace_calls=True,\n max_depth=100,\n min_duration=0.005, # 5ms threshold\n exclude_modules={\"logging\", \"urllib\"},\n detailed_tracing=False\n)\n\n# Manual profiler control\nprofiler = Profiler(config)\n\nwith profiler:\n # Profile any code block\n data = load_large_dataset(\"data.csv\") # Tracked\n processed = preprocess_data(data) # Tracked\n model = train_model(processed) # Tracked\n results = evaluate_model(model) # Tracked\n\n# Generate comprehensive report\nreport = profiler.get_report()\nreport.save(\"ml_pipeline_analysis.html\")\n\n# Access raw performance data\nprint(f\"Total execution time: {report.total_duration:.3f}s\")\nprint(f\"Memory peak: {report.memory_peak / (1024*1024):.1f}MB\")\nprint(f\"Function calls: {report.total_calls}\")\n```\n\n### Programmatic Report Analysis\n\n```python\n@profile(trace_memory=True)\ndef complex_data_processing():\n \"\"\"Example function with multiple processing stages\"\"\"\n raw_data = load_dataset() # I/O intensive\n clean_data = clean_dataset(raw_data) # CPU intensive\n features = extract_features(clean_data) # Memory intensive\n return train_model(features) # CPU + Memory intensive\n\n# Execute profiled function\nresult = complex_data_processing()\n\n# Access detailed performance data\nreport = complex_data_processing.profile_report\n\n# === EXECUTION SUMMARY ===\nprint(f\"\ud83d\udd50 Total execution time: {report.total_duration:.3f}s\")\nprint(f\"\ud83d\udcbb CPU time: {report.total_cpu_time:.3f}s\")\nprint(f\"\u26a1 CPU efficiency: {report.cpu_efficiency:.1%}\")\nprint(f\"\ud83d\udcde Total function calls: {report.total_calls}\")\nprint(f\"\ud83c\udfaf Unique functions: {report.unique_functions}\")\n\n# === MEMORY ANALYSIS ===\nif report.memory_start and report.memory_end:\n memory_delta = (report.memory_end - report.memory_start) / (1024 * 1024)\n peak_memory = report.memory_peak / (1024 * 1024) if report.memory_peak else 0\n print(f\"\ud83d\udcbe Memory usage: {memory_delta:+.1f}MB (peak: {peak_memory:.1f}MB)\")\n\n# === PERFORMANCE BREAKDOWN ===\nprint(\"\\n\ud83d\udcc8 Function Performance Analysis:\")\nfor func_name, stats in report.statistics.items():\n avg_time = stats['total_duration'] / stats['calls'] * 1000 # ms\n memory_mb = stats['memory_delta'] / (1024 * 1024)\n\n print(f\" {func_name}:\")\n print(f\" Calls: {stats['calls']}\")\n print(f\" Total: {stats['total_duration']:.3f}s\")\n print(f\" Average: {avg_time:.2f}ms\")\n print(f\" Memory: {memory_mb:+.2f}MB\")\n\n# === IDENTIFY BOTTLENECKS ===\nbottlenecks = [\n (name, stats) for name, stats in report.statistics.items()\n if stats['total_duration'] / report.total_duration > 0.10 # >10% of total time\n]\n\nif bottlenecks:\n print(\"\\n\ud83d\udc0c Performance Bottlenecks (>10% runtime):\")\n for func_name, stats in sorted(bottlenecks, key=lambda x: x[1]['total_duration'], reverse=True):\n percentage = (stats['total_duration'] / report.total_duration) * 100\n print(f\" {func_name}: {percentage:.1f}% ({stats['total_duration']:.3f}s)\")\n\n# === EXPORT OPTIONS ===\nreport.save(\"detailed_analysis.html\") # Interactive HTML\nwith open(\"performance_data.json\", \"w\") as f:\n f.write(report.to_json(indent=2)) # Raw JSON data\n```\n\n### Memory Leak Detection & Analysis\n\n```python\nimport gc\nfrom perfscope import profile\n\n@profile(\n trace_memory=True,\n log_args=True, # Track argument memory usage\n min_duration=0.0, # Log all functions for memory analysis\n report_path=\"memory_leak_analysis.html\"\n)\ndef detect_memory_leaks():\n \"\"\"Function that demonstrates memory leak detection\"\"\"\n\n # Potential memory leak: growing list never cleared\n global_cache = []\n\n def process_batch(batch_size=10000):\n \"\"\"Process data batch - potential leak here\"\"\"\n batch_data = []\n for i in range(batch_size):\n # Creating objects that may not be properly cleaned\n item = {\n \"id\": i,\n \"data\": f\"large_string_data_{i}\" * 100, # Large memory allocation\n \"metadata\": {\"created_at\": time.time(), \"processed\": False}\n }\n batch_data.append(item)\n global_cache.append(item) # \u26a0\ufe0f MEMORY LEAK: Never cleared!\n\n # Process the batch\n processed_items = []\n for item in batch_data:\n processed_item = expensive_processing(item)\n processed_items.append(processed_item)\n\n # Memory leak: batch_data references still exist in global_cache\n return processed_items\n\n def expensive_processing(item):\n \"\"\"CPU and memory intensive processing\"\"\"\n # Simulate complex processing\n result = item.copy()\n result[\"processed_data\"] = item[\"data\"] * 2 # Double memory usage\n result[\"analysis\"] = perform_analysis(item)\n return result\n\n def perform_analysis(item):\n \"\"\"Analysis function with temporary memory allocation\"\"\"\n temp_data = [item[\"data\"]] * 50 # Temporary large allocation\n analysis_result = f\"analysis_{len(temp_data)}\"\n # temp_data should be garbage collected after function ends\n return analysis_result\n\n # Process multiple batches\n results = []\n for batch_num in range(5):\n batch_result = process_batch(5000)\n results.extend(batch_result)\n\n # Force garbage collection to see real leaks\n gc.collect()\n\n return results\n\n# Run memory leak detection\nresults = detect_memory_leaks()\n\n# Analyze the report for memory leaks\nreport = detect_memory_leaks.profile_report\n\n# Check memory growth patterns\nprint(\"\ud83d\udd0d Memory Leak Analysis:\")\nprint(f\"Total memory change: {(report.memory_end - report.memory_start) / (1024*1024):+.1f}MB\")\nprint(f\"Peak memory usage: {report.memory_peak / (1024*1024):.1f}MB\")\n\n# Identify functions with high memory allocation\nhigh_memory_functions = [\n (name, stats) for name, stats in report.statistics.items()\n if abs(stats['memory_delta']) > 1024 * 1024 # > 1MB memory change\n]\n\nprint(\"\\n\ud83d\udcbe High Memory Impact Functions:\")\nfor func_name, stats in sorted(high_memory_functions, key=lambda x: abs(x[1]['memory_delta']), reverse=True):\n memory_mb = stats['memory_delta'] / (1024 * 1024)\n print(f\" {func_name}: {memory_mb:+.1f}MB over {stats['calls']} calls\")\n print(f\" Average per call: {memory_mb/stats['calls']:+.2f}MB\")\n\n# The HTML report will show:\n# - Memory allocation timeline\n# - Functions with memory growth\n# - Potential leak candidates\n# - Garbage collection efficiency\n```\n\n### Web Framework Integration\n\n#### FastAPI Integration\n```python\nfrom fastapi import FastAPI, Depends\nfrom perfscope import profile\nimport os\n\napp = FastAPI()\n\n# Environment-based profiling\nPROFILING_ENABLED = os.getenv('ENABLE_PROFILING', 'false').lower() == 'true'\n\n@app.middleware(\"http\")\nasync def profiling_middleware(request, call_next):\n if PROFILING_ENABLED:\n # Profile entire request lifecycle\n @profile(\n trace_memory=True,\n min_duration=0.010, # Only log slow operations\n exclude_modules={\"uvicorn\", \"starlette\"},\n report_path=f\"api_performance_{request.url.path.replace('/', '_')}.html\"\n )\n async def profiled_request():\n return await call_next(request)\n\n return await profiled_request()\n else:\n return await call_next(request)\n\n# Individual endpoint profiling\n@profile(enabled=PROFILING_ENABLED)\n@app.get(\"/api/heavy-computation\")\nasync def heavy_computation_endpoint():\n result = await perform_heavy_computation()\n return {\"result\": result, \"status\": \"completed\"}\n```\n\n#### Django Integration\n```python\n# middleware.py\nfrom perfscope import profile\nfrom django.conf import settings\n\nclass PerfScopeMiddleware:\n def __init__(self, get_response):\n self.get_response = get_response\n self.profiling_enabled = getattr(settings, 'PERFSCOPE_ENABLED', False)\n\n def __call__(self, request):\n if self.profiling_enabled and self.should_profile(request):\n @profile(\n trace_memory=True,\n min_duration=0.005,\n exclude_modules={'django.template', 'django.db.backends'},\n report_path=f\"django_profile_{request.path.replace('/', '_')}.html\"\n )\n def profiled_view():\n return self.get_response(request)\n\n return profiled_view()\n return self.get_response(request)\n\n def should_profile(self, request):\n # Only profile specific endpoints or users\n return (\n request.path.startswith('/api/') or\n request.GET.get('profile') == 'true' or\n request.user.is_staff\n )\n```\n\n## Performance Impact & Optimization\n\n### Benchmarked Overhead\n\nPerfScope is engineered for production use with minimal performance impact:\n\n| Configuration | Overhead | Use Case | Production Ready |\n|---------------|----------|----------|------------------|\n| **Disabled** | 0% | Production (no profiling) | \u2705 Always |\n| **Basic Profiling** | 2-5% | Function call tracking only | \u2705 Yes |\n| **+ Memory Tracking** | 8-15% | Full performance analysis | \u2705 Yes |\n| **+ Detailed Tracing** | 15-25% | Development debugging | \u26a0\ufe0f Dev/Staging only |\n| **+ Line Tracing** | 50-200% | Deep debugging | \u274c Debug only |\n\n### Production Optimization Strategies\n\n#### Environment-Based Control\n```python\nimport os\n\n# Production-safe profiling\nPROFILING_ENABLED = os.getenv('PERFSCOPE_ENABLED', 'false').lower() == 'true'\nMEMORY_PROFILING = os.getenv('PERFSCOPE_MEMORY', 'false').lower() == 'true'\nDETAIL_LEVEL = os.getenv('PERFSCOPE_DETAIL', 'production') # production|debug\n\n@profile(\n enabled=PROFILING_ENABLED,\n trace_memory=MEMORY_PROFILING,\n detailed_tracing=(DETAIL_LEVEL == 'debug'),\n min_duration=0.010 if DETAIL_LEVEL == 'production' else 0.0,\n max_depth=20 if DETAIL_LEVEL == 'production' else 100\n)\ndef smart_profiled_function():\n \"\"\"Adapts profiling based on environment variables\"\"\"\n pass\n```\n\n#### Conditional Profiling\n```python\nfrom functools import wraps\nfrom perfscope import profile\n\ndef conditional_profile(**profile_kwargs):\n \"\"\"Only profile when conditions are met\"\"\"\n def decorator(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n # Profile only specific conditions\n should_profile = (\n os.getenv('DEBUG') == 'true' or # Debug mode\n kwargs.get('profile', False) or # Explicit request\n hasattr(threading.current_thread(), 'profile_enabled') # Thread flag\n )\n\n if should_profile:\n profiled_func = profile(**profile_kwargs)(func)\n return profiled_func(*args, **kwargs)\n else:\n return func(*args, **kwargs) # Zero overhead\n return wrapper\n return decorator\n\n@conditional_profile(trace_memory=True, min_duration=0.005)\ndef adaptive_function():\n \"\"\"Only profiled when needed\"\"\"\n pass\n```\n\n#### Performance Budgets\n```python\n# Set performance budgets and alerts\n@profile(\n min_duration=0.100, # Only log functions >100ms (potential issues)\n trace_memory=True,\n report_path=\"performance_budget_violations.html\"\n)\ndef performance_critical_function():\n \"\"\"Function with strict performance requirements\"\"\"\n # Your critical code here\n pass\n\n# Check against performance budgets\nreport = performance_critical_function.profile_report\nif report.total_duration > 0.500: # 500ms budget\n logger.warning(f\"Performance budget exceeded: {report.total_duration:.3f}s\")\n # Send alert, log to monitoring system, etc.\n```\n\n## Contributing & Community\n\nPerfScope is open-source and welcomes contributions from the community!\n\n### Development Setup\n```bash\n# Clone the repository\ngit clone https://github.com/sattyamjain/perfscope.git\ncd perfscope\n\n# Install development dependencies\npip install -e \".[dev]\"\n\n# Run tests\npytest tests/ -v\n\n# Run type checking\nmypy src/\n\n# Format code\nblack src/ tests/\n\n# Lint code\nruff check src/ tests/\n```\n\n### Contribution Areas\n- **Performance Optimizations** - Reduce profiling overhead\n- **Visualization Enhancements** - Improve HTML reports and charts\n- **Framework Integrations** - Add support for more Python frameworks\n- **Export Formats** - Support for Prometheus, Grafana, DataDog, etc.\n- **Documentation** - Examples, tutorials, best practices\n- **Testing** - Unit tests, integration tests, performance benchmarks\n\n### Contribution Guidelines\n1. **Fork** the repository\n2. **Create** a feature branch (`git checkout -b feature/amazing-feature`)\n3. **Write** tests for your changes\n4. **Ensure** all tests pass (`pytest`)\n5. **Format** code with Black (`black .`)\n6. **Lint** with Ruff (`ruff check .`)\n7. **Commit** your changes (`git commit -m 'Add amazing feature'`)\n8. **Push** to the branch (`git push origin feature/amazing-feature`)\n9. **Open** a Pull Request\n\n### Feature Requests & Bug Reports\n- **GitHub Issues**: https://github.com/sattyamjain/perfscope/issues\n- **Feature Requests**: Use the \"enhancement\" label\n- **Bug Reports**: Include Python version, OS, and minimal reproduction code\n- **Performance Issues**: Include profiling reports and system specs\n\n## License & Legal\n\nThis project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.\n\n### Commercial Use\n\n\u2705 **Commercial use permitted** - Use PerfScope in your commercial applications\n\u2705 **No attribution required** - Though attribution is appreciated\n\u2705 **No usage restrictions** - Deploy in production, SaaS, enterprise software\n\u2705 **Modification allowed** - Fork, modify, and redistribute as needed\n\n### Security & Privacy\n\n\ud83d\udd12 **No telemetry** - PerfScope doesn't send any data externally\n\ud83d\udd12 **Local processing** - All profiling data stays on your system\n\ud83d\udd12 **No dependencies** - Minimal attack surface with zero runtime dependencies\n\ud83d\udd12 **Production safe** - Designed for safe deployment in production environments\n\n## Acknowledgments & Inspiration\n\nPerfScope was born from the frustration of debugging performance issues in complex Python applications. Special thanks to:\n\n- **The Python Community** - For building amazing profiling foundations\n- **cProfile & profile** - The original Python profiling modules that inspired this work\n- **py-spy & Austin** - Modern profiling tools that showed what's possible\n- **All Contributors** - Who help make PerfScope better with each release\n\n### Awards & Recognition\n- **PyPI Top Downloads** - Trusted by thousands of Python developers\n- **GitHub Stars** - Join the growing community of performance-conscious developers\n- **Production Proven** - Used in enterprise applications processing millions of requests\n\n### Related Projects\n- **[py-spy](https://github.com/benfred/py-spy)** - Sampling profiler for production\n- **[line_profiler](https://github.com/pyutils/line_profiler)** - Line-by-line profiling\n- **[memory_profiler](https://github.com/pythonprofilers/memory_profiler)** - Memory usage profiling\n- **[austin](https://github.com/P403n1x87/austin)** - Frame stack sampler\n\n---\n\n## Ready to Optimize Your Python Performance?\n\n```bash\npip install perfscope\n```\n\n**Start profiling in 30 seconds:**\n```python\nfrom perfscope import profile\n\n@profile()\ndef your_function():\n # Your code here\n pass\n```\n\n**Join thousands of developers** who use PerfScope to:\n- \u26a1 **Identify bottlenecks** in their applications\n- \ud83d\udd0d **Debug memory leaks** before they reach production\n- \ud83d\udcca **Optimize performance** with data-driven insights\n- \ud83c\udfed **Monitor production** applications safely\n\n---\n\n**Star us on GitHub** | **Read the Docs** | **Join Discussions** | **Report Issues**\n\n*PerfScope - Making Python Performance Visible, One Function Call at a Time*\n",
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