# Profiling and Benchmarking

## Overview

Profiling is the process of analyzing application performance to identify bottlenecks and optimization opportunities. This guide covers profiling techniques, tools, and best practices.

## Profiling Types

### CPU Profiling

**Identify CPU Bottlenecks:**
- Find hot functions
- Identify slow algorithms
- Measure function execution time
- Find optimization opportunities

**Tools:**
- cProfile (Python)
- py-spy (Python)
- perf (Linux)
- Xcode Instruments (macOS)
- Visual Studio Profiler (Windows)

### Memory Profiling

**Identify Memory Issues:**
- Find memory leaks
- Track memory allocation
- Identify high memory usage
- Monitor object lifetimes

**Tools:**
- memory_profiler (Python)
- Valgrind (C/C++)
- Chrome DevTools (JavaScript)
- JProfiler (Java)

### I/O Profiling

**Identify I/O Bottlenecks:**
- Track file I/O
- Monitor network I/O
- Identify slow database queries
- Find blocking operations

## Profiling Techniques

### Statistical Profiling

**Sample-Based:**
- Periodically sample call stack
- Low overhead
- Statistical accuracy
- Good for production

### Instrumentation Profiling

**Code Instrumentation:**
- Add timing code
- High accuracy
- Higher overhead
- Better for development

### Event-Based Profiling

**Track Events:**
- Function entry/exit
- Memory allocations
- I/O operations
- Custom events

## Python Profiling

### cProfile

**Built-in Profiler:**
```python
import cProfile
import pstats

# Profile a function
profiler = cProfile.Profile()
profiler.enable()
my_function()
profiler.disable()

# Analyze results
stats = pstats.Stats(profiler)
stats.sort_stats('cumulative')
stats.print_stats(20)  # Top 20 functions
```

### line_profiler

**Line-by-Line Profiling:**
```python
@profile
def my_function():
    # Code to profile
    pass

# Run with: kernprof -l -v script.py
```

### memory_profiler

**Memory Usage Profiling:**
```python
@profile
def my_function():
    # Code to profile
    pass

# Run with: python -m memory_profiler script.py
```

## Benchmarking

### Benchmarking Principles

**Accurate Benchmarks:**
- Use realistic data
- Run multiple iterations
- Account for variance
- Warm up JIT/cache
- Isolate from other processes

### Benchmarking Tools

**Common Tools:**
- timeit (Python)
- pytest-benchmark
- Apache Bench (HTTP)
- wrk (HTTP)
- JMeter (Load testing)

### Python timeit

**Simple Benchmarking:**
```python
import timeit

# Time a function
time = timeit.timeit(
    'my_function()',
    setup='from __main__ import my_function',
    number=1000
)
print(f"Average time: {time/1000:.4f} seconds")
```

### pytest-benchmark

**Automated Benchmarks:**
```python
def test_my_function(benchmark):
    result = benchmark(my_function, arg1, arg2)
    assert result is not None
```

## Performance Metrics

### Key Metrics

**Response Time:**
- p50 (median)
- p95 (95th percentile)
- p99 (99th percentile)
- p999 (99.9th percentile)

**Throughput:**
- Requests per second
- Operations per second
- Transactions per second

**Resource Usage:**
- CPU utilization
- Memory usage
- I/O wait time
- Network bandwidth

## Profiling Workflow

### 1. Establish Baseline

**Measure Current Performance:**
- Run benchmarks
- Collect metrics
- Document baseline
- Set performance goals

### 2. Profile Application

**Identify Bottlenecks:**
- Run profiler
- Analyze results
- Identify hot paths
- Find optimization opportunities

### 3. Optimize

**Make Improvements:**
- Focus on hot paths
- Apply optimizations
- Measure impact
- Verify correctness

### 4. Validate

**Verify Improvements:**
- Re-run benchmarks
- Compare with baseline
- Check for regressions
- Document improvements

## Production Profiling

### Continuous Profiling

**Always-On Profiling:**
- Low-overhead sampling
- Statistical profiling
- Aggregate results
- Alert on anomalies

**Tools:**
- Pyroscope
- Datadog Continuous Profiler
- Google Cloud Profiler
- AWS CodeGuru

### Sampling Profiling

**Low-Overhead:**
- Sample periodically
- Statistical accuracy
- Minimal performance impact
- Suitable for production

## Best Practices

1. **Profile Before Optimizing**: Measure first
2. **Focus on Hot Paths**: Optimize frequently executed code
3. **Use Multiple Tools**: Different tools for different insights
4. **Profile Real Workloads**: Use realistic data
5. **Monitor Production**: Track performance in production
6. **Document Findings**: Record profiling results
7. **Automate Benchmarking**: Include in CI/CD
8. **Compare Results**: Track performance over time

## Common Bottlenecks

### Database Queries

**Issues:**
- N+1 queries
- Missing indexes
- Inefficient queries
- Large result sets

**Solutions:**
- Use query profilers
- Add indexes
- Optimize queries
- Use pagination

### Algorithm Complexity

**Issues:**
- O(n²) algorithms
- Inefficient data structures
- Unnecessary computations

**Solutions:**
- Choose better algorithms
- Use appropriate data structures
- Cache results

### I/O Operations

**Issues:**
- Synchronous I/O
- Blocking operations
- Large file reads
- Network latency

**Solutions:**
- Use async I/O
- Batch operations
- Use streaming
- Cache results

## References

- [Python Profiling Guide](https://docs.python.org/3/library/profile.html)
- [Profiling Best Practices](https://www.oreilly.com/library/view/high-performance-python/9781449361747/)

