# API Performance Optimization

## Overview

API performance directly impacts user experience and system scalability. This guide covers API optimization techniques, best practices, and patterns.

## Response Time Optimization

### Reduce Latency

**Strategies:**
- Minimize processing time
- Use efficient algorithms
- Cache responses
- Optimize database queries
- Use CDN for static content

### Async Processing

**Non-Blocking Operations:**
- Process long-running tasks asynchronously
- Return immediately with job ID
- Poll for completion
- Use webhooks for notifications

**Example:**
```python
@app.post("/process")
async def process_data(data: Data):
    # Start async job
    job_id = start_processing(data)
    return {"job_id": job_id, "status": "processing"}

@app.get("/process/{job_id}")
async def get_status(job_id: str):
    status = get_job_status(job_id)
    return {"job_id": job_id, "status": status}
```

## Caching Strategies

### Response Caching

**Cache API Responses:**
- Cache GET requests
- Use ETags for validation
- Set appropriate Cache-Control headers
- Invalidate on updates

**Example:**
```python
from functools import lru_cache
from cachetools import TTLCache

# Cache with TTL
cache = TTLCache(maxsize=1000, ttl=3600)

@app.get("/users/{user_id}")
async def get_user(user_id: int):
    # Check cache
    if user_id in cache:
        return cache[user_id]
    
    # Fetch from database
    user = db.get_user(user_id)
    
    # Cache result
    cache[user_id] = user
    return user
```

### HTTP Caching Headers

**Set Appropriate Headers:**
```python
from fastapi import Response

@app.get("/data")
async def get_data(response: Response):
    data = fetch_data()
    
    # Set cache headers
    response.headers["Cache-Control"] = "public, max-age=3600"
    response.headers["ETag"] = generate_etag(data)
    
    return data
```

## Pagination

### Efficient Pagination

**Cursor-Based Pagination:**
- Use cursor instead of offset
- Better performance
- Consistent results

**Example:**
```python
@app.get("/items")
async def get_items(cursor: Optional[str] = None, limit: int = 20):
    if cursor:
        items = db.get_items_after(cursor, limit)
    else:
        items = db.get_items(limit)
    
    next_cursor = items[-1].id if items else None
    
    return {
        "items": items,
        "next_cursor": next_cursor
    }
```

**Offset-Based Pagination:**
- Simple to implement
- Works for small datasets
- Performance degrades with offset

## Compression

### Response Compression

**Reduce Payload Size:**
- Enable gzip/brotli compression
- Reduce network transfer time
- Trade CPU for bandwidth

**Example:**
```python
from fastapi.middleware.gzip import GZipMiddleware

app.add_middleware(GZipMiddleware, minimum_size=1000)
```

## Rate Limiting

### Protect APIs

**Prevent Abuse:**
- Limit requests per user/IP
- Protect backend resources
- Fair resource allocation

**Example:**
```python
from slowapi import Limiter

limiter = Limiter(key_func=get_remote_address)

@app.get("/api/data")
@limiter.limit("10/minute")
async def get_data(request: Request):
    return fetch_data()
```

## Database Optimization

### Query Optimization

**Efficient Database Access:**
- Use indexes
- Avoid N+1 queries
- Use connection pooling
- Batch operations

### Read Replicas

**Distribute Read Load:**
- Use replicas for reads
- Master for writes
- Reduce master load

## API Design

### RESTful Design

**Best Practices:**
- Use appropriate HTTP methods
- Resource-based URLs
- Stateless design
- Version APIs

### GraphQL Optimization

**Query Optimization:**
- Use DataLoader for batching
- Implement field-level caching
- Limit query depth
- Use persisted queries

## Monitoring

### Key Metrics

**Track Performance:**
- Response time (p50, p95, p99)
- Request rate
- Error rate
- Cache hit rate
- Database query time

### APM Tools

**Monitoring Tools:**
- New Relic
- Datadog
- AppDynamics
- Custom dashboards

## Best Practices

1. **Cache Aggressively**: Cache frequently accessed data
2. **Use Pagination**: Limit result sets
3. **Compress Responses**: Reduce payload size
4. **Optimize Queries**: Efficient database access
5. **Monitor Performance**: Track key metrics
6. **Rate Limit**: Protect APIs
7. **Use CDN**: Distribute static content
8. **Async Processing**: Handle long-running tasks

## Common Issues

### Slow Responses

**Causes:**
- Inefficient queries
- No caching
- Synchronous processing
- Large payloads

**Solutions:**
- Optimize queries
- Add caching
- Use async processing
- Compress responses

### High Error Rates

**Causes:**
- Resource exhaustion
- Database issues
- External service failures
- Invalid requests

**Solutions:**
- Add rate limiting
- Optimize resources
- Handle errors gracefully
- Validate input

## References

- [REST API Best Practices](https://restfulapi.net/)
- [API Performance Optimization](https://www.nginx.com/blog/api-performance-optimization/)

