# Scalable APIs

### Architecture Level

Scalability is key for a FastAPI app to handle increasing traffic and maintain performance. Here's a step-by-step guide:

***

#### 1. **Use an ASGI Server**

* Deploy FastAPI with an ASGI server like **Uvicorn** or **Hypercorn** for production.
*   Use `--workers` to scale the number of worker processes.

    ```bash
    uvicorn app:app --host 0.0.0.0 --port 8000 --workers 4
    ```

***

#### 2. **Set Up Load Balancing**

* Use a load balancer (e.g., **NGINX**, **AWS ALB**, or **Google Load Balancer**) to distribute incoming traffic across multiple instances of your app.

***

#### 3. **Horizontal Scaling**

* Run multiple instances of your FastAPI app on different servers or containers.
* Tools to manage scaling:
  * **Kubernetes**: Orchestrates containers with auto-scaling.
  * **Docker Swarm**: A simpler alternative for managing containerized apps.
  * **AWS ECS** or **Azure AKS**: Managed container services.

***

#### 4. **Database Optimization**

* Use **connection pooling** to manage database connections efficiently (e.g., with **SQLAlchemy** or **asyncpg**).
* Implement **read replicas** for read-heavy workloads.
* Use **caching** for frequently accessed queries (e.g., **Redis**, **Memcached**).

***

#### 5. **Enable Caching**

* Cache static data or expensive computations using tools like:
  * **Redis**
  * **FastAPI’s dependency caching**
*   Example:

    ```python
    from fastapi_cache import FastAPICache
    from fastapi_cache.backends.redis import RedisBackend

    @app.get("/data")
    @cache(expire=60)
    async def get_data():
        return {"data": "cached response"}
    ```

***

#### 6. **Use Content Delivery Networks (CDN)**

* Serve static assets like images, CSS, or JavaScript through a CDN (e.g., **Cloudflare**, **AWS CloudFront**).

***

#### 7. **Optimize API Performance**

* Use asynchronous endpoints to handle I/O-bound operations.
* Optimize payload size with:
  * Compression (e.g., **GZip** middleware).
  * Pagination for large datasets.
*   Example for async:

    ```python
    @app.get("/items")
    async def get_items():
        await some_async_operation()
        return {"items": []}
    ```

***

#### 8. **Monitor and Auto-Scale**

* Use monitoring tools (e.g., **Prometheus**, **Grafana**, or **AWS CloudWatch**) to monitor performance.
* Configure auto-scaling based on CPU, memory usage, or request count.

***

#### 9. **Use a Task Queue for Background Jobs**

* Offload time-consuming tasks to a task queue like **Celery** or **Dramatiq**, backed by Redis or RabbitMQ.
*   Example:

    ```python
    from celery import Celery
    celery_app = Celery("tasks", broker="redis://localhost:6379/0")

    @celery_app.task
    def long_task(param):
        return f"Processed {param}"
    ```

***

#### 10. **API Gateway**

* Use an API Gateway (e.g., **AWS API Gateway** or **Kong**) for authentication, rate limiting, and request routing.

***

#### 11. **Containerization and CI/CD**

* Use Docker to containerize your app for consistent deployments.
* Set up a CI/CD pipeline (e.g., **GitHub Actions**, **GitLab CI**, or **Jenkins**) for automated testing and deployment.

***

#### 12. **Implement Rate Limiting**

*   Prevent abuse by rate-limiting requests with tools like **fastapi-limiter**:

    ```python
    from fastapi_limiter.depends import RateLimiter

    @app.get("/resource", dependencies=[Depends(RateLimiter(times=5, seconds=60))])
    async def read_resource():
        return {"message": "Welcome"}
    ```

***

#### 13. **Test Scalability**

* Use tools like **Locust** or **Apache JMeter** to simulate load and find bottlenecks.

By combining these strategies, your FastAPI app will be well-prepared to handle high traffic and scale effectively. Let me know if you need more details on any step!

### Code Level

Certain optimizations can only be implemented at the **code level** and are independent of infrastructure. These include improving application logic, optimizing resource usage, and ensuring efficient data handling. Here's a list of such code-level improvements:

***

#### 1. **Efficient Data Processing**

* Optimize database queries:
  * Avoid N+1 query problems.
  * Use indexing, proper JOINs, and SELECT only necessary columns.
* Use batch processing for bulk operations.
* Avoid unnecessary data loading into memory.

***

#### 2. **Asynchronous Programming**

* Use asynchronous functions (`async/await`) for I/O-bound tasks like database queries or API calls.
*   Example:

    ```python
    @app.get("/data")
    async def fetch_data():
        data = await some_async_db_call()
        return data
    ```

***

#### 3. **Dependency Injection**

* Use dependency injection in FastAPI to reuse expensive resources (e.g., database connections, configuration objects).
*   Example:

    ```python
    @app.on_event("startup")
    async def startup():
        app.state.db = await init_db()

    @app.get("/items")
    async def get_items(db=Depends(lambda: app.state.db)):
        return await db.fetch_all("SELECT * FROM items")
    ```

***

#### 4. **Code Profiling and Refactoring**

* Profile your code to identify bottlenecks using tools like **cProfile**, **py-spy**, or **line\_profiler**.
* Refactor inefficient logic:
  * Replace nested loops with vectorized operations or comprehensions.
  * Use generators for large data processing to reduce memory usage.

***

#### 5. **Caching Logic**

* Cache results of expensive computations or frequent queries within the code.
*   Use libraries like **functools.lru\_cache** for in-memory caching.

    ```python
    from functools import lru_cache

    @lru_cache(maxsize=100)
    def compute_expensive_operation(param):
        return param ** 2
    ```

***

#### 6. **Pagination**

* Implement pagination for APIs to avoid sending large datasets in a single response.
*   Example:

    ```python
    @app.get("/items")
    async def get_items(limit: int = 10, offset: int = 0):
        return await db.fetch_all(f"SELECT * FROM items LIMIT {limit} OFFSET {offset}")
    ```

***

#### 7. **Input Validation and Error Handling**

* Validate inputs at the endpoint level to prevent unnecessary processing.
*   Use Pydantic models for validation in FastAPI.

    ```python
    from pydantic import BaseModel

    class Item(BaseModel):
        name: str
        price: float

    @app.post("/items")
    async def create_item(item: Item):
        return item
    ```

***

#### 8. **Optimize Serialization**

* Use optimized libraries like **orjson** for JSON serialization instead of the default Python library.

```python
    import orjson
    from fastapi.responses import ORJSONResponse

    @app.get("/data", response_class=ORJSONResponse)
    async def get_data():
        return {"key": "value"}
```

***

#### 9. **Memory Management**

* Use **generators** to process large data streams instead of storing everything in memory.
*   Example:

    ```python
    def generate_large_data():
        for i in range(1_000_000):
            yield i
    ```

***

#### 10. **Avoid Code-Level Bottlenecks**

* Minimize use of global variables and ensure thread safety.
* Avoid blocking operations in asynchronous functions (e.g., file I/O, synchronous database calls).
* Replace recursive algorithms with iterative ones to avoid stack overflows.

***

#### 11. **Logging Optimization**

* Avoid logging excessively in performance-critical sections.
* Use structured logging (e.g., **loguru**) for better traceability.

***

#### 12. **Implement Rate Limiting or Throttling**

* Add custom logic for rate limiting or throttling within your code to control user abuse.
*   Example:

    ```python
    from collections import defaultdict
    import time

    request_counts = defaultdict(list)

    def rate_limit(user_id, limit, window):
        now = time.time()
        request_counts[user_id] = [t for t in request_counts[user_id] if t > now - window]
        if len(request_counts[user_id]) >= limit:
            return False
        request_counts[user_id].append(now)
        return True
    ```

***

#### 13. **Reduce Payload Size**

* Optimize API response payloads:
  * Compress responses with middleware like GZip.
  * Remove unnecessary fields or use concise field names in responses.

***

#### 14. **Optimize Business Logic**

* Simplify complex algorithms where possible.
* Replace brute force methods with efficient alternatives (e.g., binary search, hash maps).

***

#### 15. **Use Proper Exception Handling**

* Catch exceptions at appropriate levels to prevent crashes.
*   Customize exception handling for better user feedback and debugging.

    ```python
    pythonCopy codefrom fastapi import HTTPException

    @app.get("/item/{id}")
    async def get_item(id: int):
        item = await db.get_item(id)
        if not item:
            raise HTTPException(status_code=404, detail="Item not found")
        return item
    ```

***

By applying these code-level practices, you can achieve significant performance improvements that infrastructure changes alone may not address.
