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Scraper cool down problem for multiple requests

Created by: Srikar-V675

First Solution

To tackle the issue of being blocked by the website due to multiple repeated attempts, you can implement several strategies. These strategies include rate limiting, using proxies, and implementing retries with exponential backoff. Here’s a detailed approach to managing these challenges:

1. Rate Limiting

Rate limiting helps to control the number of requests you make to the website within a specific time frame to avoid being blocked.

Implementation:

  • Python Implementation: You can use a library like ratelimit to enforce rate limits on your scraper functions.
from ratelimit import limits, sleep_and_retry
import time

# Define the rate limit (e.g., 10 requests per minute)
ONE_MINUTE = 60

@sleep_and_retry
@limits(calls=10, period=ONE_MINUTE)
def scrape_usn(usn, result_link):
    # Your scraping logic here
    pass

2. Using Proxies

Using proxies can help to distribute your requests across multiple IP addresses, reducing the chances of being blocked.

Implementation:

  • Using Proxy Rotation: You can use a proxy service or a list of proxies to rotate IP addresses.
import requests
from itertools import cycle

proxies = ["http://proxy1.com", "http://proxy2.com", "http://proxy3.com"]
proxy_pool = cycle(proxies)

def scrape_usn_with_proxy(usn, result_link):
    proxy = next(proxy_pool)
    response = requests.get(result_link, proxies={"http": proxy, "https": proxy})
    # Your scraping logic here
    pass

3. Exponential Backoff

Exponential backoff introduces a delay before retrying a failed request, with the delay time increasing exponentially.

Implementation:

  • Retry Logic: Use the tenacity library for implementing retries with exponential backoff.
from tenacity import retry, wait_exponential, stop_after_attempt

@retry(wait=wait_exponential(multiplier=1, min=4, max=60), stop=stop_after_attempt(5))
def scrape_usn_with_backoff(usn, result_link):
    # Your scraping logic here
    pass

4. Queueing System

Using a queueing system can help to manage and distribute scraping tasks effectively.

Implementation:

  • Celery with Redis: Use Celery for managing background tasks and Redis as the message broker.

  1. Install Celery and Redis:

    pip install celery redis

  2. Configure Celery:

    # celery_config.py
from celery import Celery

app = Celery('tasks', broker='redis://localhost:6379/0')

app.conf.update(
    task_routes={
        'tasks.scrape_usn': {'queue': 'scraping'},
    },
    task_serializer='json',
    accept_content=['json'],
    result_serializer='json',
    timezone='UTC',
    enable_utc=True,
)

  3. Define Celery Task:

    # tasks.py
from celery_config import app
import requests

@app.task
def scrape_usn(usn, result_link):
    response = requests.get(result_link)
    # Your scraping logic here
    return response.json()

  4. Queue Tasks:

    from tasks import scrape_usn

# Queue scraping tasks
scrape_usn.delay(usn, result_link)

Combining Strategies

Combining rate limiting, proxy rotation, and exponential backoff within a queueing system like Celery will provide a robust solution:

  1. Queue tasks in Celery with rate limiting:

    from celery_config import app
from ratelimit import limits, sleep_and_retry
import requests

ONE_MINUTE = 60

@app.task
@sleep_and_retry
@limits(calls=10, period=ONE_MINUTE)
def scrape_usn(usn, result_link):
    response = requests.get(result_link)
    # Your scraping logic here
    return response.json()

  2. Use proxies in Celery task:

    from celery_config import app
from itertools import cycle
import requests

proxies = ["http://proxy1.com", "http://proxy2.com", "http://proxy3.com"]
proxy_pool = cycle(proxies)

@app.task
def scrape_usn(usn, result_link):
    proxy = next(proxy_pool)
    response = requests.get(result_link, proxies={"http": proxy, "https": proxy})
    # Your scraping logic here
    return response.json()

By implementing these strategies, you can effectively manage the scraping tasks without overwhelming the target website, thus minimizing the risk of getting blocked.

Second Solution

Handling multiple simultaneous requests to the extract_results endpoint and preventing a "too many requests" error requires a strategy to manage the rate limits at both the per-request and global levels. Here are some strategies you can implement:

  1. Global Rate Limiting: Implement a global rate limit for the endpoint to ensure the total number of requests across all users doesn’t exceed the allowed rate.

  2. Per-User Rate Limiting: Implement per-user rate limits to ensure individual users don’t overwhelm the system with too many requests.

  3. Centralized Throttling and Queueing: Use a centralized queue system to manage and throttle the requests, ensuring they are processed at a controlled rate.

Implementing Global and Per-User Rate Limiting in FastAPI

  1. Install Dependencies:

    pip install fastapi-limiter aioredis

  2. Setup Redis for Rate Limiting: FastAPI-Limiter uses Redis to keep track of request counts and time windows.

  3. Update Your FastAPI Application:

Here’s how you can set up global and per-user rate limiting in your FastAPI application:

from fastapi import FastAPI, BackgroundTasks, Depends, HTTPException, Request
from fastapi_limiter import FastAPILimiter
from fastapi_limiter.depends import RateLimiter
from sqlalchemy.orm import Session
from concurrent.futures import ThreadPoolExecutor
import time
import requests
from tenacity import retry, wait_exponential, stop_after_attempt, retry_if_exception_type
import aioredis

class TooManyRequestsException(Exception):
    pass

# Mock function to simulate a scraper that may hit rate limits
def mock_scraper(usn):
    response = requests.get(f"https://example.com/data/{usn}")
    if response.status_code == 429:
        raise TooManyRequestsException("Too many requests")
    return response.json()

@retry(
    retry=retry_if_exception_type(TooManyRequestsException),
    wait=wait_exponential(min=1, max=60),
    stop=stop_after_attempt(5)
)
def scrape_and_store(section_id: int, usn_subset: list, db: Session):
    for usn in usn_subset:
        try:
            data = mock_scraper(usn)
            print(f"Stored data for {usn}")
        except TooManyRequestsException:
            print(f"Rate limit hit for {usn}, retrying...")
        time.sleep(1)

# Mock database session and function
def get_db():
    pass

class Section:
    id: int
    usn_range: list

def generate_usn_range(usn_range):
    return usn_range

app = FastAPI()

@app.on_event("startup")
async def startup():
    redis = await aioredis.create_redis_pool("redis://localhost")
    await FastAPILimiter.init(redis)

@app.post("/extract/{section_id}", dependencies=[Depends(RateLimiter(times=5, seconds=60))])  # Global rate limit
async def extract_results(section_id: int, request: Request, background_tasks: BackgroundTasks, db: Session = Depends(get_db)):
    section = db.query(Section).filter(Section.id == section_id).first()
    if not section:
        raise HTTPException(status_code=404, detail="Section not found")

    usn_range = generate_usn_range(section.usn_range)

    num_threads = 4
    subset_size = len(usn_range) // num_threads
    usn_subsets = [usn_range[i:i + subset_size] for i in range(0, len(usn_range), subset_size)]

    def process_subsets():
        with ThreadPoolExecutor(max_workers=num_threads) as executor:
            futures = [executor.submit(scrape_and_store, section_id, subset, db) for subset in usn_subsets]
            for future in futures:
                future.result()

    background_tasks.add_task(process_subsets)
    return {"message": "Extraction process started"}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="127.0.0.1", port=8000)

Explanation:

  1. Rate Limiting Dependency:

    • RateLimiter(times=5, seconds=60): This limits the endpoint to 5 requests per 60 seconds globally. Adjust these values based on your requirements.

  2. FastAPI-Limiter:

    • FastAPILimiter: This library is used to handle rate limiting in FastAPI. It requires Redis to keep track of the request counts and time windows.

  3. Redis Setup:

    • Ensure Redis is running on your system. You can install Redis using package managers like apt on Linux, brew on macOS, or download directly from the Redis website.

  4. Startup Event:

    • During the startup of your FastAPI application, initialize the Redis connection and FastAPI-Limiter.

  5. Background Task Handling:

    • The process_subsets function manages the multithreading for the usn_subsets, similar to the previous example, with retry logic to handle rate limits.

Handling Multiple Requests

By implementing the rate limiter, you ensure that the number of requests to the extract_results endpoint is controlled. The retry logic with exponential backoff further ensures that if any request encounters a rate limit, it will wait and retry without overwhelming the target server.

For more advanced use cases, consider a distributed task queue system like Celery with Redis or RabbitMQ, which allows better control over task execution and retries.