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A single-process scraper is limited by one machine's CPU and network. To scale, you split work across many workers running in parallel, possibly on different servers.

A task queue is the glue that distributes work safely.

The queue pattern has two roles:

• Producers discover URLs and push tasks onto the queue.
• Consumers (workers) pull tasks and fetch the pages.

Decoupling them lets each side scale independently.

Redis lists make a simple, fast queue. Producers LPUSH URLs; workers BRPOP them, blocking until work is available.

An in-memory list only works within one process. A Redis queue is shared across processes and machines, persists if a worker crashes, and handles concurrency atomically.

Celery is a full task framework built on a broker like Redis. You define tasks as functions and call them asynchronously; workers pick them up automatically.

Calling .delay() enqueues the task and returns immediately. Workers running celery worker consume and execute them in parallel.

Celery can automatically retry failed tasks with backoff, so a transient network error does not lose a URL.

In distributed crawling the same URL can be discovered twice. Use a Redis set as a 'seen' filter so each page is fetched once.

Tune worker concurrency to match target-site politeness and your bandwidth. Too many workers overwhelm the site; too few leave the queue backed up. Monitor queue length to find balance.

Workers should write parsed data to a shared store (a database or object storage), not return it through the queue. The queue carries tasks; the datastore holds results.

Not all URLs are equal. Route urgent tasks (a category index that unlocks many child pages) to a high-priority queue so workers handle them before low-value pages.

You learned to scale scraping with task queues: the producer/consumer pattern, Redis-backed queues, Celery tasks with .delay(), automatic retries, URL deduplication with Redis sets, tuning concurrency, and writing results to shared storage.