Scraping at Scale

When scraping grows from 100 pages to 100,000 pages,
simple scripts fail.

You need structure.

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Problems at scale

• crashes waste hours
• duplicate scraping
• memory overflow
• no resume support

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Core ideas

1. Split work into tasks
2. Use workers
3. Save progress
4. Resume on failure

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Simple worker pattern

from collections import deque

queue = deque(["url1", "url2", "url3"])

while queue:
    url = queue.popleft()
    print("Processing", url)

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Save checkpoints

done = set()

def process(url):
    print("Scrape", url)

for url in urls:
    if url in done:
        continue
    process(url)
    done.add(url)

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Use files or DB for progress

Store processed URLs so you can restart safely.

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Graph: scalable system

flowchart LR
  A[URL List] --> B[Queue]
  B --> C[Worker 1]
  B --> D[Worker 2]
  B --> E[Worker 3]
  C --> F[Storage]
  D --> F
  E --> F

Remember

• Always design for failure
• Save progress often
• Split work into small tasks