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

```python from collections import deque

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

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

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Save checkpoints

```python 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

```mermaid 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