62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
import asyncio
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import json
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from datetime import timedelta
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from typing import Any
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from aws_lambda_powertools.utilities.typing import LambdaContext
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from crawlee.crawlers import BeautifulSoupCrawler, BeautifulSoupCrawlingContext
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from crawlee.storage_clients import MemoryStorageClient
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from crawlee.storages import Dataset, RequestQueue
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async def main() -> str:
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# highlight-start
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# Disable writing storage data to the file system
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storage_client = MemoryStorageClient()
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# highlight-end
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# Initialize storages
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dataset = await Dataset.open(storage_client=storage_client)
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request_queue = await RequestQueue.open(storage_client=storage_client)
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crawler = BeautifulSoupCrawler(
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storage_client=storage_client,
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max_request_retries=1,
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request_handler_timeout=timedelta(seconds=30),
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max_requests_per_crawl=10,
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)
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@crawler.router.default_handler
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async def request_handler(context: BeautifulSoupCrawlingContext) -> None:
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context.log.info(f'Processing {context.request.url} ...')
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data = {
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'url': context.request.url,
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'title': context.soup.title.string if context.soup.title else None,
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'h1s': [h1.text for h1 in context.soup.find_all('h1')],
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'h2s': [h2.text for h2 in context.soup.find_all('h2')],
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'h3s': [h3.text for h3 in context.soup.find_all('h3')],
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}
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await context.push_data(data)
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await context.enqueue_links()
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await crawler.run(['https://crawlee.dev'])
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# Extract data saved in `Dataset`
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data = await crawler.get_data()
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# Clean up storages after the crawl
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await dataset.drop()
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await request_queue.drop()
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# Serialize the list of scraped items to JSON string
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return json.dumps(data.items)
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def lambda_handler(_event: dict[str, Any], _context: LambdaContext) -> dict[str, Any]:
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result = asyncio.run(main())
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# Return the response with results
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return {'statusCode': 200, 'body': result}
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