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Reliable scraping is less about finding a clever parser than controlling what you fetch, handling failures visibly, and checking that the data you save is still the data you intended to collect. I start by confirming there is no documented API or export, checking the site’s crawler guidance and separate terms or permissions, then making bounded requests and validating every result.
Choose the client that fits the job
Python’s standard library includes URL handling, HTTP request and error modules, and urllib.robotparser. Requests offers a higher-level HTTP interface with sessions, connection pooling, timeouts, streaming, and response handling. Scrapy adds crawler-oriented request and response abstractions and framework controls. None of those choices makes a scraper reliable by itself.
| Option | Good fit | What it provides |
|---|---|---|
| Python urllib | Small scripts or a preference to use the standard library | URL and HTTP modules, plus a robots parser; no separate package is needed. |
| Requests | Scripts that benefit from a straightforward HTTP client interface | Sessions, connection pooling, timeouts, streaming, and response handling. |
| Scrapy | Crawler workflows that need framework-level request and response handling | Crawler abstractions and controls, including retry controls. |
Choose based on workflow scale, session and connection needs, crawl scheduling, and implementation overhead. The documentation does not establish a universal speed or reliability winner.
Check access and crawler guidance before fetching
First identify the exact pages and fields you need. Look for an API, export, or another documented access route before scraping HTML. Then inspect the site’s robots.txt for your crawler identity and target paths. Python’s RobotFileParser can check whether a user agent may fetch a URL and read crawl-delay or request-rate fields when they are present.
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Robots rules are crawler guidance, not a grant of permission. RFC 9309, published by the IETF in September 2022, says: “These rules are not a form of access authorization.” Site terms and applicable law remain separate questions and depend on the site, data, jurisdiction, and purpose.
RFC 9309 also distinguishes a successfully fetched, parseable robots file from unavailable and unreachable cases. It recommends not using a cached robots file for more than 24 hours unless it is unreachable. If you implement robots handling, account for those distinctions rather than treating every fetch error as permission to proceed.
Make requests controlled and diagnosable
Set an explicit timeout for every network request. Both urllib.request.urlopen and Requests document timeout support; in urllib, it bounds blocking operations such as connection attempts. A timeout prevents a stalled request from holding up a run indefinitely, but it does not guarantee a response.
Use a descriptive user agent where appropriate, low concurrency, and delays that respect the site’s guidance and observed load. Scrapy’s AutoThrottle adjusts download delays using response latency. Whatever client you use, avoid turning a data collection task into an uncontrolled burst of traffic.
Retries should be bounded and limited to transient failures. Scrapy documents retry controls, including per-request metadata. A retry may help with a temporary network problem; it cannot repair a changed page layout, missing data, or persistent blocking. Record the URL, status or error, and timing so that a failed fetch is visible rather than silently dropped.
Validate the response before parsing
A successful network call does not prove you received the expected page. Before extracting fields, inspect the status, headers, redirects, response size, and content. In particular, check whether the content type and body match what your parser expects: an error page, login screen, or changed redirect can otherwise produce empty or misleading output.
Parse only the fields you need, then validate the resulting records. I treat missing required values, duplicate records, unexpected shapes, and implausible record counts as signals to investigate—not as rows to quietly accept. The client documentation describes response and error handling; these validation checks are engineering practices, not guarantees supplied by a library.
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- Define scope: Write down the target pages, required fields, and the expected record shape. Check for a documented API or export first.
- Review crawler rules and permission: Check the relevant
robots.txtpaths for your user agent, and assess terms and other authorization questions separately. - Set request limits: Choose a client, explicit timeouts, low concurrency, and a delay that respects site guidance and server load.
- Fetch and inspect: Record status, headers, redirects, response size, and content before attempting extraction.
- Extract and check: Validate required fields, duplicates, record structure, and expected counts; keep failed URLs and their error details.
- Save provenance: Store checkpoints along with source URLs and fetch times so a run can be diagnosed and resumed.
- Recheck extraction: Test against representative saved pages and revisit those checks when the site’s structure or behavior changes.
This workflow makes collection failures easier to locate: request problems are distinguishable from parsing or validation problems, and a rerun does not have to rely on memory about what happened previously.
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