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Improve web data extraction by reusing stored responses while they are fresh, validating stale responses with HTTP validators, and pacing requests to suit each target site. Caching avoids unnecessary downloads and parsing; concurrency and delay control how quickly requests are sent. They solve different problems, and both need to match how current your extracted data must be.
How caching improves a data-extraction pipeline
An HTTP cache stores a response associated with a request and can reuse it while the response is fresh. That can avoid transferring the same response body and repeating downstream parsing. The benefit depends on whether the cache’s freshness rules fit the job: a long freshness period can reduce work but leave extracted data out of date, while a short one checks for updates more often. See MDN’s HTTP caching guide.
Think of a repeated fetch as three possible outcomes: reuse a fresh cached response, validate a stale response with the server, or fetch a new representation when the resource has changed. A development replay cache may deliberately return an earlier response for repeatable testing; that is different from a production cache that follows HTTP freshness rules.
Choose HTTP directives to match freshness needs
Cache directives determine whether responses can be stored and when a stored response can be reused. Apply them deliberately, and verify how your client or cache implementation interprets them.
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max-agesets a freshness lifetime. Choose a duration that reflects how quickly the data needs to reflect changes.no-cacheallows storage but requires validation before reuse. It does not mean that the response cannot be stored.no-storeprevents storage.privateindicates that a response is intended for a private cache rather than reuse by shared caches. Take particular care with personalized responses; see MDN’s discussion of cache behavior.
A cache policy is not a substitute for understanding the data. If the response varies by user, authorization, or other request details, ensure your cache does not serve one context’s content to another.
Use validators to avoid downloading unchanged pages
When a cached entry is stale, keep its validators and ask the server whether the representation has changed. An ETag is an identifier for a representation; send it back in If-None-Match. If the server confirms the resource is unchanged, it can return 304 Not Modified, allowing your extractor to reuse the stored body instead of retransmitting it. If the resource changed, the server returns a new representation. The alternative date-based pair is Last-Modified and If-Modified-Since. See MDN’s guide to conditional requests and its ETag reference.
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- Store the response body and its
ETagorLast-Modifiedvalue when available. - When the entry is no longer fresh, send
If-None-Matchwith the stored ETag, orIf-Modified-Sincewith the stored modification date. - On
304 Not Modified, retain and reuse the stored body while updating the cache metadata as appropriate for your client. - On a response containing a new representation, replace the stored body and validators.
A 304 response does not contain a replacement representation body. Your extraction code therefore needs access to the previously stored body; a validator alone is not enough to reconstruct the page.
Configure Scrapy’s cache for the job
Scrapy provides HTTP cache middleware, storage backends, and policies. Configure HTTPCACHE_STORAGE to select storage and HTTPCACHE_POLICY to select cache behavior. Its documentation describes filesystem and DBM storage, plus two policies with different purposes:
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- RFC2616 policy: HTTP-cache-aware behavior for cache directives and freshness.
- Dummy policy: useful for deterministic replay and development, but it treats requests as cached without HTTP cache-control awareness.
Choose the policy according to whether you need HTTP-aware production behavior or repeatable replay. Scrapy documentation is versioned; check the documentation matching the Scrapy version installed in your crawler. See Scrapy’s downloader middleware documentation.
Set request concurrency and delay by target
Cache policy governs reuse of response bytes. Crawler scheduling governs request rate. Increasing concurrency is not automatically faster: if a target site cannot tolerate the load, it may throttle requests, return errors, or block the crawler, making the overall job slower. Scrapy’s optimization guide recommends tuning concurrency and delay for the target rather than assuming higher concurrency will improve throughput. See Scrapy’s optimization guide.
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In Scrapy, the relevant settings include CONCURRENT_REQUESTS, CONCURRENT_REQUESTS_PER_DOMAIN, and DOWNLOAD_DELAY. Adjust them based on observed response latency, errors, and throttling, while keeping the required data freshness in view. There is no universally fastest setting established by these sources.
The cited Scrapy optimization guide says Scrapy does not act on robots.txt Crawl-delay and Request-rate directives. Where those directives apply, translate them into crawler settings and verify behavior against the documentation for your deployed Scrapy version.
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Cache robots.txt within the standard’s limits
RFC 9309 permits caching robots.txt, but says crawlers should not use a cached copy for more than 24 hours unless the file is unreachable. Do not treat an unavailable file and an unreachable file as interchangeable cases: the RFC specifies different response handling. In particular, when robots.txt is unreachable because of server or network errors, crawlers must assume complete disallow. Consult RFC 9309 for the exact rules for response codes and retrieval failures.
Measure performance without sacrificing freshness
There is no general cache-hit rate, request rate, or speed-up that can be promised for every extraction workload. Measure your own pipeline before and after a policy or scheduling change, using the same targets and freshness requirement. Useful operational measures include:
- Cache hit rate and bytes transferred.
- Response latency and extraction or parsing time.
- Error and throttle rates.
- Age of the extracted data when the job completes.
A change that transfers fewer bytes may still be unsuitable if it makes the output too old. Likewise, a faster request burst is not an improvement if it increases failures or triggers throttling.
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