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To crawl a known list of URLs efficiently, bound and deduplicate the list first, then fetch it with controlled per-host concurrency, observe responses, and save enough state to resume without repeating work. There is no universal best worker count: the target’s tolerance and your crawler’s bottlenecks determine whether more concurrency helps.
Start with the right URL source and a bounded scope
If you already have the URLs, use that inventory rather than discovering the same pages link by link. When you need to assemble a list, consider whether the site’s sitemap, documented API, bulk export, or search endpoint gives you the required coverage more directly. Scrapy’s optimization guidance notes that an API, bulk export, or search endpoint can be faster for the operator and cheaper for the website than crawling pages (Scrapy optimization documentation).
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Before fetching anything, define which hosts are in scope, the maximum inventory size, the URL patterns you need, and whether redirects may take requests to a different host. A crawler should not follow a redirect outside the allowed host set by accident. Keep the scope finite; calendar links, filters, and other page-generated URLs can otherwise expand indefinitely.
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Normalize and deduplicate without changing meaning
Equivalent URLs can waste requests, but aggressive normalization can erase meaningful pages. Remove duplicate entries and only discard query parameters when you know they do not change page content, authorization, locale, pagination, or the requested result. Session identifiers and tracking parameters often create variants, while a filter or page number may represent a genuinely distinct resource.
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- Retain meaningful query parameters, locale variants, and pagination when the task requires them.
- Exclude unbounded calendar or filter patterns unless they are explicitly part of the crawl.
- Do not remove authorization-related values or assume that two URLs are equivalent based on appearance alone.
Google’s guidance discusses duplicate and parameterized URLs as factors in crawl efficiency; its recommendations concern Google’s systems and are not universal crawler behavior (Google crawl budget guidance; Google URL structure guidance).
Set concurrency per host, not just globally
A global worker limit alone can concentrate too many simultaneous requests on one domain. Partition the queue by host and enforce per-host concurrency and request-rate limits, alongside any overall cap. Use a bounded queue so a large inventory does not consume unbounded memory or disk.
Begin conservatively, then increase concurrency in small increments while observing status codes, response latency, retries, and whether the site returns challenge or ban pages. If 429 or 503 responses, retries, or latency rise, reduce the rate or pause. Excessive concurrency can trigger throttling or bans and may reduce useful throughput rather than improve it. Scrapy recommends choosing a limit with the target site’s tolerance in mind (Scrapy optimization documentation).
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Measure more than pages per second when comparing settings: track completeness, per-host request rate, errors and throttles, response latency, retry volume, and queue memory or disk use. There is no evidence-backed universal concurrency number that is right for every host and crawler.
Respect access rules and verify crawler behavior
Check the target’s terms and applicable access constraints, and inspect its robots.txt for the relevant host, protocol, and port. Robots rules are not authentication and do not by themselves grant permission to access a resource. RFC 9309 defines the Robots Exclusion Protocol, while individual crawler documentation explains implementation details (RFC 9309).
Do not assume that every crawler interprets the same directives the same way. Google documents support for user-agent, allow, disallow, and sitemap, but not crawl-delay. Scrapy says its robots middleware does not automatically act on Crawl-delay or Request-rate. Check the documentation for the crawler you actually run before relying on a directive (Google robots.txt guidance; Scrapy optimization documentation).
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Keep the downloader busy without overwhelming your machine
A known URL list can improve utilization because requests need not wait for a serial link-discovery chain. But queueing every request at once trades speed for scheduler memory and disk use. Feed work in bounded batches or through a bounded queue, sized for the resources available to the crawler.
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Persist progress and avoid downloading unchanged pages
Save crawl state and results incrementally so an interrupted run can resume rather than start over. For repeat crawls, keep response validators or content fingerprints and use conditional requests where supported and appropriate. A matching resource may be returned as 304 Not Modified, allowing a client with a cached representation to avoid downloading the page body again.
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For Google Search specifically, Google recommends keeping sitemaps current, avoiding long redirect chains, and improving server response speed; it describes reuse of a cached page representation after a 304 response. Those are Google-specific recommendations, not a guarantee that every custom crawler or server behaves identically (Google crawl budget guidance).
A practical crawl workflow
- Define the boundary: specify permitted hosts, URL patterns, maximum list size, and redirect policy.
- Collect the inventory: start with the supplied file or database; use a sitemap, API, export, or search endpoint when it is a better fit and allowed.
- Normalize carefully: deduplicate equivalent entries without deleting query parameters or variants that change the requested content.
- Partition and bound: queue work by host, cap per-host and global concurrency, and limit queued work to available memory and disk.
- Ramp up gradually: begin at a conservative rate and adjust using response codes, latency, retries, throttles, and completeness.
- Persist and resume: record completed work and cache validators or fingerprints if the crawl will be repeated.
- Inspect bottlenecks: distinguish target-side delay from slow callbacks, middleware, parsing, or pipelines.
Common problems and how to respond
- 429 or 503 responses increase: lower per-host concurrency or request rate, respect applicable access rules, and retry cautiously rather than immediately repeating a burst.
- Retries rise or ban/challenge pages appear: pause or reduce traffic and verify that the crawler is operating within the target’s constraints. Do not treat a challenge page as a successful content capture.
- More workers make the crawl slower: revert the last increase and compare latency, throttles, and local processing time. The target may be limiting traffic, or your event loop and callbacks may be saturated.
- The queue consumes too much memory or disk: stop preloading the entire inventory; use a bounded queue or batches and persist progress.
- The same content is fetched repeatedly: review URL normalization for safe deduplication and use validators or fingerprints for repeat runs.
- The crawl follows unexpected pages: tighten URL patterns and redirect policy, especially for calendar links, additive filters, and host changes.
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Frequently Asked Questions
How many concurrent requests should a crawler make?
There is no universal number. Set per-host limits conservatively and adjust them based on the host’s responses and your crawler’s measured bottlenecks.
Does robots.txt give permission to crawl a website?
No. Robots.txt communicates crawler rules; it is not authentication or blanket permission. Check the target’s terms and applicable constraints.
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