Open Source Web Crawler

A web crawler discovers and fetches pages by following links, sitemaps, or URL rules. A scraper usually extracts specific fields from known pages. Many systems do both, but the hard parts differ. Crawlers need frontier management, deduplication, politeness, and retry logic. Scrapers need stable selectors, parsing rules, and data validation. Know which problem dominates before choosing.

The license fee may be zero, but crawling consumes bandwidth, storage, CPU, memory, and engineering time. Browser-based crawling can be especially expensive because each page behaves like a small application. Budget for monitoring, queue storage, proxy or network capacity if needed, and ongoing parser fixes. The cheaper option is often the crawler that fails clearly and resumes cleanly.

Look beyond advertised concurrency. Check how it partitions work across domains, enforces per-host rate limits, stores the frontier, and recovers after a worker dies. A scalable crawler should resume without refetching everything, avoid duplicate work, and expose queue depth and failure counts. If adding workers breaks politeness or creates duplicate fetches, it will not scale safely.

For most legitimate use, yes. Robots.txt is the basic protocol sites use to state crawler preferences, including disallowed paths and crawl delays. You still need your own judgment about terms of service, privacy, copyright, and load. Choose a crawler that handles robots.txt consistently, caches it sensibly, and lets you audit decisions when a page was skipped or allowed.

JavaScript rendering changes the resource profile and failure modes. You may need a real browser engine, waiting rules, network-idle detection, cookie support, and control over blocked assets. Rendering also makes crawls slower and harder to reproduce. If the data is available in server-rendered HTML or structured endpoints, prefer that path. Use browser crawling only where it is actually required.

That depends on whether you need raw evidence, extracted records, or both. Raw responses preserve headers, status codes, redirects, and page bodies for later reprocessing. Extracted records are easier to query but can hide parser bugs. For serious recurring crawls, store enough metadata to explain when a page changed, why extraction failed, and which fetch produced each record.

Start by behaving predictably. Set a clear user agent, honor robots.txt, limit concurrency per host, back off on errors, and avoid retry storms. Watch HTTP status codes such as 429 and 503 as signals, not obstacles. Rotating network addresses without controlling request rate usually makes the crawler less trustworthy. Good politeness settings protect both the target site and your data quality.

It can be, but it raises the bar. The crawler must isolate credentials, avoid leaking cookies across jobs, and respect authorization boundaries. You also need to handle session expiration, multifactor workflows, and pages that trigger side effects. Prefer read-only accounts where possible. Keep audit logs of what was fetched under which identity, especially when crawling customer portals or internal systems.

They are central to crawl quality. The same content may appear under URLs with tracking parameters, trailing slashes, mixed casing, redirects, or calendar traps. Without canonicalization, a crawler can waste most of its time on duplicates. Look for configurable URL normalization, content hashing, redirect tracking, and rules for infinite spaces such as faceted search pages or generated archives.

At minimum, you want programmatic job control, crawl status, structured logs, and export hooks. Teams often need to send results into object storage, search indexes, queues, databases, or data pipelines. A useful API should let you start, pause, resume, and inspect crawls without shell access. Webhooks or event streams help when downstream processing should begin before the whole crawl finishes.

Migration depends on how much state you need to keep. Seed URLs and extraction rules are usually portable with cleanup. Crawl history, content hashes, retry state, and canonical URL decisions are harder because each crawler models them differently. Plan a parallel run on a known slice of sites, compare fetched URL counts and extracted records, then migrate schedules after the differences are understood.

Check whether you can keep operating without upstream changes. The risk is highest when the crawler depends on browser automation, modern TLS behavior, or site compatibility fixes. Favor designs with ordinary storage, documented configuration, and exportable state. If the code is understandable and the data is portable, you can patch, fork, or replace it without losing crawl history.