Secure Your Feeds: Best Practices for a Web2RSS Proxy

Build a Fast Web2RSS Proxy — Step‑by‑Step GuideA Web2RSS proxy converts web pages (Web2) into RSS feeds so users can follow sites that don’t publish feeds natively. A fast, reliable Web2RSS proxy caches content, minimizes latency, handles site differences, and resists breakage when HTML changes. This guide walks through design decisions, implementation steps, optimization, and deployment so you can build a high-performance, maintainable Web2RSS proxy.

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Why build a Web2RSS proxy?

• Aggregates content from sites without RSS.
• Enables feed readers to follow dynamic or JavaScript-heavy pages.
• Centralizes scraping logic and caching for many users.
• Improves privacy by acting as an intermediary between client and origin sites.
• Customizes feeds (filtering, deduplication, normalization).

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Design overview

A Web2RSS proxy typically consists of these components:

• Ingress API: receives feed requests (e.g., /rss?url=…).
• Fetcher: retrieves page HTML, possibly executing JS.
• Parser/Extractor: extracts title, date, content, links, and other metadata.
• Transformer: normalizes items into RSS/Atom XML.
• Cache layer: stores parsed feed data and raw HTML for TTL.
• Rate limiter & queue: protects origin sites and backend.
• Storage (optional): for saved user preferences, custom rules.
• Monitoring & logging: performance, errors, site-specific failures.

Key goals: low latency, high uptime, modest resource use, resilience to HTML changes.

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Step 1 — Choose fetching strategy

Options:

1. Headless browser (Puppeteer / Playwright)
   • Pros: runs JS, perfect for SPA sites.
   • Cons: heavy CPU/memory, higher latency.
2. HTTP client + HTML parser (requests, fetch)
   • Pros: light, fast for static pages.
   • Cons: fails on client‑rendered content.
3. Hybrid: try lightweight fetch first, fall back to headless if needed.
   • Best balance for performance.

Recommendation: implement hybrid fetching. Use a fast HTML fetch by default and only use a headless browser when the page appears to require JS or when a user opts in.

Implementation notes:

• Use HTTP client with good concurrency (e.g., node-fetch, axios for Node; aiohttp for Python).
• Respect robots.txt and site rate limits.
• Set sensible timeouts (e.g., 10s lightweight fetch, 30s headless).

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Step 2 — Parsing and extraction

Parsing goals: identify list of items, extract title, link, publication date, summary/content, and media.

Approaches:

• Heuristics + libraries: Readability, Mercury (deprecated but ideas useful), Newspaper3k (Python).
• CSS/XPath rules per-site: most accurate but requires maintenance.
• ML-based extractor: can generalize but complex to build and run.

Practical strategy:

• Implement layered parsing:
  1. Try feed discovery (link rel=“alternate” type=“application/rss+xml”) — if present, proxy that feed directly.
  2. Try built-in generic extractors (Readability-style) to get main article content and convert paginated lists into items.
  3. Fallback to site-specific rules (store CSS/XPath selectors in a rules DB).
  4. Optionally allow user-provided CSS/XPath.

For lists (like blogs, news index pages):

• Detect repeated DOM structures: same tag/class repeated — treat each as an item.
• Extract title, href, timestamp (look for time tags, datetime attributes, human texts).
• If no timestamp, synthesize using crawl time and deduplicate later.

Libraries to consider:

• Node: cheerio, jsdom, @mozilla/readability.
• Python: BeautifulSoup, lxml, newspaper3k.

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Step 3 — Transform to RSS/Atom

Canonicalize extracted items into a standard feed structure. Key fields:

• feed title, description, link
• item title, link (absolute), guid, pubDate, author, content:encoded or summary, enclosure (for media)

Rules:

• Use absolute URLs for links and media (resolve relative URLs).
• Normalize dates to RFC 822 for RSS or ISO 8601 for Atom.
• Provide unique GUIDs: stable hash of source URL + item URL + pubDate.
• Include content:encoded with sanitized HTML (see security below).

Example RSS item creation (conceptual):

• guid = sha1(site + itemPath + pubDate)
• pubDate = formatRFC822(parsedDate or crawlTime)

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Step 4 — Caching and freshness

Caching is vital for speed and reducing load.

Layers:

1. HTTP-level caching (ETag, Last-Modified): conditional requests to origin.
2. Raw HTML cache: store fetched HTML for N minutes/hours.
3. Parsed feed cache: store generated RSS XML for TTL (e.g., 5–30 minutes depending on site).
4. Per-item dedupe store: short-term store of item GUIDs to avoid duplicates.

Cache policies:

• High-traffic, fast-changing sites: short TTL (1–5 min).
• Static blogs: longer TTL (30–60 min).
• Honor Cache-Control headers from origin when possible.

Implement distributed cache (Redis, Memcached) for scale. Use local LRU in each instance for micro‑caching.

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Step 5 — Rate limiting, queuing, and politeness

Protect origin sites and your infrastructure.

• Global + per-domain rate limits.
• Concurrency limits per domain to avoid DoS.
• Backoff on repeated ⁄₅₀₃ responses from origin.
• Use task queue (RabbitMQ, BullMQ, Celery) for heavy fetches/headless jobs.
• Schedule slow-growth warm-up for new domains.

Include retry policies with exponential backoff and jitter.

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Step 6 — Security and sanitization

Feeds will be consumed by third-party readers; sanitize carefully.

• Sanitize HTML to remove scripts, inline event handlers, JavaScript URLs, and iframes (or sandbox them).
• Rewrite or proxy images/media URLs if you want to hide referer or cache media.
• Strip or rewrite forms and interactive elements.
• Enforce Content Security Policy when serving HTML previews.
• Protect against XML injection and CDATA misuse.

Use established sanitizers:

• DOMPurify (Node) for HTML sanitization.
• Bleach (Python) for whitelisting tags/attributes.

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Step 7 — Performance optimizations

• Serve pre-generated RSS XML from cache (fastest).
• Use HTTP/2 or HTTP/3 on the proxy for lower latency.
• Use gzip or brotli compression for feed responses.
• Use connection pooling and keep-alive for outbound HTTP.
• Batch small requests where possible.
• Use server-side rendering minimal headless instances for JS-heavy fetches and recycle them.
• Use CDN in front of your proxy for public feeds to reduce origin load.

Metrics to monitor: request latency, cache hit rate, headless job queue length, per-domain error rates.

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Step 8 — Handling JavaScript-heavy sites

When headless is required:

• Use Playwright for reliability and multi-browser options.
• Reuse browser contexts and pages to reduce startup cost.
• Use stealth plugins sparingly to reduce bot detection.
• Execute minimal JS: wait for network idle or specific DOM selector.
• Capture pre-rendered HTML snapshot and close page quickly.
• Consider rendering only the parts needed (e.g., fetch JSON XHR endpoints directly if possible).

Alternative: inspect network requests, find underlying JSON APIs the site uses, and build lightweight adapters.

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Step 9 — Monitoring and maintenance

• Site-specific failures are inevitable. Track per-site error trends and set alerts.
• Provide a way to add/update CSS/XPath rules for sites.
• Keep a small team or crowdsourced contributors to maintain selectors.
• Log parsing heuristics and sample failures for faster debugging.

Key alerts:

• Sudden drop in item counts for a site.
• Spike in parsing errors or headless fallbacks.
• Increased 4xx/5xx from origins.

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Step 10 — Deployment and scaling

Architecture sketch:

• Lightweight API layer (stateless) behind a load balancer.
• Worker fleet for fetch+parse jobs (can use autoscaling).
• Shared Redis for cache and rate-limiting tokens.
• Optional persistent DB for custom rules and user settings.
• CDN in front for public feed caching.

Autoscale workers based on queue length and headless job demand. Use container orchestration (Kubernetes, ECS) and observability (Prometheus + Grafana, or hosted alternatives).

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Developer checklist — quick summary

• [ ] Hybrid fetcher (HTTP client + headless fallback)
• [ ] Feed discovery and layered parsing (generic + per-site rules)
• [ ] Robust caching: raw HTML + parsed RSS + dedupe store
• [ ] Rate limiting and per-domain politeness
• [ ] HTML sanitization and link resolution
• [ ] Metrics, logging, and per-site monitoring
• [ ] Worker queue and autoscaling
• [ ] CDN and compression for responses
• [ ] Mechanism for site-specific rule updates

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Example: minimal Node.js flow (conceptual)

1. Receive /rss?url=…
2. If cached RSS exists and fresh → return cached XML.
3. Fetch page with fast HTTP client.
4. If content missing or JS required → enqueue headless render job.
5. Parse HTML to extract items.
6. Generate RSS XML, store in cache, return to client.

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Appendix — Practical tips & gotchas

• Time parsing is messy: prefer multiple libraries (date-fns, chrono) and fallback to crawl time.
• Some sites block scrapers aggressively. Use respectful client headers and caching to reduce requests.
• Beware legal/ToS concerns — check site policies if building a public service.
• Consider providing an opt-in developer API/key system to manage abuse.
• Provide user controls: item filters, max items, include/exclude domains.

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This guide gives the roadmap and practical details to build a fast, resilient Web2RSS proxy. If you want, I can: provide example code for the fetch+parse flow (Node or Python), a sample CSS/XPath rule schema, or a Kubernetes deployment manifest. Which would you like next?