Custom News and RSS Feed Parser Development
Gathering news from dozens of sources manually is an unacceptable waste of time. A parser automates this process: it fetches fresh entries, strips ads, removes duplicates, and stores them in a database. We have been handling such projects for over 5 years and have developed more than 30 parsers for media and aggregators. Our solutions handle up to 100 sources without performance loss, and we guarantee stable operation. At the core is choosing the optimal stack: Node.js with rss-parser for high throughput or Python with feedparser for flexible processing.
RSS (Really Simple Syndication) is one of the main content syndication formats used for automatic publication of news feeds. — Wikipedia
How Does a News and RSS Feed Parser Work?
A scheduler (cron or Celery Beat) triggers feed polling on a schedule. For each new item, the title, description, full text, date, tags, and author are extracted. The text is cleaned of ads and scripts via sanitize-html (Node) or bleach (Python) — data volume decreases by an average of 40%. Deduplication uses the feed's GUID or a URL hash: processed GUIDs are stored in Redis for fast duplicate filtering. If a source lacks a feed, we use an HTML parser with Cheerio or BeautifulSoup and manual CSS selectors. For JavaScript-rendered pages, we use Puppeteer.
| Format | Type | Complexity | Processing Speed |
|---|---|---|---|
| RSS 2.0 | XML | Low | ~500 feeds/min |
| Atom | XML | Low | ~500 feeds/min |
| JSON Feed | JSON | Low | ~600 feeds/min |
| HTML (no feed) | HTML | High | ~100 pages/min |
Why Are Deduplication and Content Cleaning Important?
Without deduplication, the database fills with copies of the same news within days. Storing GUIDs in Redis allows filtering duplicates in milliseconds. HTML cleaning removes ad inserts, scripts, and excess styles — article volume is reduced by 40%, speeding up loading and lowering storage costs.
How We Develop a Parser? Stages of Work
- Source analysis — examine feed structure and HTML, identify dynamic loading and anti-scraping mechanisms.
- Architecture design — choose stack (Node.js or Python), define task queue, database schema.
- Adapter development — modules for RSS, Atom, HTML, JSON Feed.
- Integration with your CMS via REST API or direct database writing (WordPress, Drupal, Laravel — ready adapters).
- Testing and monitoring — load tests, deduplication checks, Grafana setup, Telegram alerts.
- Deployment and documentation — deploy on your server, hand over code and instructions.
We also provide a 6-month code warranty and train your engineers to work with the parser.
What Technologies Are Used?
-
Node.jswithrss-parser/feedparser-promisedorPythonwithfeedparser. - Scheduler:
Cron/Celery Beat. - Database:
PostgreSQLwith full-text indextsvector. - GUID cache:
Redis. - For JS pages:
Puppeteer/Playwright.
| Criteria | Node.js + rss-parser | Python + feedparser |
|---|---|---|
| Processing speed | up to 500 feeds/min | up to 150 feeds/min |
| Ecosystem | npm, powerful streams | scikit-learn, NLTK |
| Memory consumption | low | medium |
Stack choice affects final performance: the Node.js architecture processes feeds 3 times faster than the Python equivalent for 50+ sources.
How Are Problems with Changing Source Structure Solved?
Sources may change HTML markup or XML schema. We implement a monitoring system that tracks parsing errors (e.g., a sharp drop in extracted items). When an alert triggers, an engineer promptly adjusts selectors or the adapter. In most cases, this takes no more than 2–3 hours.
Timelines and Costs
A basic version for 10–20 sources takes 3–4 working days. For complex projects (HTML parsing, custom filters, deployment), the timeline extends to 1–2 weeks. Cost is calculated individually after reviewing the sources. Contact us — we will prepare an estimate within one day.
Practice Example: Parsing 50+ Sources
For a news aggregator, we developed a parser processing 50 feeds every 15 minutes. After implementation, manual news collection dropped from 4 hours to 15 minutes per day, and processing time was reduced by 90%. HTML cleaning decreased stored data volume by 35%, and deduplication completely eliminated duplicates. The time savings equate to tens of about $9–13 in savings monthly.
How to Start?
Order a news parser development today. Get a free consultation — we'll explain how automation will save your budget. Contact us to discuss the project and receive a quality guarantee.