crawleevsdataflowkit
Crawlee is a modern web scraping and browser automation framework for JavaScript and TypeScript, built by Apify. It is the successor to the Apify SDK and provides a unified interface for building reliable web scrapers and crawlers that can scale from simple scripts to large-scale data extraction projects.
Crawlee supports multiple crawling strategies through different crawler classes:
- CheerioCrawler For fast, lightweight HTML scraping using Cheerio (no browser needed). Best for static pages.
- PlaywrightCrawler Uses Playwright for full browser automation. Handles JavaScript-rendered pages, SPAs, and complex interactions.
- PuppeteerCrawler Similar to PlaywrightCrawler but uses Puppeteer as the browser automation backend.
- HttpCrawler Minimal crawler for raw HTTP requests without HTML parsing.
Key features include:
- Automatic request queue management with configurable concurrency and rate limiting
- Built-in proxy rotation with session management
- Persistent request queue and dataset storage (local or cloud via Apify)
- Automatic retry and error handling with configurable strategies
- TypeScript-first design with full type safety
- Middleware-like request/response hooks (preNavigationHooks, postNavigationHooks)
- Output pipelines for storing extracted data
- Easy deployment to Apify cloud platform
Crawlee is considered the most feature-complete web scraping framework in the JavaScript/TypeScript ecosystem, comparable to Python's Scrapy but with native browser automation support.
Dataflow kit ("DFK") is a Web Scraping framework for Gophers. It extracts data from web pages, following the specified CSS Selectors. You can use it in many ways for data mining, data processing or archiving.
Web-scraping pipeline consists of 3 general components:
- Downloading an HTML web-page. (Fetch Service)
- Parsing an HTML page and retrieving data we're interested in (Parse Service)
- Encoding parsed data to CSV, MS Excel, JSON, JSON Lines or XML format.
For fetching dataflowkit has several types of page fetchers:
- Base fetcher uses standard golang http client to fetch pages as is. It works faster than Chrome fetcher. But Base fetcher cannot render dynamic javascript driven web pages.
- Chrome fetcher is intended for rendering dynamic javascript based content. It sends requests to Chrome running in headless mode.
For parsing dataflowkit extracts data from downloaded web page following the rules listed in configuration JSON file. Extracted data is returned in CSV, MS Excel, JSON or XML format.
Some dataflowkit features:
- Scraping of JavaScript generated pages;
- Data extraction from paginated websites;
- Processing infinite scrolled pages.
- Sсraping of websites behind login form;
- Cookies and sessions handling;
- Following links and detailed pages processing;
- Managing delays between requests per domain;
- Following robots.txt directives;
- Saving intermediate data in Diskv or Mongodb. Storage interface is flexible enough to add more storage types easily;
- Encode results to CSV, MS Excel, JSON(Lines), XML formats;
- Dataflow kit is fast. It takes about 4-6 seconds to fetch and then parse 50 pages.
- Dataflow kit is suitable to process quite large volumes of data. Our tests show the time needed to parse appr. 4 millions of pages is about 7 hours.