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dataflowkitvsferret

BSD-3-Clause 4 3 645
Feb 09 2017 2024-05-11(a month ago)
5,641 7 52 Apache-2.0
Aug 06 2019 58.1 thousand (month) v0.18.0(1 year, 2 months ago)

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.
  • S—Āraping 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.

Ferret is a web scraping system. It aims to simplify data extraction from the web for UI testing, machine learning, analytics and more. ferret allows users to focus on the data. It abstracts away the technical details and complexity of underlying technologies using its own declarative language. It is extremely portable, extensible, and fast.

Features

  • Declarative language
  • Support of both static and dynamic web pages
  • Embeddable
  • Extensible

Ferret is always implemented in Python through pyfer

Example Use


Dataflowkit uses JSON configuration like:
{
  "name": "collection",
  "request": {
      "url": "https://example.com"
  },
  "fields": [
      {
          "name": "Title",
          "selector": ".product-container a",
          "extractor": {
              "types": [
                  "text",
                  "href"
              ],
              "filters": [
                  "trim",
                  "lowerCase"
              ],
              "params": {
                  "includeIfEmpty": false
              }
          }
      },
      {
          "name": "Image",
          "selector": "#product-container img",
          "extractor": {
              "types": [
                  "alt",
                  "src",
                  "width",
                  "height"
              ],
              "filters": [
                  "trim",
                  "upperCase"
              ]
          }
      },
      {
          "name": "Buyinfo",
          "selector": ".buy-info",
          "extractor": {
              "types": [
                  "text"
              ],
              "params": {
                  "includeIfEmpty": false
              }
          }
      }
  ],
  "paginator": {
      "selector": ".next",
      "attr": "href",
      "maxPages": 3
  },
  "format": "json",
  "fetcherType": "chrome",
  "paginateResults": false
}
which is then ingested through CLI command.
// Example scraper for Google in Ferret:
LET google = DOCUMENT("https://www.google.com/", {
    driver: "cdp",
    userAgent: "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.87 Safari/537.36"
})

HOVER(google, 'input[name="q"]')
WAIT(RAND(100))
INPUT(google, 'input[name="q"]', @criteria, 30)
WAIT(RAND(100))
CLICK(google, 'input[name="btnK"]')

WAITFOR EVENT "navigation" IN google

WAIT_ELEMENT(google, "#res")

LET results = ELEMENTS(google, X("//*[text() = 'Search Results']/following-sibling::*/*"))

FOR el IN results
    RETURN {
        title: INNER_TEXT(el, 'h3')?,
        description: INNER_TEXT(el, X("//em/parent::*")),
        url: ELEMENT(el, 'a')?.attributes.href
    }

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