Skip to content

dataflowkitvsralger

BSD-3-Clause 4 3 641
Feb 09 2017 2024-04-04(a day ago)
153 1 3 MIT
2.2.4(3 years ago) Dec 22 2019 1.2 thousand (month)

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.

ralger is a small web scraping framework for R based on rvest and xml2.

It's goal to simplify basic web scraping and it provides a convenient and easy to use API.

It offers functions for retrieving pages, parsing HTML using CSS selectors, automatic table parsing and auto link, title, image and paragraph extraction.

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.
library("ralger")

url <- "http://www.shanghairanking.com/rankings/arwu/2021"

# retrieve HTML and select elements using CSS selectors:
best_uni <- scrap(link = url, node = "a span", clean = TRUE)
head(best_uni, 5)
#>  [1] "Harvard University"
#>  [2] "Stanford University"
#>  [3] "University of Cambridge"
#>  [4] "Massachusetts Institute of Technology (MIT)"
#>  [5] "University of California, Berkeley"

# ralger can also parse HTML attributes
attributes <- attribute_scrap(
  link = "https://ropensci.org/",
  node = "a", # the a tag
  attr = "class" # getting the class attribute
)

head(attributes, 10) # NA values are a tags without a class attribute
#>  [1] "navbar-brand logo" "nav-link"          NA
#>  [4] NA                  NA                  "nav-link"
#>  [7] NA                  "nav-link"          NA
#> [10] NA
#

# ralger can automatically scrape tables:
data <- table_scrap(link ="https://www.boxofficemojo.com/chart/top_lifetime_gross/?area=XWW")

head(data)
#> # A tibble: 6 × 4
#>    Rank Title                                      `Lifetime Gross`  Year
#>   <int> <chr>                                      <chr>            <int>
#> 1     1 Avatar                                     $2,847,397,339    2009
#> 2     2 Avengers: Endgame                          $2,797,501,328    2019
#> 3     3 Titanic                                    $2,201,647,264    1997
#> 4     4 Star Wars: Episode VII - The Force Awakens $2,069,521,700    2015
#> 5     5 Avengers: Infinity War                     $2,048,359,754    2018
#> 6     6 Spider-Man: No Way Home                    $1,901,216,740    2021

Alternatives / Similar