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dataflowkitvsgracy

BSD-3-Clause 4 3 641
Feb 09 2017 2024-04-04(a day ago)
235 2 1 MIT
1.33.0(a month ago) Feb 05 2023 917 (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.
  • Sัraping 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.

Gracy is an API client library based on httpx that provides an extra stability layer with:

  • Retry logic
  • Logging
  • Connection throttling
  • Tracking/Middleware

In web scraping, Gracy can be a convenient tool for creating scraper based API clients.

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.
# 0. Import
import asyncio
from typing import Awaitable
from gracy import BaseEndpoint, Gracy, GracyConfig, LogEvent, LogLevel

# 1. Define your endpoints
class PokeApiEndpoint(BaseEndpoint):
    GET_POKEMON = "/pokemon/{NAME}" # ๐Ÿ‘ˆ Put placeholders as needed

# 2. Define your Graceful API
class GracefulPokeAPI(Gracy[str]):
    class Config:  # type: ignore
        BASE_URL = "https://pokeapi.co/api/v2/" # ๐Ÿ‘ˆ Optional BASE_URL
        # ๐Ÿ‘‡ Define settings to apply for every request
        SETTINGS = GracyConfig(
          log_request=LogEvent(LogLevel.DEBUG),
          log_response=LogEvent(LogLevel.INFO, "{URL} took {ELAPSED}"),
          parser={
            "default": lambda r: r.json()
          }
        )

    async def get_pokemon(self, name: str) -> Awaitable[dict]:
        return await self.get(PokeApiEndpoint.GET_POKEMON, {"NAME": name})

    # Note: since Gracy is based on httpx we can customized the used client with custom headers etc"
    def _create_client(self) -> httpx.AsyncClient:
        client = super()._create_client()
        client.headers = {"User-Agent": f"My Scraper"} 
        return client

pokeapi = GracefulPokeAPI()

async def main():
    try:
      pokemon = await pokeapi.get_pokemon("pikachu")
      print(pokemon)

    finally:
        pokeapi.report_status("rich")


asyncio.run(main())

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