dataflowkitvsruia
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.
Ruia is an async web scraping micro-framework, written with asyncio and aiohttp, aims to make crawling url as convenient as possible.
Ruia is inspired by scrapy however instead of Twisted it's based entirely on asyncio and aiohttp.
It also supports various features like cookies, headers, and proxy, which makes it very useful in dealing with complex web scraping tasks.
Example Use
{
"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
}
#!/usr/bin/env python
"""
Target: https://news.ycombinator.com/
pip install aiofiles
"""
import aiofiles
from ruia import AttrField, Item, Spider, TextField
class HackerNewsItem(Item):
target_item = TextField(css_select="tr.athing")
title = TextField(css_select="a.storylink")
url = AttrField(css_select="a.storylink", attr="href")
async def clean_title(self, value):
return value.strip()
class HackerNewsSpider(Spider):
start_urls = [
"https://news.ycombinator.com/news?p=1",
"https://news.ycombinator.com/news?p=2",
]
concurrency = 10
# aiohttp_kwargs = {"proxy": "http://0.0.0.0:1087"}
async def parse(self, response):
async for item in HackerNewsItem.get_items(html=await response.text()):
yield item
async def process_item(self, item: HackerNewsItem):
async with aiofiles.open("./hacker_news.txt", "a") as f:
self.logger.info(item)
await f.write(str(item.title) + "\n")
if __name__ == "__main__":
HackerNewsSpider.start(middleware=None)