dataflowkitvsphoton
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.
Photon is a Python library for web scraping. It is designed to be lightweight and fast, and can be used to extract data from websites and web pages. Photon can extract the following data while crawling:
- URLs (in-scope & out-of-scope)
- URLs with parameters (example.com/gallery.php?id=2)
- Intel (emails, social media accounts, amazon buckets etc.)
- Files (pdf, png, xml etc.)
- Secret keys (auth/API keys & hashes)
- JavaScript files & Endpoints present in them
- Strings matching custom regex pattern
- Subdomains & DNS related data
The extracted information is saved in an organized manner or can be exported as json.
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
}
from photon import Photon
#Create a new Photon instance
ph = Photon()
#Extract data from a specific element of the website
url = "https://www.example.com"
selector = "div.main"
data = ph.get_data(url, selector)
#Print the extracted data
print(data)
#Extract data from multiple websites asynchronously
urls = ["https://www.example1.com", "https://www.example2.com"]
data = ph.get_data_async(urls)