dataflowkitvsscrapyd
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.
Scrapyd is a service for running Scrapy spiders. It allows you to schedule spiders to run at regular intervals and also allows you to run spiders on remote machines. It is built in Python, and it is meant to be used in a server-client architecture, where the scrapyd server runs on a remote machine, and clients can schedule and control spider runs on the server using an HTTP API. With Scrapyd, you can schedule spider runs on a regular basis, schedule spider runs on demand, and view the status of running spiders.
You can also see the logs of completed spiders, and manage spider settings and
configurations. Scrapyd also provides an API that allows you to schedule spider runs, cancel spider
runs, and view the status of running spiders.
You can install the package via pip by running pip install scrapyd and
then you can run the package by running scrapyd command in your command prompt.
By default, it will start a web server on port 6800, but you can specify a different port using the
`--port`` option.
Scrapyd is a good solution if you need to run Scrapy spiders on a remote machine, or if you need to schedule spider runs on a regular basis. It's also useful if you have multiple spiders, and you need a way to manage and monitor them all in one place.
for more web interface see scrapydweb
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
}
$ scrapyd
$ curl http://localhost:6800/schedule.json -d project=myproject -d spider=spider2