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A scalable headless data fetching library written with python and message queue service to enable quickly and easily prasing web data in a distributive way.

Project description

pifetcher

A scalable headless data fetching library written with python and message queue service to enable quickly and easily prasing web data in a distributive way.

dependencies:

  • selenium
  • BeautifulSoup4
  • boto3 (optional but by default)
  • ChromeDriver for chrome 76(by default)
  • Chrome executable v 76(by default)

how to use:

  1. set up work queue component on the host computer(aws simple queue service by default), such as credentials, regions AWS BOTO3 initial set up docs

  2. configure a fetcher by creating a field mapping config file, for example: create a mapping config file for fetching amazon.com item pricing data

{
    "price": {
        "type": "text",
        "selector": "#priceblock_ourprice",
        "attribute":".text"
    },
    "id": {
        "type": "text",
        "selector": "#ASIN",
        "attribute": "value"
    },
    "title": {
        "type": "text",
        "selector": "#productTitle",
        "attribute":".text"
    }
}
  1. create a pifetcherConfig.json file, and add the fetcher mapping file that previously created to fetcher -> mappingConfigs with its name and file path
    "browser":{
        "browser_options":["--window-size=1920,1080", "--disable-extensions", "--proxy-server='direct://'", "--proxy-bypass-list=*", "--start-maximized","--ignore-certificate-errors", "--headless"],
        "win-driver_path":"chromedriver-win-76.exe",
        "win-binary_location": "",
        "mac-driver_path":"chromedriver-mac-76",
        "mac-binary_location": ""

    },
    "queue":
    {
        "num_works_per_time": 1,
        "queue_type":"AWSSimpleQueueService",
        "queue_name":"datafetch.fifo"
    },
    "logger":
    {
        "output":"console"
    },
    "fetcher":
    {
        "mappingConfigs":{
            "amazon":"amazon.json"
        }

    }
}
  1. to use the fetcher worker
  • import the fetcher worker class and config class
from pifetcher.core import Config
from pifetcher.core import FetchWorker
  • load the pifetherConfig.json to the Config class
Config.use('pifetcherConfig.json')
  • implement event function with your own logic on_save_result : this will be called when a data object has been successfully parsed on_empty_result_error: this will be called after parsing an empty object, you may want to stop/ pause the process to investigate the problem before continuing parsing on_start_process_signal: this will be called when the worker received a start process signaal , you may implement your logic of adding fetching tasks to the queue here

example:

class TestWorker(FetchWorker):
    def on_save_result(self, results):
        print(results)
    def on_empty_result_error(self):
        self.stop()
    def on_start_process_signal(self):
        work = {}
        work['url'] = 'a amazon url'
        work['fetcher_name'] = 'amazon'
        self.add_works([work])
  1. Run the worker and, send a StartProcess Signal to the queue to start the process
  • start the worker to receive and process works
tw = TestWorker()
tw.do_works()
  • to send a start signal to the queue
    sqs = boto3.resource('sqs')
    queue = sqs.get_queue_by_name(QueueName='datafetch.fifo')
    content = {"type":"StartProcess","content":{}}
    queue.send_message(MessageBody=json.dumps(content), MessageGroupId = "FetchWork", MessageDeduplicationId = str(time.time()))

To do list items:

  • simplify initial setup process

Completed items

  • put all constants in config the config file (checked)
  • complete the type conversions for different data types (checked)
  • add message type (work initiation message type) (checked)
  • logging (checked)
  • data fetching with use of aws sqs

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