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Python Web Scraping Cookbook

You're reading from   Python Web Scraping Cookbook Over 90 proven recipes to get you scraping with Python, microservices, Docker, and AWS

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787285217
Length 364 pages
Edition 1st Edition
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Author (1):
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Michael Heydt Michael Heydt
Author Profile Icon Michael Heydt
Michael Heydt
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Scraping 2. Data Acquisition and Extraction FREE CHAPTER 3. Processing Data 4. Working with Images, Audio, and other Assets 5. Scraping - Code of Conduct 6. Scraping Challenges and Solutions 7. Text Wrangling and Analysis 8. Searching, Mining and Visualizing Data 9. Creating a Simple Data API 10. Creating Scraper Microservices with Docker 11. Making the Scraper as a Service Real 12. Other Books You May Enjoy

Checking Elasticsearch for a listing before scraping

Now lets leverage Elasticsearch as a cache by checking to see if we already have stored a job listing and hence do not need to hit StackOverflow again. We extend the API for performing a scrape of a job listing to first search Elasticsearch, and if the result is found there we return that data. Hence, we optimize the process by making Elasticsearch a job listings cache.

How to do it

We proceed with the recipe as follows:

The code for this recipe is within 09/05/api.py. The JobListing class now has the following implementation:

class JobListing(Resource):
def get(self, job_listing_id):
print("Request for job listing with id: " + job_listing_id)

...
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