Book Image

Python Web Scraping Cookbook

By : Michael Heydt
Book Image

Python Web Scraping Cookbook

By: Michael Heydt

Overview of this book

Python Web Scraping Cookbook is a solution-focused book that will teach you techniques to develop high-performance scrapers and deal with crawlers, sitemaps, forms automation, Ajax-based sites, caches, and more. You'll explore a number of real-world scenarios where every part of the development/product life cycle will be fully covered. You will not only develop the skills needed to design and develop reliable performance data flows, but also deploy your codebase to AWS. If you are involved in software engineering, product development, or data mining (or are interested in building data-driven products), you will find this book useful as each recipe has a clear purpose and objective. Right from extracting data from the websites to writing a sophisticated web crawler, the book's independent recipes will be a godsend. This book covers Python libraries, requests, and BeautifulSoup. You will learn about crawling, web spidering, working with Ajax websites, paginated items, and more. You will also learn to tackle problems such as 403 errors, working with proxy, scraping images, and LXML. By the end of this book, you will be able to scrape websites more efficiently and able to deploy and operate your scraper in the cloud.
Table of Contents (13 chapters)

Storing data in Elasticsearch

Elasticsearch is a search engine based on Lucene. It provides a distributed, multitenant-capable, full-text search engine with an HTTP web interface and schema-free JSON documents. It is a non-relational database (often stated as NoSQL), focusing on the storage of documents instead of records. These documents can be many formats, one of which is useful to us: JSON. This makes using Elasticsearch very simple as we do not need to convert our data to/from JSON. We will use Elasticsearch much more later in the book

For now, let's go and store our planets data in Elasticsearch.

Getting ready

We will access a locally installed Elasticsearch server. To do this from Python, we will use the Elasticsearch...