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Elasticsearch Essentials

You're reading from   Elasticsearch Essentials Harness the power of ElasticSearch to build and manage scalable search and analytics solutions with this fast-paced guide

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781784391010
Length 240 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started with Elasticsearch FREE CHAPTER 2. Understanding Document Analysis and Creating Mappings 3. Putting Elasticsearch into Action 4. Aggregations for Analytics 5. Data Looks Better on Maps: Master Geo-Spatiality 6. Document Relationships in NoSQL World 7. Different Methods of Search and Bulk Operations 8. Controlling Relevancy 9. Cluster Scaling in Production Deployments 10. Backups and Security Index

Creating a search database

It's always good to have some practical examples with real data sets, and what could be better than real-time social media data? In this section, we will write the code that will fetch tweets from Twitter in real time based on the search keywords provided. There are three dependencies of the code written in this section:

  • tweepy is a Python client for Twitter.
  • elasticsearch is a Python client for Elasticsearch that we have already installed.
  • For Twitter API access token keys, please follow the instructions at this link. https://dev.twitter.com/oauth/overview/application-owner-access-tokens, to create a sample Twitter application and get all the four keys that are needed to interact with the Twitter API. These four tokens are named: Access Token, Access Token Secret, Consumer Key, and Consumer Secret.

After generating the auth tokens and keys stored it inside config.py with the variable names: consumer_key, consumer_secret, access_token, and access_token_secret...

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