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Modern Big Data Processing with Hadoop

You're reading from  Modern Big Data Processing with Hadoop

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Pages 394 pages
Edition 1st Edition
Languages
Concepts
Authors (3):
V Naresh Kumar V Naresh Kumar
Profile icon V Naresh Kumar
Manoj R Patil Manoj R Patil
Profile icon Manoj R Patil
Prashant Shindgikar Prashant Shindgikar
Profile icon Prashant Shindgikar
View More author details
Toc

Table of Contents (12) Chapters close

Preface 1. Enterprise Data Architecture Principles 2. Hadoop Life Cycle Management 3. Hadoop Design Consideration 4. Data Movement Techniques 5. Data Modeling in Hadoop 6. Designing Real-Time Streaming Data Pipelines 7. Large-Scale Data Processing Frameworks 8. Building Enterprise Search Platform 9. Designing Data Visualization Solutions 10. Developing Applications Using the Cloud 11. Production Hadoop Cluster Deployment

Analyzer

We have already learned about an inverted index. We know that Elasticsearch stores a document into an inverted index. This transformation is known as analysis. This is required for a successful response of the index search query.

Also, many of the times, we need to use some kind of transformation before sending that document to Elasticsearch index. We may need to change the document to lowercase, stripping off HTML tags if any from the document, remove white space between two words, tokenize the fields based on delimiters, and so on.

Elasticsearch offers the following built-in analyzers:

  • Standard analyzer: It is a default analyzer. This uses standard tokenizer to divide text. It normalizes tokens, lowercases tokens, and also removes unwanted tokens.
  • Simple analyzer: This analyzer is composed of lowercase tokenizer.
  • Whitespace analyzer: This uses the whitespace tokenizer...
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