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Learning Elasticsearch

You're reading from   Learning Elasticsearch Structured and unstructured data using distributed real-time search and analytics

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787128453
Length 404 pages
Edition 1st Edition
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Author (1):
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Abhishek Andhavarapu Abhishek Andhavarapu
Author Profile Icon Abhishek Andhavarapu
Abhishek Andhavarapu
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Toc

Table of Contents (11) Chapters Close

Preface 1. Introduction to Elasticsearch FREE CHAPTER 2. Setting Up Elasticsearch and Kibana 3. Modeling Your Data and Document Relations 4. Indexing and Updating Your Data 5. Organizing Your Data and Bulk Data Ingestion 6. All About Search 7. More Than a Search Engine (Geofilters, Autocomplete, and More) 8. How to Slice and Dice Your Data Using Aggregations 9. Production and Beyond 10. Exploring Elastic Stack (Elastic Cloud, Security, Graph, and Alerting)

Ingest Node

Traditionally, Logstash is used to preprocess your data before indexing into Elasticsearch. Using Logstash, you can define pipelines to extract, transform, and index your data into Elasticsearch.

In Elasticsearch 5.0, the ingest node has been introduced. Using the ingest node, pipelines to modify the documents before indexing can be defined. A pipeline is a series of processors, each processor working on one or more fields in the document. The most commonly used Logstash filters are available as processors. For example, using a grok filter to extract data from an Apache log file into a document, extracting fields from JSON, changing the date format, calculating geo-distance from a location, and so on. The possibilities are endless. Elasticsearch supports many processors out of the box. You can also develop your own processors using any JVM-supported languages.

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