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Learning Elasticsearch

You're reading from  Learning Elasticsearch

Product type Book
Published in Jun 2017
Publisher Packt
ISBN-13 9781787128453
Pages 404 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Abhishek Andhavarapu Abhishek Andhavarapu
Profile icon Abhishek Andhavarapu
LinkedIn
Abhishek Andhavarapu is a software engineer at eBay who enjoys working on highly scalable distributed systems. He has a master's degree in Distributed Computing and has worked on multiple enterprise Elasticsearch applications, which are currently serving hundreds of millions of requests per day. He began his journey with Elasticsearch in 2012 to build an analytics engine to power dashboards and quickly realized that Elasticsearch is like nothing out there for search and analytics. He has been a strong advocate since then and wrote this book to share the practical knowledge he gained along the way.
See other products by Abhishek Andhavarapu

Table of Contents (11) Chapters

Preface Introduction to Elasticsearch Setting Up Elasticsearch and Kibana Modeling Your Data and Document Relations Indexing and Updating Your Data Organizing Your Data and Bulk Data Ingestion All About Search More Than a Search Engine (Geofilters, Autocomplete, and More) How to Slice and Dice Your Data Using Aggregations Production and Beyond Exploring Elastic Stack (Elastic Cloud, Security, Graph, and Alerting)

Caching

In Elasticsearch 5.0, a lot of refactoring has been done to support better caching. The different types of cache available are as follows:

  • Node Query cache: Queries that run in filter context are cached here
  • Shard request cache: The results of the entire query are cached here

Node Query cache

Queries, such as numeric or date range, which run in the filter context are great candidates for caching. Since they have no scoring phase, they can be reused. The Node query cache is a smart cache; you do not have to worry about invalidating the cache. Individual queries that run in filter context are cached here. This cache is maintained at a node level and defaults to 10% of the heap and can be configured using the elasticsearch...

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Authors (1)

Profile icon Abhishek Andhavarapu
LinkedIn
Abhishek Andhavarapu is a software engineer at eBay who enjoys working on highly scalable distributed systems. He has a master's degree in Distributed Computing and has worked on multiple enterprise Elasticsearch applications, which are currently serving hundreds of millions of requests per day. He began his journey with Elasticsearch in 2012 to build an analytics engine to power dashboards and quickly realized that Elasticsearch is like nothing out there for search and analytics. He has been a strong advocate since then and wrote this book to share the practical knowledge he gained along the way.
Read more
See other products by Abhishek Andhavarapu

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