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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)

Caching

When you execute an aggregation query, the node that receives the request sends the query to all the shards of the index. The results from each shard are gathered back and sent to the client. The aggregation results are cached at a shard level. Computing an aggregation is an expensive operation. Caching the response greatly improves the performance and reduces the strain on the system. Elasticsearch cache is smart and is automatically invalidated where there is new data. Since the cache is per shard, the cache is invalidated only for the shards that have new/modified data. Starting Elasticsearch 5.0, the request cache is enabled by default. The query JSON is used as a key.

Cache greatly improves performance for indexes that have static data. For example, if you have a time-based index, the old indexes are not changed anymore. The aggregation results for the old data can...

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