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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)

Shrink API

Shrink API is used to shrink an existing index into a new index with a fewer number of shards. If the data in the index is no longer changing, the index can be optimized for search and aggregation by reducing the number of shards. The number of shards in the destination index must be a factor of the original index. For example, an index with 6 primary shards can be shrunk into 3, 2, or 1 shards. When working with time-sensitive data, such as logs, data is only indexed into the current indexes and older indexes are mostly read only. Shrink API doesn't re-index the document; it simply relinks the index segments to the new index.

To shrink an index, the index should be marked as read-only, and either a primary or a replica of all the shards of the index should be moved to one node. We can force the allocation of the shards to one node and mark it as read only as shown...

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