Book Image

Learning Elasticsearch

By : Abhishek Andhavarapu
Book Image

Learning Elasticsearch

By: Abhishek Andhavarapu

Overview of this book

Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, and open source search and analytics engine. You can use Elasticsearch for small or large applications with billions of documents. It is built to scale horizontally and can handle both structured and unstructured data. Packed with easy-to- follow examples, this book will ensure you will have a firm understanding of the basics of Elasticsearch and know how to utilize its capabilities efficiently. You will install and set up Elasticsearch and Kibana, and handle documents using the Distributed Document Store. You will see how to query, search, and index your data, and perform aggregation-based analytics with ease. You will see how to use Kibana to explore and visualize your data. Further on, you will learn to handle document relationships, work with geospatial data, and much more, with this easy-to-follow guide. Finally, you will see how you can set up and scale your Elasticsearch clusters in production environments.
Table of Contents (11 chapters)
10
Exploring Elastic Stack (Elastic Cloud, Security, Graph, and Alerting)

Concurrency

We discussed before that an update operation has to first retrieve the old document, apply the changes, and re-index the document. Between retrieving the old document and re-indexing the document, if some other operation updates the document, you would potentially overwrite the change. To solve this problem, Elasticsearch increments the version of the document on each operation.

If the version of the document has been changed between the document retrieval and re-indexing, the index operation fails. Let's take an example:

POST chapter4/person/2/_update 
{
"doc" : {
"name" : "name update 3"
}
}

The response to the preceding operation is as follows:

{
"_index": "chapter4",
"_type": "person",
"_id": "2",
"_version": 4,
"result": "updated&quot...