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Learning Elastic Stack 7.0

Learning Elastic Stack 7.0

By : Pranav Shukla, Sharath Kumar M N
3.8 (6)
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Learning Elastic Stack 7.0

Learning Elastic Stack 7.0

3.8 (6)
By: Pranav Shukla, Sharath Kumar M N

Overview of this book

The Elastic Stack is a powerful combination of tools that help in performing distributed search, analytics, logging, and visualization of data. Elastic Stack 7.0 encompasses new features and capabilities that will enable you to find unique insights into analytics using these techniques. This book will give you a fundamental understanding of what the stack is all about, and guide you in using it efficiently to build powerful real-time data processing applications. The first few sections of the book will help you understand how to set up the stack by installing tools and exploring their basic configurations. You’ll then get up to speed with using Elasticsearch for distributed search and analytics, Logstash for logging, and Kibana for data visualization. As you work through the book, you will discover the technique of creating custom plugins using Kibana and Beats. This is followed by coverage of the Elastic X-Pack, a useful extension for effective security and monitoring. You’ll also find helpful tips on how to use Elastic Cloud and deploy Elastic Stack in production environments. By the end of this book, you’ll be well-versed with fundamental Elastic Stack functionalities and the role of each component in the stack to solve different data processing problems.
Table of Contents (17 chapters)
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1
Section 1: Introduction to Elastic Stack and Elasticsearch
4
Section 2: Analytics and Visualizing Data
10
Section 3: Elastic Stack Extensions
12
Section 4: Production and Server Infrastructure

Modeling time series data

Often, we have a need to store time series data in Elasticsearch. Typically, one would create a single index to hold all documents. This typical approach of one big index to hold all documents has its own limitations, especially for the following reasons:

  • Scaling the index with an unpredictable volume over time
  • Changing the mapping over time
  • Automatically deleting older documents

Let's look at how each problem manifests itself when we choose a single monolithic index.

Scaling the index with unpredictable volume over time

One of the most difficult choices when creating an Elasticsearch cluster and its indexes is deciding how many primary shards should be created and how many replica shards should...

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