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Learning Elastic Stack 6.0

Learning Elastic Stack 6.0

By : Pranav Shukla, Sharath Kumar
4.5 (10)
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Learning Elastic Stack 6.0

Learning Elastic Stack 6.0

4.5 (10)
By: Pranav Shukla, Sharath Kumar

Overview of this book

The Elastic Stack is a powerful combination of tools for distributed search, analytics, logging, and visualization of data from medium to massive data sets. The newly released Elastic Stack 6.0 brings new features and capabilities that empower users to find unique, actionable insights through these techniques. This book will give you a fundamental understanding of what the stack is all about, and how to use it efficiently to build powerful real-time data processing applications. After a quick overview of the newly introduced features in Elastic Stack 6.0, you’ll learn how to set up the stack by installing the tools, and see their basic configurations. Then it shows you how to use Elasticsearch for distributed searching and analytics, along with Logstash for logging, and Kibana for data visualization. It also demonstrates the creation of custom plugins using Kibana and Beats. You’ll find out about Elastic X-Pack, a useful extension for effective security and monitoring. We also provide useful tips on how to use the Elastic Cloud and deploy the Elastic Stack in production environments. On completing this book, you’ll have a solid foundational knowledge of the basic Elastic Stack functionalities. You’ll also have a good understanding of the role of each component in the stack to solve different data processing problems.
Table of Contents (12 chapters)
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Logstash architecture

The Logstash event processing pipeline has three stages, they are: Inputs, Filters and Outputs. A Logstash pipeline has two required elements; input, output, and, optionally, filters:

Inputs create events, Filters modify the input events, and Outputs ship them to the destination. Inputs and outputs support codecs which enable you to encode or decode the data as and when it enters or exits the pipeline without having to use a separate filter.

Logstash uses in-memory bounded queues between pipeline stages by default (Input to Filter and Filter to Output) to buffer events. If Logstash terminates unsafely, any events that are stored in memory will be lost. To prevent data loss, you can enable Logstash to persist in-flight events to the disk by making use of persistent queues. 

Persistent queues can be enabled by setting the...

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