Book Image

Mastering Elastic Stack

By : Ravi Kumar Gupta, Yuvraj Gupta
Book Image

Mastering Elastic Stack

By: Ravi Kumar Gupta, Yuvraj Gupta

Overview of this book

Even structured data is useless if it can’t help you to take strategic decisions and improve existing system. If you love to play with data, or your job requires you to process custom log formats, design a scalable analysis system, and manage logs to do real-time data analysis, this book is your one-stop solution. By combining the massively popular Elasticsearch, Logstash, Beats, and Kibana, elastic.co has advanced the end-to-end stack that delivers actionable insights in real time from almost any type of structured or unstructured data source. If your job requires you to process custom log formats, design a scalable analysis system, explore a variety of data, and manage logs, this book is your one-stop solution. You will learn how to create real-time dashboards and how to manage the life cycle of logs in detail through real-life scenarios. This book brushes up your basic knowledge on implementing the Elastic Stack and then dives deeper into complex and advanced implementations of the Elastic Stack. We’ll help you to solve data analytics challenges using the Elastic Stack and provide practical steps on centralized logging and real-time analytics with the Elastic Stack in production. You will get to grip with advanced techniques for log analysis and visualization. Newly announced features such as Beats and X-Pack are also covered in detail with examples. Toward the end, you will see how to use the Elastic stack for real-world case studies and we’ll show you some best practices and troubleshooting techniques for the Elastic Stack.
Table of Contents (19 chapters)
Mastering Elastic Stack
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Configuring Elastic Stack components


In this section, we will configure all the tools for capturing the data. The components we will use are Elasticsearch, Logstash, Kibana, Filebeat, Metricbeat, and Packetbeat. Our pipeline would look like the following diagram:

All of the components share the same version, that is, 5.1.1. We will read logs using Filebeat, push those logs to Logstash for processing, and then add them to Elasticsearch for indexing. For our setup, Logstash is used at 192.168.0.112, Kibana is installed at 192.168.0.111 and Elasticsearch instance is set up at 192.168.0.110. This Elasticsearch instance is different than what we installed for Liferay search engine capability. The one used for Liferay is a lower version, v1.4.0, because that is the one supported by Elasticray

On the other hand, we will use Metricbeat and Packetbeat to collect data and send it directly to Elasticsearch. Finally, we can visualize the data using Kibana.

Setting up Elasticsearch

Depending on the requirements...