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Mastering Elastic Stack

Mastering Elastic Stack

By : Kumar Gupta, Gupta
1 (1)
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Mastering Elastic Stack

Mastering Elastic Stack

1 (1)
By: Kumar Gupta, 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 (13 chapters)
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Ingest nodes

As we learnt in the previous section, ingest nodes help to preprocess things before a document is indexed. Before a bulk request or index operations, the ingest node intercepts the request and does required processing on the document. An example of such a processor can be the date processor, which is used to parse the dates in fields. Another example is a convert processor, which converts a field value to a target type, for example, string to integer. A number of processors are available at: https://www.elastic.co/guide/en/elasticsearch/reference/5.1/ingest-processors.html.

These kinds of nodes are helpful when a huge processing happens and we do not want a data node or master node to engage in processing. Dedicated ingest nodes can help to reduce the load significantly. It is best to set node.ingest as false for data and master nodes.

To understand how ingest nodes work with pipelines, let's follow these steps. Let's take the example of our library index and type...

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