Book Image

Advanced Elasticsearch 7.0

By : Wai Tak Wong
Book Image

Advanced Elasticsearch 7.0

By: Wai Tak Wong

Overview of this book

Building enterprise-grade distributed applications and executing systematic search operations call for a strong understanding of Elasticsearch and expertise in using its core APIs and latest features. This book will help you master the advanced functionalities of Elasticsearch and understand how you can develop a sophisticated, real-time search engine confidently. In addition to this, you'll also learn to run machine learning jobs in Elasticsearch to speed up routine tasks. You'll get started by learning to use Elasticsearch features on Hadoop and Spark and make search results faster, thereby improving the speed of query results and enhancing the customer experience. You'll then get up to speed with performing analytics by building a metrics pipeline, defining queries, and using Kibana for intuitive visualizations that help provide decision-makers with better insights. The book will later guide you through using Logstash with examples to collect, parse, and enrich logs before indexing them in Elasticsearch. By the end of this book, you will have comprehensive knowledge of advanced topics such as Apache Spark support, machine learning using Elasticsearch and scikit-learn, and real-time analytics, along with the expertise you need to increase business productivity, perform analytics, and get the very best out of Elasticsearch.
Table of Contents (25 chapters)
Free Chapter
Section 1: Fundamentals and Core APIs
Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics
Section 3: Programming with the Elasticsearch Client
Section 4: Elastic Stack
Section 5: Advanced Features


Time flies! We have completed the chapter. Now, we understand mappings and know how to index documents. We have performed mapping APIs and created our own mapping design to index the sample documents. We also dealt with the remaining task from Chapter 3, Document APIs, to refresh the mapping changes of static mapping in order to avoid document reindexing.

In the next chapter, we'll delve into the analyzer. With a custom analyzer, you can define how the document fields behave before storing or during search time later. You have great control over how document fields are used in your queries to make your search more accurate and efficient. We'll first learn the tokenizers, and then the filters. Then, after reviewing the built-in analyzers, we'll write our own analyzer.