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

Aggregation Frameworks

The two key features of Elasticsearch are search and data analytics. In the previous two chapters, we learned about the search API and how to design search data modeling. We also used real-world examples from the Investor Exchange (IEX) Cloud ETF system to practice using the search feature. In this chapter, we will discuss data analytics using the aggregation framework. Aggregation can be thought of as a unit of work for building analytic information on a set of documents. The framework consists of many building blocks that can be composed to build a complex summary of the data selected by a search query. The framework is straightforward, simple, extensible, quick to access, and awesome. It can be very helpful for our business.

By the end of this chapter, we will have covered the following topics and used IEX historical ETF data to work on supported aggregation...