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
1
Section 1: Fundamentals and Core APIs
8
Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics
13
Section 3: Programming with the Elasticsearch Client
16
Section 4: Elastic Stack
20
Section 5: Advanced Features

Building a RESTful web service with Spring Boot

In this project, we want to view the Bollinger Band on the Kibana Visualize page. There may be more than one way to do it. What we are going to do is pre-compute the related values of the Bollinger Band and index them when the trading price of the targeted symbol is ready. In addition to the fields from the cf_etf_history_price index, we are going to index fields such as the standard deviation and the moving average of 20 trading days, the upper and lower bounds of the Bollinger Band, the typical price = (high+low+close)/3, and the predicted anomaly class from the k-means model. We will create a Bollinger Band visualization with Kibana, as shown in the following screenshot:

There are five lines on the chart. The uppermost line on the chart represents the predicted class that uses the k-means model for the 20-day standard deviation...