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

Mapping APIs

In the last chapter, we learned about the document life cycle of Elasticsearch and executed single and multiple document APIs. We introduced a commission-free ETF from TD Ameritrade and used this information to practice document APIs. We also reindexed the documents from the multiple mapping types index for migration.

In this chapter, we are going to learn about mapping APIs. In Elasticsearch, mapping is a data model that describes the structure of a document. It allows you to specify fields, field types, relationships between documents, data conversion rules, and so on. Schema-less only means that documents can be indexed without specifying the schema in advance, because the schema is dynamically derived from the first document index structure based on the built-in mapping rules in Elasticsearch. If you have a good search database design plan, you should use explicit...