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

Apache Spark support

Apache Spark is one of the most popular big data tools. It is a second-generation computing engine that works with Hadoop as an alternative to MapReduce. It provides in-memory computing capabilities to achieve high-performance analytics. The major components in Spark include Spark SQL, Spark Streaming, SparkR, Machine Learning Library (MLlib), and GraphX. Spark is built on the Scala programming language and also supports APIs for Java, Python, and R. The following diagram depicts the ecosystem of Spark:

Spark provides a hybrid processing framework, which means it supports both batch processing and stream processing. Let's look at these brief descriptions of each type of processing:

  • Batch processing: Usually, this applies to blocks of data that have been stored for a period of time and it takes a long time to complete the process. Spark handles all...