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Advanced Elasticsearch 7.0

Advanced Elasticsearch 7.0

By : Wong
3.5 (4)
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Advanced Elasticsearch 7.0

Advanced Elasticsearch 7.0

3.5 (4)
By: 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)
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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

Summary

Voila! We have completed this chapter. We should now understand what Elasticsearch plugin management is and how it enhances the core functionality of Elasticsearch.

In this chapter, we introduced Analysis Plugins and deep dived into their details. We practiced the ICU Analysis plugin, the Smart Chinese Analysis plugin, and the IK Analysis plugin to perform analysis for Chinese texts. It seems that the IK Analysis plugin is better than the other two plugins due to its ability to segment word boundaries. We also added a new custom dictionary to improve word segmentation in order to make it work better.

In the next chapter, we will study the machine learning support that is available in Elasticsearch and use a Python scikit-learn package to work with Elasticsearch. We will follow two examples; the first one will create a single metric job to track the custom indicator to...

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