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  • Book Overview & Buying Advanced Elasticsearch 7.0
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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

Query DSL

Query DSL is a JSON-based search language for querying specific and analytical datasets. Basically, a query can be classified in two contexts—a query context and a filter context. In the filter context, the search results are based on the question relating to the match or lack of match to the query clause, where the no scoring value, _score, is provided in the results. In the context of the query, the search results are based on measuring the appropriateness of the match on the query clause. We will introduce a number of common query subtypes in the following subsection.

Full text queries

This type of query will use analyzers (or given search analyzers) to tokenize the query string first. Then, it uses the...

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