For join datatypes, the parent allows re-indexing/adding/deleting specific children. However, using has_child or has_parent queries can have a significant impact on performance. If you need better performance, always use nested datatypes. Nonetheless, as long as you have to update, you need to re-index all children to their parent. The nested datatype approach is also easier to manage than the join datatype approach. You must be very careful while using the join datatype method because you can index children without a parent. Also, if you want to remove a parent, it is not an automatic cascading task to delete all of its children. You need to clean it up by yourself. On the other hand, if you want to update parent or child document, the join datatypes approach will be more convenient because you can update the values in the parent field or the child field...
Advanced Elasticsearch 7.0
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Advanced Elasticsearch 7.0
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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.
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Current Title:
Advanced Elasticsearch 7.0
Table of Contents (25 chapters)
Preface
Overview of Elasticsearch 7
Index APIs
Document APIs
Mapping APIs
Anatomy of an Analyzer
Search APIs
Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics
Modeling Your Data in the Real World
Aggregation Frameworks
Preprocessing Documents in Ingest Pipelines
Using Elasticsearch for Exploratory Data Analysis
Section 3: Programming with the Elasticsearch Client
Elasticsearch from Java Programming
Elasticsearch from Python Programming
Section 4: Elastic Stack
Using Kibana, Logstash, and Beats
Working with Elasticsearch SQL
Working with Elasticsearch Analysis Plugins
Section 5: Advanced Features
Machine Learning with Elasticsearch
Spark and Elasticsearch for Real-Time Analytics
Building Analytics RESTful Services
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