Elasticsearch Joins & Parent-Child Queries

This course is part of Elastic Stack Mastery with Elasticsearch & AWS Specialization

Instructor: EDUCBA

Access provided by SGCSRC

3 modules

Gain insight into a topic and learn the fundamentals.

6 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

6 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Apply nested and parent-child queries for data modeling.
Optimize Elasticsearch joins and query performance.
Implement relational-style data structures using mappings.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments

Taught in English

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This course is part of the Elastic Stack Mastery with Elasticsearch & AWS Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
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There are 3 modules in this course

By the end of this course, learners will be able to apply nested queries, implement parent-child mappings, and execute relational-style joins in Elasticsearch for advanced data modeling. You will gain the skills to index event-driven data, use inner hits for detailed matches, and optimize query performance with global ordinals.

This course is designed to bridge the gap between traditional relational databases and Elasticsearch’s document-oriented model. Through practical workshops and structured lessons, learners will explore application-side joins, nested queries, and indexing best practices. The course then advances into modeling complex one-to-many relationships using parent-child queries, with hands-on exercises for has_child and has_parent functionalities. What makes this course unique is its step-by-step alignment of relational concepts with Elasticsearch capabilities, ensuring you not only understand the theory but also practice real-world applications. Whether you are a data engineer, developer, or search specialist, this course equips you with essential strategies to analyze, structure, and query large-scale datasets efficiently in Elasticsearch.

This module introduces learners to the relational aspects of Elasticsearch, focusing on how queries can simulate relational joins within JSON structures. It covers application-side joins, nested queries, mapping validations, and practical workshops to ensure accurate and efficient query execution. By the end of this module, learners will understand how to structure and query complex document relationships.

What's included

8 videos3 assignments

8 videos Total 66 minutes

Introduction to Joining Queries 10 minutes
Application Side Joins 9 minutes
Nested Queries 9 minutes
Nested Queries Continues 8 minutes
Checking Mapping Documents 7 minutes
Nested Queries Workshop 7 minutes
Index with Proper Mapping 7 minutes
Indexing Document 9 minutes

3 assignments Total 50 minutes

Joining Queries and Application-Side Approaches 10 minutes
Nested Queries in Practice 10 minutes
Query Joins and Fundamentals 30 minutes

This module explores how event-based data is indexed and analyzed in Elasticsearch. Learners will work with sign-up and payment events, post-event indexing, and the use of inner hits for detailed query results. The module also introduces parent-child mappings and global ordinals to efficiently model one-to-many relationships.

What's included

7 videos3 assignments

7 videos Total 65 minutes

Sign Up and Payment Event 10 minutes
Post Event Index 9 minutes
Inner Hits 9 minutes
Parent Child Mapping 11 minutes
Parent Child Mapping Continues 9 minutes
Parent Field 9 minutes
Global Ordinals 8 minutes

3 assignments Total 50 minutes

Event Indexing and Data Flow 10 minutes
Parent-Child Mapping Essentials 10 minutes
Event Data and Advanced Query Features 30 minutes

This module provides hands-on practice with advanced parent-child queries. Learners will master the has_child and has_parent queries, refine queries with constraints, and sort parent documents using child values. The focus is on understanding performance considerations and effectively linking parent and child data for scalable queries.