Elasticsearch Course

Lesson 1 • Interacting with the Cluster

Introduces the REST API and Kibana Dev Tools as primary interfaces for cluster interaction. Students execute their first index and document operations.

Lesson 2 • Core Concepts and Terminology

Defines indices, documents, shards, and replicas as the building blocks of Elasticsearch. Provides vocabulary used throughout the entire course.

Lesson 3 • Installing and Configuring Elasticsearch

Guides students through installation on common operating systems and basic configuration. Produces a running local instance ready for hands-on exercises.

Lesson 1 • Deleting Documents and Indices

Covers single-document deletion, delete-by-query, and index deletion. Completes the document lifecycle and introduces data management considerations.

Lesson 2 • Updating Documents

Explains partial updates, scripted updates, and upsert operations. Builds on indexing knowledge to handle evolving data requirements.

Lesson 3 • Creating and Indexing Documents

Covers PUT and POST methods for indexing single and bulk documents. Connects document ingestion to the index structures introduced in Chapter 1.

Lesson 4 • Versioning and Concurrency Control

Introduces optimistic concurrency control using sequence numbers and primary terms. Prevents data corruption in concurrent write scenarios.

Lesson 1 • Core Field Data Types

Covers text, keyword, numeric, date, boolean, and binary field types. Correct type selection directly impacts search relevance and aggregation accuracy.

Lesson 2 • Understanding Dynamic vs. Explicit Mappings

Contrasts dynamic mapping inference with explicit field definitions. Highlights trade-offs that affect query behavior and index performance.

Lesson 3 • Analyzers and Text Analysis

Explains the analysis pipeline: character filters, tokenizers, and token filters. Directly shapes how text fields are indexed and searched.

Lesson 4 • Complex and Nested Data Types

Introduces object, nested, and flattened types for hierarchical data. Prepares students to model documents with arrays and embedded objects.

Lesson 1 • Compound Queries and Boolean Logic

Combines queries using bool, dis_max, and boosting query types. Enables complex search logic by composing simpler queries from previous sections.

Lesson 2 • Sorting and Result Relevance

Explains TF-IDF and BM25 scoring, field-based sorting, and score explanation. Students learn to interpret and influence result ranking.

Lesson 3 • Full-Text Queries

Covers match, match_phrase, multi_match, and query_string queries for analyzed text. These are the primary tools for user-facing search features.

Lesson 4 • Search API Basics

Introduces the Search API request structure, URI search, and request body search. Establishes the foundation for all Query DSL work that follows.

Lesson 1 • Aggregation Framework Overview

Introduces the aggregation request structure and the three aggregation families. Provides the conceptual map needed before writing specific aggregations.

Lesson 2 • Pipeline Aggregations

Introduces derivative, moving_avg, cumulative_sum, and bucket_sort pipeline aggregations. Enables time-series analysis and cross-bucket calculations.

Lesson 3 • Nested and Sub-Aggregations

Demonstrates nesting metric aggregations inside bucket aggregations for layered analysis. Builds on bucket knowledge to produce multi-dimensional analytics.

Lesson 4 • Metric Aggregations

Covers sum, avg, min, max, stats, and cardinality aggregations for numeric analysis. Produces single-value and multi-value statistical outputs.

Lesson 1 • Shard Allocation and Sizing

Covers shard count decisions, replica configuration, and allocation awareness. Directly impacts cluster balance, query performance, and fault tolerance.

Lesson 2 • Snapshot and Restore

Configures snapshot repositories and schedules automated backups. Provides the disaster recovery capability required in production environments.

Lesson 3 • Cluster Health and Monitoring

Uses cluster health, cat, and nodes APIs to monitor cluster state. Enables proactive identification of issues before they affect availability.

Lesson 4 • Index Lifecycle Management

Automates index transitions through hot, warm, cold, and delete phases using ILM policies. Reduces operational overhead for time-series and log data.

Lesson 1 • Search Performance Optimization

Addresses query caching, shard request caching, and filter context usage for faster searches. Applies Query DSL knowledge from Chapter 4 to performance scenarios.

Lesson 2 • Profiling and Diagnosing Slow Queries

Uses the Profile API, slow logs, and hot threads to locate performance bottlenecks. Gives students a systematic diagnostic workflow for production issues.

Lesson 3 • Mapping and Storage Optimization

Reduces index size through field mapping choices, doc_values, and index options. Connects mapping design from Chapter 3 to storage and memory efficiency.

Lesson 4 • JVM and Memory Tuning

Configures heap size, garbage collection, and off-heap memory for stable operation. Addresses the JVM layer that underpins all Elasticsearch performance.

Lesson 1 • Production Deployment Checklist

Reviews OS settings, network configuration, and cluster hardening for production. Consolidates all prior chapters into a deployable, enterprise-grade configuration.

Lesson 2 • Enabling Elasticsearch Security

Activates built-in security features including TLS and basic authentication. Establishes the security baseline required before any production deployment.

Lesson 3 • Users, Roles, and Privileges

Creates users and roles with index, cluster, and field-level privileges. Implements least-privilege access control for multi-tenant environments.

Lesson 4 • Audit Logging and Compliance

Enables audit logging to record authentication events and data access. Supports compliance with data governance and access-control audit requirements.