This plan is divided into modules. Try to complete them sequentially, ensuring you understand each concept before moving to the next. Hands-on practice is crucial, so set up an Elasticsearch instance early on.

Useful Material

• Official public source code
• Beginner’s Crash Course to Elastic Stack

# Module 1: Elasticsearch Fundamentals 🌍

Goal: Understand what Elasticsearch is, its core concepts, and common use cases.

# 1.1. What is Elasticsearch?

• Search engine, NoSQL database, analytics platform.
• Use cases: Log analytics, full-text search, real-time application monitoring, business analytics, etc.

# 1.2. Core Concepts:

• Document: Basic unit of information (JSON).
• Index: A collection of documents with similar characteristics (like a database table).
• Node: A single server that is part of a cluster.
• Cluster: A collection of one or more nodes.
• Shard: Indices are horizontally divided into shards for distribution and scalability.
  • Primary Shard
  • Replica Shard
• Mapping: Schema definition for an index (data types, how fields are indexed).
• REST APIs: Interacting with Elasticsearch.

# 1.3. Elastic Stack (ELK/Elastic Stack):

• Kibana: Visualization and management UI.
• Logstash: Data ingestion and processing pipeline.
• Beats: Lightweight data shippers.

Learning Activities:

• Read the “What is Elasticsearch?” and “Basic Concepts” sections of the official Elasticsearch documentation.
• Watch introductory videos on Elasticsearch.
• Explore common use cases and identify how Elasticsearch solves those problems.

Estimated Time: 1 week

# Module 2: Getting Started - Installation & Basic Operations 🚀

Goal: Set up an Elasticsearch instance and perform basic operations.

# 2.1. Installation:

• Local installation (Windows, macOS, Linux).
• Docker setup.
• Elastic Cloud (optional, for a managed experience).

# 2.2. Interacting with Elasticsearch:

• Using cURL or Kibana Dev Tools to send requests.
• Basic REST API commands (Cluster health, list indices, etc.).

# 2.3. CRUD Operations:

• Create: Indexing documents (POST or PUT).
• Read: Retrieving documents (GET).
• Update: Modifying documents.
• Delete: Removing documents.

# 2.4. Simple Searches:

• URI Search.
• Introduction to Query DSL (Domain Specific Language).
  • match_all query.
  • match query.

Learning Activities:

• Install Elasticsearch and Kibana locally or use Docker.
• Follow tutorials to perform CRUD operations.
• Practice sending requests via Kibana Dev Tools and/or cURL.
• Index some sample JSON documents and retrieve them.

Estimated Time: 1-2 weeks

# Module 3: Search & Aggregations 🔍📊

Goal: Master Elasticsearch’s powerful search and aggregation capabilities.

# 3.1. Query DSL Deep Dive:

Covered in Beginner’s Crash Course to Elastic Stack - Part 3: Full text queries

• Queries vs. Filters: Understanding the difference and when to use each.

  • Queries and filters

• Term-level queries: term, terms, range, exists, prefix.

  • term level queries

    • term query: returns documents that contain an exact term in a provided field. Useful when you have to lookup for an ID or some kind of specific value.

      Do not use term queries for text fields. By default, Elasticsearch changes the value of a give text field during the analysis process but the term search does not analyze the search term.

      Use match query instead.

    • terms query: same that term query except you can search for multiple values

    • range query

    • exists query: returns documents that contains an indexed value for a given field. Pretty useful when you want to filter out results where the value of a given field can be empty or null.

    • prefix query: returns documents that contain a specific prefix in a provided field.

• Full-text queries: match, match_phrase, multi_match, query_string.

  Beginner’s Crash Course to Elastic Stack - Part 3: Full text queries

  • full-text queries

    • match query: this is one of the most used search queries. The query parameter, by default, will use an OR clause for every term in the query. For instance, a query value of capital of Hungary is interpreted as capital OR of OR Hungary.

      Beginner’s Crash Course to Elastic Stack - Part 3: Full text queries # match query

    • match_phrase query: all terms must be present in the document, and they must appear in the exact order specified, and be contiguous.

      Beginner’s Crash Course to Elastic Stack - Part 3: Full text queries # match_phrase query

      match + AND != match_phrase

      In match_phrase all the terms must be included in the document, they must appear in the exact order specified, and be contiguous. On the other hand, match using AND is not equivalent to match_phrase because even all the terms must be included in the document, their order and proximity do not matter. It will find all the documents where all those words are present regardless of the arrangement.

    • multi_match query: it allows to use a match query against multiple fields.

      Beginner’s Crash Course to Elastic Stack - Part 3: Full text queries # muti_match query

• Compound queries: bool (must, should, must_not, filter), constant_score.

• Joining queries (nested, has_child, has_parent - if applicable to your use cases).

# 3.2. Analyzers and Mappings

All data types here.

• How text is analyzed (tokenizers, token filters, character filters).
• Standard analyzer vs. other built-in analyzers.
• Defining custom analyzers.
• Explicit mapping: Defining data types, indexing options (analyzed, not_analyzed - now text and keyword).
  • text vs keyword. From min 13:15 onwards
  • store fields
  • doc_values

# 3.3. Sorting and Pagination.

# 3.4. Aggregations:

• Bucket Aggregations: terms, range, date_histogram, nested.
• Metric Aggregations: sum, avg, min, max, cardinality, stats.
• Combining aggregations.

Learning Activities:

• Work through Query DSL examples in the documentation.
• Define custom mappings for your indices.
• Experiment with different analyzers to see how text is tokenized.
• Create complex queries combining multiple clauses.
• Build various aggregations on your sample data.

Estimated Time: 2-3 weeks

# Module 4: Elasticsearch Internals - Architecture & Data Handling ⚙️

Goal: Understand how Elasticsearch works under the hood.

# 4.1. Distributed Architecture:

• Cluster Discovery & Master Election: How nodes find each other and elect a master.

  Take a look

  Elasticsearch defines a custom binary protocol for inter-node communication using Netty. Look at org.elasticsearch.transport package, especially TransportService and Netty4Transport.

  Elasticsearch cluster coordination algorithms

• Shard Allocation & Routing: How Elasticsearch distributes shards and routes requests.

• Replication Process: How data is kept consistent across primary and replica shards.

• Fault Tolerance & High Availability.

# 4.2. Indexing Internals (Lucene Basics):

Covered in Elasticsearch Definitive Guide - Chapter 11 Inside a shard

Recommended: What is in a Lucene index? Adrien Grand, Software Engineer, Elasticsearch

• Inverted Index: The core data structure.
• Segments: How data is stored and managed in Lucene.
• Commits & Translog: Durability and data recovery.
• Refresh, Flush, Merge: Operations affecting data visibility and segment management.

# 4.3. Search Internals:

• Query Execution Flow: How a search request is processed across the cluster (scatter-gather).
• Relevance & Scoring:
  • TF-IDF (Term Frequency/Inverse Document Frequency) - conceptual understanding.
  • BM25 (Okapi BM25) - the default similarity algorithm.
  • How scores are calculated.
• Fielddata and Doc Values: Understanding their purpose and impact on memory.

Learning Activities:

• Read blogs and articles about Elasticsearch/Lucene internals.
• Use monitoring APIs (_cat APIs, _cluster/stats, _nodes/stats) to observe cluster behavior.
• Simulate node failures (if you have a multi-node setup) to see how the cluster reacts.
• Analyze query explanations (explain=true) to understand scoring.

Estimated Time: 3-4 weeks

# Module 5: Advanced Topics & Best Practices ✨

Goal: Explore advanced features, performance tuning, and operational best practices.

# 1. Data Modeling:

• Best practices for designing indices and mappings.
• Handling relationships (denormalization, nested objects, parent-child).

# 2. Performance Tuning:

• JVM Tuning: Heap size configuration.
• Shard Optimization: Number of shards, shard size.
• Query Optimization: Identifying and improving slow queries.
• Caching: Filter cache, shard request cache.
• Hardware Considerations.

# 3. Monitoring & Alerting:

• Using the _cat APIs and other monitoring APIs.
• Kibana for monitoring.
• Setting up alerts (e.g., via X-Pack alerting or other tools).

# 4. Index Lifecycle Management (ILM):

• Automating index management (hot, warm, cold, delete phases).

# 5. Security:

• Basic security concepts (authentication, authorization).
• Elasticsearch Security features (X-Pack).

# 6. Backup & Restore:

• Snapshot and Restore API.

Learning Activities:

• Design and implement data models for different scenarios.
• Profile and optimize some of your complex queries.
• Set up ILM policies for sample indices.
• Explore Kibana’s monitoring and management features.
• Practice backup and restore operations.

Estimated Time: 3-4 weeks

# Module 6: Practical Application & Continued Learning 🧑‍💻📚

Goal: Solidify your knowledge by building a project and staying updated.

## 1. Build a Project:

• Develop a small application that heavily utilizes Elasticsearch (e.g., a product search, a log analysis tool, a document search engine).

# 2. Explore Client Libraries:

• Familiarize yourself with an official Elasticsearch client library for your preferred programming language (e.g., Python, Java, JavaScript).

## 3. Contribute or Deep Dive:

• Explore the Elasticsearch source code (if interested in very deep internals).
• Contribute to an open-source project that uses Elasticsearch.

# 4. Stay Updated:

• Follow the Elastic Blog.
• Keep an eye on new releases and features.
• Join Elasticsearch communities (forums, Slack/Discord channels).

Learning Activities:

• Define project requirements, design the Elasticsearch integration, and build it.
• Read documentation and examples for your chosen client library.
• Attend webinars or read release notes for new Elasticsearch versions.

Estimated Time: Ongoing