Elasticsearch for Developers Training Course

1. Introduction to Elasticsearch

• Defining Elasticsearch.
• Practical use cases for Elasticsearch.
• Overview of Elasticsearch architecture.
• Key components of the Elastic Stack (Elasticsearch, Logstash, Kibana, Beats).
• Installing and running Elasticsearch using containers.
• Investigating the REST API.

2. Understanding Indices and Documents

• Document structure and JSON format.
• Organizing data using indices.
• The role of shards and replicas.
• Concepts behind the index lifecycle.
• Procedures for creating, updating, and deleting indices.
• Performing CRUD operations on documents.

3. Writing Search Queries

• Overview of the Query DSL.
• Using match queries.
• Using term queries.
• Constructing boolean queries.
• Using range queries.
• Prefix, wildcard, and fuzzy search techniques.
• Implementing pagination and sorting.
• Distinguishing between filtering and querying.

4. Performing Text Analysis

• Basics of full-text search.
• The function of analyzers.
• Working with tokenizers.
• Applying character filters.
• Applying token filters.
• Using language-specific analyzers.
• Creating custom analyzers.
• Strategies for enhancing search relevance.

5. Defining Mappings

• How dynamic mappings work.
• Configuring explicit mappings.
• Different field data types.
• Handling nested and object fields.
• Managing date and numeric fields.
• Best practices for mapping data.
• Safely updating existing mappings.

6. Expanding Your Searches

• Searching across multiple fields.
• Utilizing multi-match queries.
• Executing phrase searches.
• Highlighting specific search results.
• Adjusting search boosts.
• Using function score queries.
• Implementing search templates.

7. Understanding the Distributed Model

• Cluster architecture overview.
• Node types and their roles.
• Differences between primary and replica shards.
• Assessing cluster health.
• How data is distributed.
• Mechanisms for fault tolerance.
• Concepts of high availability.

8. Manipulating Search Results

• Strategies for pagination.
• Filtering the source of results.
• Collapsing fields in results.
• Using script fields.
• Techniques for sorting results.
• Highlighting features in search results.
• Optimizing the search response payload.

9. Aggregations and Analytics

• Performing metric aggregations.
• Creating bucket aggregations.
• Using pipeline aggregations.
• Executing statistical calculations.
• Working with histograms.
• Performing date-based aggregations.
• Constructing complex analytical queries.
• Optimizing aggregation performance.

10. Handling Data Relationships

• Structure of object fields.
• Managing nested documents.
• Setting up parent-child relationships.
• Strategies for denormalization.
• Selecting the appropriate data model.
• Querying associated data sets.

11. Integrating Elasticsearch with Applications

• Integrating via the REST API.
• Using Elasticsearch client libraries.
• Indexing data from your application.
• Utilizing the Bulk API.
• Interacting with Search APIs.
• Implementing error handling.
• Best practices for application integration.

12. Performance Optimization

• Strategies for efficient indexing.
• Optimizing search queries.
• Implementing bulk indexing.
• Configuring refresh intervals.
• Leveraging caching mechanisms.
• Managing memory usage.
• Monitoring system performance.

13. Monitoring and Troubleshooting

• Tracking cluster health.
• Analyzing index statistics.
• Diagnosing slow-running queries.
• Addressing common indexing issues.
• Resolving cluster-related problems.
• Understanding backups and snapshots.
• Utilizing logs for diagnostics.

14. Hands-on Workshop and Summary

• Developing a fully searchable application.
• Designing optimal indices and mappings.
• Implementing full-text search features.
• Creating aggregations and analytics views.
• Tuning search performance.
• Recap of fundamental concepts.
• Open Q&A session.
• Review of best practices and future steps.