### Module 3: Data Modeling and Index Management #### Lesson 1: Understanding Data Modeling in OpenSearch **Objective**: Introduce the principles of data modeling in OpenSearch, emphasizing how data structure impacts performance and scalability. **Topics**: - **Data Modeling Concepts**: Overview of data modeling in a search engine context, contrasting with traditional relational databases. - **Documents and Fields**: Understanding the nature of [[Document]]s and [[Field]]s in OpenSearch, including data types and their impact on search and analytics. - **Schema Design**: Discussion on dynamic vs. explicit schemas, benefits of each, and use cases. See [[Mapping]] - **Normalization vs. Denormalization**: Exploring the trade-offs between normalization and denormalization within the context of search engines and the impact on performance. #### Lesson 2: Index Creation, Mapping, and Management **Objective**: Equip participants with the knowledge to create, map, and manage indices in OpenSearch efficiently. **Topics**: - **Creating Indices**: Step-by-step process for creating indices in OpenSearch, including settings and configurations. - **Understanding Mappings**: Deep dive into [[Mapping]]s, the role they play in indexing, and how to define them for various field types. - **Index Templates**: Use of index templates for automating index creation with predefined settings and mappings. - **Index Management**: Techniques for managing and optimizing indices over time, including aliasing, reindexing, and lifecycle management. #### Lesson 3: Strategies for Efficient Indexing **Objective**: Explore advanced techniques and best practices for efficient indexing, focusing on optimizing performance and resource usage. **Topics**: - **Bulk Indexing**: Best practices for using the bulk API for efficient data ingestion. - **Shard Strategies**: Understanding how shard size and number affect indexing and search performance, and strategies for choosing the optimal configuration. - See [[Shard#Sharding Strategies]] - **Refresh and Flush Policies**: Configuring refresh intervals and understanding flush mechanics to balance between indexing performance and search latency. - **Indexing Performance Tuning**: Advanced settings and techniques to enhance indexing throughput and efficiency, such as thread pool configurations and hardware considerations.