### Module 4: Cluster Sizing and Resource Allocation #### Lesson 1: Translating Business Requirements into Cluster Specifications **Objective**: Teach how to interpret business needs and translate them into technical specifications for an OpenSearch cluster. **Topics**: - **Understanding Business Objectives**: Overview of common business objectives that influence cluster design, such as search performance, data retention policies, and scalability requirements. - **Capacity Planning**: Introduction to capacity planning, including estimating data volume growth, query load, and the impact of these factors on cluster size and configuration. - **Performance Objectives**: Defining performance metrics and objectives, such as query response times and indexing throughput, and how they impact cluster specifications. - **Availability and Reliability**: Considerations for high availability and disaster recovery planning in cluster design. #### Lesson 2: Rightsizing for Cost-Effectiveness and Performance **Objective**: Provide strategies and tools for rightsizing OpenSearch clusters to balance cost with performance and operational needs. **Topics**: - **Rightsizing Principles**: Key concepts in rightsizing, including starting small, iterative testing, and scaling. - **Monitoring and Metrics**: Utilizing performance metrics and monitoring tools to inform rightsizing decisions. - **Scaling Strategies**: Detailed look at horizontal vs. vertical scaling, including when to choose each strategy based on different scenarios. - **Cost Optimization Techniques**: Techniques for cost-saving, such as choosing the appropriate instance types, leveraging reserved instances or savings plans, and optimizing storage. #### Lesson 3: Storage and Memory Considerations **Objective**: Dive into the specifics of managing storage and memory in OpenSearch clusters, crucial for performance and scalability. **Topics**: - **Storage Types and Options**: Overview of storage options (e.g., SSD vs. HDD) and their impact on performance. - **Estimating Storage Needs**: Techniques for estimating storage requirements based on data volume, indexing overhead, and replication. - **Memory Management**: Understanding the relationship between memory and performance in OpenSearch, including heap size configuration and garbage collection tuning. - **Data Lifecycle Management**: Introduction to strategies for managing the lifecycle of data in OpenSearch, such as index rollover and deletion policies to manage storage efficiently.