Description

Introduction of Cosmos DB for Performance Optimization

Cosmos DB for Performance Optimization course dives into the advanced aspects of Microsoft Azure Cosmos DB, focusing on techniques to optimize its performance for high-demand, globally distributed applications. Participants will explore strategies for efficient data modeling, query optimization, and cost management while ensuring scalability and reliability.

Prerequisites

• Fundamental Knowledge of Cosmos DB: Understanding of basic architecture, APIs, and data modeling concepts.
• Proficiency in Query Languages: Familiarity with SQL or NoSQL queries.
• Experience with Cloud-Based Databases: General knowledge of Azure and cloud computing.

Table of Contents

1. Overview of Cosmos DB Performance Optimization
1.1. Key Performance Challenges in Cosmos DB
1.2. Metrics for Measuring Performance
1.3. Tools for Performance Monitoring

2. Request Units (RUs) and Throughput Management
2.1. Understanding Request Units (RUs)
2.2. Setting and Scaling Provisioned Throughput
2.3. Autoscaling: Benefits and Best Practices
2.4. Cost Optimization with Consumption-Based Billing

3. Partitioning for Optimal Performance
3.1. Choosing the Right Partition Key(Ref: Cosmos DB Essentials: Building Scalable Applications)
3.2. Balancing Workloads Across Partitions
3.3. Analyzing Partition Metrics and Hotspots
3.4. Repartitioning Strategies

4. Query Performance Tuning
4.1. Indexing Policies: Custom vs. Default Indexing
4.2. Query Optimization Techniques
4.3. Avoiding Performance Bottlenecks in Queries
4.4. Leveraging Query Metrics for Fine-Tuning

5. Advanced Data Modeling
5.1. Schema Design for Performance
5.2. Denormalization vs. Normalization: Pros and Cons
5.3. Optimizing Data for Read-Heavy Workloads
5.4. Strategies for Write-Optimized Models

6. Performance with Multi-Model Support
6.1. Key-Value and Table API Performance Considerations
6.2. Optimizing Graph and Document Models
6.3. Tuning MongoDB and Cassandra APIs

7. Global Distribution and Latency Optimization
7.1. Configuring Multi-Region Writes and Reads
7.2. Consistency Levels and Their Impact on Performance
7.3. Leveraging Geo-Partitioning for Latency Reduction
7.4. Real-World Use Cases of Distributed Applications

8. Real-Time Performance Monitoring and Troubleshooting
8.1. Using Azure Metrics and Insights
8.2. Analyzing Logs for Performance Issues
8.3. Automated Alerts and Recommendations
8.4. Resolving Common Performance Pitfalls

9. Advanced Indexing and Query Customization
9.1. Using Composite Indexes for Complex Queries
9.2. Query Previews and Execution Plans
9.3. Utilizing User-Defined Functions (UDFs)
9.4. Efficiently Handling Large Query Results

10. Security and Performance
10.1. Securing High-Performance Databases
10.2. Managing Encryption Overheads
10.3. Balancing RBAC with Performance Needs
10.4. Compliance and Secure Performance

11. Conclusion
11.1. Recap of Optimization Techniques
11.2. Practical Use Cases and Success Stories
11.3. Future Trends in Database Performance
11.4. Final Thoughts

Conclusion

By mastering the advanced performance optimization techniques covered in this course, participants will unlock the full potential of Cosmos DB for their applications. This training ensures the capability to scale efficiently, optimize costs, and deliver high-performance solutions tailored to complex business requirements.

Reference