This course introduces you to the best practices and tools that help you optimize and improve the performance of your Amazon DocumentDB (with MongoDB compatibility) clusters. You will learn how to design your Amazon DocumentDB database for optimized performance by monitoring key metrics and troubleshooting the most common challenges. Following performance-tuning best practices can help save costs related to performance and billing.
- Course level: Advanced
- Duration: 1.5 hours
Activities
This course includes presentations, videos, and assessments.
Course objectives
In this course, you will learn to:
- Describe the most common techniques and best practices for designing and architecting your Amazon DocumentDB cluster to help optimize performance.
- Identify the most common metrics and tools used to monitor your Amazon DocumentDB cluster for optimized performance.
- Troubleshoot the most common performance challenges with proven techniques, tools, and best practices.
- Locate Amazon Web Services (AWS) support contacts and resources for questions about Amazon DocumentDB performance tuning.
Intended audience
This course is intended for:
- Developers and data platform engineers (database engineers, database architects, database administrators)
- Solutions architects who are curious to know about performance-tuning techniques for a specific AWS database
Prerequisites
We recommend that attendees of this course have:
- Completed the Getting Started with Amazon DocumentDB course
- Knowledge of building with Amazon DocumentDB
- Knowledge of data modeling with Amazon DocumentDB
Course outline
Introduction
- The database performance tuning process
- Value of performance tuning and query optimization
Module 1: Designing for High-Performance Factors
- Architecture
- Data Modeling
- Instance Types
- Indexing Strategy
Module 2: Metrics and Tools for Monitoring Performance
- Available Metrics
- Monitoring Tools
- Event Notifications and Alarms
- Database Status Commands
Module 3: Troubleshooting Performance Challenges
- Slow-Running Query
- Large Aggregation Pipeline Queries
- High CPU Utilization
- Low Freeable Memory Compared to RAM
- High Number of Connections
- High Number of Cursors
- Low Cache Usage
Module 4: Wrap-Up
- Resources
