Performance Tools and Techniques
Explore key performance tuning techniques in MongoDB using profiling, slow query analysis, and system monitoring. Learn how to optimize indexes, cache utilization, and hardware configurations to improve database efficiency at scale.
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Upon completion of the Performance Tools and Techniques skill and assessment, you will earn a Credly Badge that you are able to share with your network. |
Learning Objectives
Use tuning techniques including profiling, slow query analysis, and system monitoring
Analyze query performance and identify slow operations using MongoDB’s profiling and monitoring tools.
Optimize indexes
Apply indexing strategies to improve query performance and reduce latency.
Optimize cache utilization
Configure MongoDB's caching mechanisms to enhance read efficiency and reduce database load.
Who is this Course Good for?
This skill is ideal for developers and operations engineers who are responsible for keeping MongoDB applications fast, responsive, and reliable in production. If you work on systems where slow queries, resource bottlenecks, or unpredictable performance can quickly impact user experience, this Performance Tools and Techniques Skill Badge is designed for you. It is particularly useful if you are comfortable building applications on MongoDB but want a clearer, more systematic way to investigate performance issues and tune your deployments. Whether you manage MongoDB in the cloud or on self-managed infrastructure, this course will help you understand how MongoDB performance tools fit together so you can diagnose issues confidently and keep your applications running efficiently.
What to Expect in this Course
In this skill badge, you will learn how to use MongoDB performance tools to analyze, troubleshoot, and improve application behavior under load. The skill begins by introducing profiling tools, including the Query Profiler in MongoDB Atlas and the Database Profiler for self-managed deployments. You learn the purpose of profiling in performance analysis, how to capture detailed information about database operations, and how to use this information to pinpoint problematic queries that slow your application down.
From there, you focus on slow queries and how to investigate them effectively. The skill defines what constitutes a slow query in a MongoDB context and shows you how to analyze these operations using MongoDB log files and command-line tools. You also learn to use the explain command to understand how the query planner executes a given operation, which indexes it uses, and where there may be opportunities to optimize. This gives you a structured way to move from symptoms, like high latency, to root causes in your query patterns and index design.
Next, you explore how system resources such as CPU, memory, disk I/O, and network bandwidth influence MongoDB performance. You examine performance metrics at the namespace and cluster levels using tools like Atlas Query Insights, the Atlas Cluster Metrics page, and the Atlas Real-Time Monitoring panel. These tools help you correlate query behavior with resource utilization so you can distinguish between query-level inefficiencies and broader hardware or configuration constraints. You also learn how to monitor MongoDB with mongotop and mongostat to understand how different collections affect performance, and you complement this with btop to gain a deeper view of CPU and memory usage on the host system.
Finally, the skill covers how indexes fit into MongoDB performance tuning. You review the role indexes play in speeding up queries and learn how to add or optimize indexes using Atlas Performance Advisor as well as the MongoDB shell (mongosh) to create, modify, and remove indexes where appropriate. Hands-on labs provide realistic scenarios where you apply profiling, monitoring, and indexing tools together, giving you a practical, end-to-end understanding of how to keep MongoDB systems running smoothly under real-world workloads.
Summary of the Course
- Understand how MongoDB performance tools work together to keep your system running efficiently.
- Use the Query Profiler in Atlas and the Database Profiler in self-managed deployments to identify problematic operations.
- Define slow queries and analyze them using MongoDB log files, command-line tools, and the explain command.
- Evaluate how CPU, memory, disk I/O, and network bandwidth impact MongoDB performance and identify resource bottlenecks.
- Analyze performance metrics with Atlas Query Insights, the Atlas Cluster Metrics page, and the Atlas Real-Time Monitoring panel.
- Monitor MongoDB activity across collections and databases using mongotop and mongostat, and complement this with host-level insights using btop.
- Improve query performance by adding and optimizing indexes with Atlas Performance Advisor and by managing indexes through the MongoDB shell.
- Apply performance tools and techniques in hands-on labs that reflect real-world troubleshooting and optimization scenarios.
Sequoyha Pelletier | Senior Technologist, Education
Sequoyha Pelletier is a Senior Technologist at MongoDB, bringing over 15 years of experience in technical curriculum development and delivery. Before joining MongoDB, he worked in the Worldwide Support team for DataStax, eventually leading the curriculum team for new hire onboarding.
Sequoyha is extremely passionate about providing quality education for free to those in need and enjoys pushing the boundaries of what is considered "normal" practices with delivering educational content.
Sarah Evans | Senior Curriculum Engineer
Sarah is a Senior Curriculum Engineer on the Curriculum team at MongoDB. Prior to MongoDB, she taught and developed curricula for developer bootcamps. Sarah has a MAT degree from Columbia University Teachers College and studied Software Engineering at Flatiron School in Chicago, IL.
Manuel Fontan Garcia | Senior Technologist, Education
Manuel is a Senior Technologist on the Curriculum team at MongoDB. Previously he was a Senior Technical Services Engineer in the Core team at MongoDB. In between Manuel worked as a database reliability engineer at Slack for a little over 2 years and then for Cognite until he re-joined MongoDB. With over 15 years experience in software development and distributed systems, he is naturally curious and holds a Telecommunications Engineering MSc from Vigo University (Spain) and a Free and Open Source Software MSc from Rey Juan Carlos University (Spain).
Daniel Curran | Senior Software Engineer
Daniel is a Senior Software Engineer at MongoDB. Before joining MongoDB, he worked as an Instructional Designer and Content Developer specialising in technical content for a host of international clients. Daniel's goal is to remove obstacles so learners can feel confident on their journey to become masters of MongoDB.
John McCambridge | Curriculum Engineer
John is a Curriculum Engineer on the University team at MongoDB. Before his work as a Curriculum Engineer, he was an instructor and teaching assistant for coding boot camps at UT (Austin), and UCLA. Additionally, he worked as a QA engineer for a startup called Coder and spent five years at Apple Inc. John is a passionate software engineer and educator who enjoys taking complex topics and making them digestible for the community.
Hello. My name is Aaron, and I'm a curriculum engineer at MongoDB.
Welcome to this skill on performance tools for MongoDB.
Here, we'll explore the powerful performance tools available in MongoDB to tackle these challenges head on and restore application responsiveness.
First, we'll look at the tools for profiling our MongoDB databases, including the query profiler in Atlas and the database profiler for self managed instances. We'll discuss the purpose of profiling and performance analysis, as well as how these tools can help us find problematic operations.
Then we'll look at how we can leverage tools to analyze our slow queries to determine the best path towards optimization.
We'll define what constitutes a slow query and learn how to analyze them using command line tools with MongoDB log files, as well as the explain command in MongoDB Shell.
We'll then look at how system resources like CPU, memory, disk IO, and network bandwidth affect database performance and identify resource bottlenecks that impact query performance.
We'll analyze performance metrics at the name space level using Atlas query insights, explore the range of available metrics on the Atlas cluster metrics page, and look at the Atlas real time monitoring panel to monitor real time changes.
We'll also learn how to monitor our system using MongoTop and MongoStat to see how different collections impact our database performance and gain broader hardware insights by using BTOP to monitor our CPU and memory usage. Finally, we'll go over the role that indexes play in query performance and how we can add or optimize our indexes using the Atlas performance advisor, as well as using the MongoDB shell to create, modify, and remove indexes. You'll have plenty of opportunities to practice what you've learned by completing labs that present real world scenarios.
When you're finished, you'll be ready to put your new skills to the test. To earn your badge, complete all the related content and then take the short skill check at the end. After passing it, you'll receive an official Credly badge via the email you provided. Be sure to share your badge on LinkedIn to show off your new skills. By completing this skill badge, you'll have a basic understanding of how the various performance tools in MongoDB work together to help us keep our system running efficiently.
Let's get started.
