Vector Search Performance
In this skill badge, you'll learn how MongoDB Vector Search works under the hood and what to do when it slows down in production. You'll build a diagnostic playbook using Atlas Metrics to trace slow queries to their root cause, whether that's memory pressure, an oversized index, or CPU contention. From there, you'll explore the two deployment architectures for MongoDB Vector Search and understand how your choice directly affects how much memory is available to your vector indexes. You'll also apply the two core optimization techniques: quantization, which reduces the memory footprint of your vector index by lowering the precision of each stored dimension, and partial indexing with views, which reduces the number of vectors indexed in the first place. By the end, you'll be equipped to confidently size, monitor, and tune MongoDB Vector Search deployments.
|
Upon completion of the Vector Search Performance skill and skill check, you will earn a Credly Badge that you are able to share with your network. |
Learning Objectives
Manage index size to ensure low-latency retrieval
Explain the importance of ensuring vector search indexes fit into available memory (RAM) for low-latency retrieval and implement strategies to manage index size to meet memory constraints in production environments.
Leverage Search Nodes to improve performance for vector search workloads
Select the optimal deployment approach for your vector search workload based on your performance requirements. Learn to compare a search node architecture versus a coupled architecture where your operational and search workloads are co-located on the same nodes as your core database nodes.
Optimize vector search performance:
Apply quantization and partial indexing with views to reduce index memory requirements and keep vector search performant as your data grows.
Who is this Course Good for?
If you are a backend developer, data engineer, or AI practitioner building search features with MongoDB, the Vector Search Performance Skill Badge is designed for you. You may already have a working vector search implementation, but are seeing latency spikes in production or struggling to understand why queries are hitting disk. Whether you are deploying for the first time or tuning an existing system, this skill will give you the diagnostic tools and optimization techniques to keep vector search fast at scale.
What to Expect in this Course
The skill begins with the mechanics of MongoDB Vector Search. You will learn how the HNSW algorithm builds a multi-layered graph to enable fast approximate nearest-neighbor search, how the two-process architecture of `mongod` and `mongot` divides available RAM, and why keeping your vector index resident in memory is the defining factor in query latency. You will also estimate the in-memory footprint of a vector index, so you can size your infrastructure before problems arise.
From there, you will build a diagnostic playbook using Atlas Metrics. Starting from a user-reported slow query, you will trace the problem through Search System Memory, Search Process Memory, Search Index Size, Search Page Faults, and Search Normalized Process CPU to identify whether the root cause is memory pressure, an oversized index, or CPU contention from concurrent indexing.
Next, you will compare the two deployment architectures for MongoDB Search: co-located nodes, where `mongod` and `mongot` share the same hardware, and dedicated Search Nodes, where `mongot` runs on isolated infrastructure with its own memory budget. You will learn how WiredTiger's cache reservation affects the memory available to your vector indexes on co-located nodes, and when the contention and cold-start problems that come with co-location make dedicated Search Nodes the right investment.
The final lessons cover the two core optimization techniques for reducing index memory requirements. You will learn how scalar (int8) and binary (int1) quantization reduce the memory footprint of a vector index by up to 24x, understand the recall trade-offs of each approach, and implement both automatic and pre-computed quantization in code. You will also use MongoDB views to build partial indexes, controlling exactly which documents enter the HNSW graph, through patterns like filtering out documents without embeddings and scoping indexes per tenant in a multi-tenant application.
Throughout the skill, concepts are reinforced through detailed video lessons and a hands-on skill check. By the end, you will be equipped to diagnose vector search performance problems using Atlas Metrics, choose the right deployment architecture for your workload, and apply quantization and views to keep your indexes lean and fast as your data scales.
Summary of the Course
- Explain how a vector search query travels from user input to results, including the role of mongot and the HNSW graph.
- Describe why RAM availability is the primary determinant of vector search query latency.
- Calculate the approximate in-memory footprint of a vector index given a model's dimensionality and number of vectors.
- Trace a user-reported slow vector search query to a root cause using Atlas monitoring metrics.
- Interpret the System Memory, Search Process Memory, Index Size, and Search Page Faults metrics to assess vector search health.
- Identify CPU bottlenecks caused by concurrent indexing and query workloads.
- Describe the difference between a co-located architecture and a dedicated Search Node architecture.
- Identify the trade-offs in resource contention, memory isolation, and operational costs between the two deployment models.
- Explain how vector dimensionality drives index storage cost and RAM requirements.
- Describe how scalar (int8) and binary (int1) quantization reduce index size and the recall trade-offs involved.
- Implement automatic quantization in a MongoDB Vector Search index definition.
- Store pre-computed quantized vectors as BSON binary values and create a vector index over them.
- Explain how MongoDB views can be used to control which documents are included in a vector search index.
- Choose between standard views and materialized views when building vector search indexes.
- Describe the trade-offs between dedicated Search Nodes, quantization, and views for vector search performance.
- Choose an appropriate combination of these techniques for a given workload, balancing cost, engineering effort, and risk.
- Explain which optimizations require infrastructure changes vs. application or data-model changes.
Emilio Scalise | Senior Technologist
Emilio is a multi-skilled IT specialist with a vast knowledge in system administration, databases, software development, network security, and cloud solutions. He is currently a Staff Technologist at MongoDB, producing internal and external learning materials. With over 8 years at MongoDB Support Organization, including five as a Staff Technical Support Engineer, he's developed considerable expertise in MongoDB's products and cloud services. In addition, Emilio is a certified MySQL DBA and experienced in technical translations between English and Italian.
Emily Pope | Lead Curriculum Designer
Emily Pope is a Lead Curriculum Designer at MongoDB. She loves learning and loves making it easy for others to learn how and when to use deeply technical products. Recently, she's been creating AI and vector search content for MongoDB University. Before that, she's created learning experiences on databases, computer science, full stack development, and even clinical trial design and analysis. Emily holds an Ed.M. in International Education Policy from Harvard Graduate School of Education and began her career as an English teacher in Turkiye with the Fulbright program.
Manuel Fontan Garcia | Senior Technologist
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).
Parker Faucher | University Curriculum Engineer
Parker is a Curriculum Engineer on the Education team at MongoDB. Prior to joining MongoDB, he helped maintain a world class developer bootcamp that was offered in multiple universities. He is a self taught developer who loves being able to give back to the community that has helped him so much.
Building a vector search feature that works is only half the battle. The moment your collection grows and your indexes outgrow available memory, query latency stops being predictable.
Users start filing tickets. Response times spike. The root cause isn't always obvious, and the fix isn't always what you'd expect. This skill badge is about understanding what's happening inside MongoDB Vector Search when a query runs and knowing what to do when things slow down.
We'll trace performance problems to their source, compare deployment architectures, and apply concrete optimization techniques.
Here's what we'll cover. We'll start by building the foundation, how MongoDB Vector Search works under the hood. You'll learn how HNSW (hierarchical navigable small world) indexes enable fast approximate nearest neighbor search and why RAM availability is an important factor in query latency.
From there, we'll build a diagnostic playbook using Atlas metrics to trace a user reported slow query through search system memory, index size, page faults, and CPU, all the way to a root cause. Next, we'll compare colocated nodes and dedicated search nodes, explaining how your deployment architecture directly affects how much memory is available to MongoDB and your vector indexes.
Then we'll cover quantization, a technique that reduces the number of bits used to store each vector dimension. After that, we'll put quantization into practice and walk through both approaches.
Automatic quantization using a field in your index definition and precomputed quantization using BSON bin data vectors.
Then we'll look at MongoDB Views as a tool for partial indexing. You'll see how to control exactly which documents enter your HNSW (hierarchical navigable small world) graph. We'll close by pulling everything together into a decision framework. Given your constraints, your data model, and your scale, which tool or combination of tools is the right fit?
In this skill badge, you'll learn concepts through detailed videos and then take a short skill check to demonstrate your knowledge.
After passing the test, you'll receive an official Credly badge to share on LinkedIn. Let's get started.
