Search Fundamentals
Learn the fundamentals of MongoDB Search, including lexical search, indexing strategies, and query optimization. Understand how to build efficient, full-text search experiences within MongoDB-powered applications.
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Upon completion of the Search Fundamentals skill and skill check, you will earn a Credly Badge that you are able to share with your network. |
Learning Objectives
Understand Lexical Search
Explain how lexical search works in MongoDB.
Implement Efficient Search Indexing Strategies
Create and optimize search indexes using MongoDB Search, use the correct index types and configurations to improve query performance.
Construct and Execute Search Queries
Build, structure, and execute search queries using the $search aggregation pipeline stage, including filters, faceting, and sorting for optimized search results.
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.
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.
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.
Most of us have encountered that frustrating moment where we type something into a search box and get zero results even though we know the content exists somewhere.
When users can't locate what they're seeking within those first crucial few seconds, they often abandon the experience entirely, never to return. For search to truly serve as the critical bridge connecting users to the products and information they need, it must be accurate and deliver relevant results quickly. Effective search features increase conversion rates, user satisfaction, and engagement time. For ecommerce sites, this means higher sales or for content platforms, it means users consume more content and return more frequently. Developers commonly face significant challenges when implementing search features. They often need to build and maintain custom search infrastructure or manage separate search services, which adds a heavy burden of complexity.
MongoDB's Atlas Search significantly reduces the complexity of implementing sophisticated search capabilities by allowing teams to integrate them directly into their existing MongoDB workflows. Atlas Search is a form of lexical search which analyzes and tokenizes text to match user queries against index terms, delivering precise and efficient search functionality that can be customized for your specific application needs.
In this skill, we'll show you how you can use Atlas Search to build search experiences directly within your application, all without managing a separate search infrastructure alongside your MongoDB database.
We'll start by introducing you to Atlas Search so you can understand how it works. We'll discuss search indexes, search queries, and look at how they work together to deliver fast, relevant results that transform basic database lookups into intelligent, user friendly search experiences. Next, we'll introduce you to the real world scenario that we'll use as a demo throughout this skill, A movie streaming platform called mFlix that needs to transform their basic catalog browsing into an intuitive search experience that helps users quickly discover films they'll actually want to watch. We'll begin by strategically planning our Atlas search implementation for this example, clearly defining the search experience we want to deliver to our users.
Here, we'll introduce you to a framework that you can adapt and apply when planning Atlas search integration in your own applications. We'll start by building a search index for our search feature.
We'll discuss the difference between dynamic and static mappings and show you how to decide which to use. You'll also learn about the different data types available for search indexes. Then, we'll show you how to use MongoDB's aggregation framework to create a search query that uses the search index that we created. Finally, we'll explore how to use facets when using Atlas Search to group and filter search results based on specific categories or attributes.
Once you're done, you'll have opportunities to practice creating indexes by completing hands on labs that present real world scenarios. When you've finished, you'll be ready to put your new skills to the test. To earn your badge, simply complete all the related content and then take the short test at the end. After passing this test, 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 working understanding of search fundamentals in MongoDB. Let's get started.
