Tag: mongodb

MongoDB and GraphQL: A Perfect Match

May 15, 2026 Nic Raboy

GraphQL is a powerful and efficient way to build APIs. The client queries the API, similarly to how they would a database, and that API returns only the data that they've requested, often reducing the response payload and improving response times. Low response times are critical in the modern world.

When the GraphQL API is paired with MongoDB, you're not only getting those fast response times, but you're also getting a data format that is consistent from start to finish.

Imagine this: Your client is executing a GraphQL query and that query looks similar to JSON. When the data reaches your application—which, let's say, is TypeScript in this example—you're now working with a data format that is similar to JSON in your application. Taking it a step further, when working with MongoDB, the data you send to and from MongoDB will also be similar to JSON. So what you're getting is that consistent data experience on top of performance. No need to worry too much about manipulating and formatting your data, and instead you get to focus on the user experience of your application, not the database and tooling.

In this tutorial, we're going to see just how easy it is to use MongoDB in your GraphQL API, this time built with TypeScript.

Building a REST API With Express Framework and MongoDB

May 8, 2026 Nic Raboy

Almost every modern web application will need a REST API for the frontend to communicate with, and in almost every scenario, that frontend is going to expect to work with JSON data. As a result, the best development experience will come from a stack that will allow you to use JSON throughout, with no transformations that lead to overly complex code.

Take MongoDB, Express Framework, and Node.js as an example.

Node.js and Express Framework handle your application logic, receiving requests from clients, and sending responses back to them. MongoDB is the database that sits between those requests and responses. In this example, the client can send JSON to the application and the application can send the JSON to the database. The database will respond with JSON and that JSON will be sent back to the client. This works well because MongoDB is a document database that works with BSON, a JSON-like data format.

In this tutorial, we'll see how to create an elegant REST API using MongoDB and Express Framework.

Using Dot Notation to Query Nested Fields in MongoDB

April 28, 2026 Nic Raboy

If you're just starting to dabble with MongoDB, you've probably come to a point where your documents are looking a little complex. These documents might have gone from flat, relational-looking pieces of data to something with nested objects, nested arrays, and maybe even four or five more levels of nesting. So how do you query this data when it is a few layers deep?

In this short tutorial, we're going to look at dot notation within MongoDB and see how we can very quickly and easily filter our documents based on data that contains certain values in the nested fields.

Build a Movie Watchlist with Node.js, TypeScript, and MongoDB

April 13, 2026 Nic Raboy

Almost every modern web application will need a REST API for a client to talk to, and in almost every scenario, that client is going to expect JSON. The best developer experience is a stack where you can stay in JSON-shaped data end to end, without awkward transformations in the middle.

Take MongoDB, Express Framework, and Node.js as an example.

Express receives HTTP requests and sends responses. MongoDB sits in the middle and stores documents. The client can send JSON to your routes, your routes can send documents to MongoDB, and MongoDB can hand BSON back that maps naturally to what you serialize in the response. That works well because MongoDB is a document database. When you also want text search over fields like title and plot, MongoDB Search gives you a $search stage in an aggregation pipeline on the same cluster, so you are not bolting on a separate search system just to power a search box.

In this tutorial, we'll see how to build a small movie watchlist API using TypeScript and MongoDB. We'll explore a few different schema design opportunities and make use of MongoDB Search for full-text search.

Migrating Your Content Management System (CMS) Assets With MongoDB and Node.js

May 9, 2025 Nic Raboy

Content platforms evolve as business strategies shift. At MongoDB, we embraced external publishing platforms like Dev.to, Medium, The Polyglot Developer, etc. to better engage developer communities, requiring us to redistribute content while maintaining our existing CMS data in MongoDB.

To support our multi-platform publishing strategy, we created a system to publish content between our MongoDB CMS and external platforms. As a result, we needed to migrate the content we had in our CMS to its new home. The migration process included exporting the written content stored in MongoDB and downloading a copy of the media assets that were stored on third-party servers.

In this tutorial, we'll explore the export process to get the job done with as little friction as possible.

Django MongoDB Backend Quickstart

May 6, 2025 Anaiya Raisinghani

Interested in diving into our Django MongoDB Backend integration? Follow along with this quickstart to create a Django application, connect that application to a MongoDB deployment, ensure your deployment is hosted on MongoDB Atlas, and interact with the data stored in your database using simple CRUD operations.

How to Make a RAG Application With LangChain4j

May 3, 2025 Tim Kelly

Retrieval-augmented generation, or RAG, introduces some serious capabilities to your large language models (LLMs). These applications can answer questions about your specific corpus of knowledge, while leveraging all the nuance and sophistication of a traditional LLM.

This tutorial will take you through the ins and outs of creating a Q&A chatbot using RAG. The application will:

1. Retrieve data from a MongoDB Atlas database.
2. Embed and store documents as vector embeddings.
3. Use LangChain4j to query the database and augment LLM prompts with the retrieved data.
4. Enable secure, scalable, and efficient AI-powered applications.

If you want to see the completed application, it is available in the GitHub repository.