MongoDB Developer Blog
Deep dives into technical concepts, architectures, and innovations with MongoDB.
Enhance Your In-IDE Data Browsing Experience With MongoDB
MongoDB is excited to announce the general availability of our enhanced data browsing experience in the MongoDB for Visual Studio (VS) Code extension. This new experience offers a unified workspace for developers to visually browse, query, and edit their data natively, streamlining workflows so they can manage their database right where they write their code.
Why MongoDB Atlas is the Native Home for ISO 20022 Compliance
For decades, global financial institutions relied on messaging standards defined by SWIFT to exchange information about cross-border payments. These legacy standards—a.k.a. MT messages designed in the 1970s—carried payment instructions in largely unstructured formats that required downstream systems to interpret free-text fields.
Modelence: A Complete Platform for Agentic App Development
As modern applications become increasingly data-driven and AI-powered, development teams face a growing challenge: how to move quickly from idea to production without stitching together multiple tools, managing complex infrastructure, or reinventing backend workflows. Modelence offers a new approach. It is a full‑stack, AI‑native development platform that brings together every core component needed to build, run, and scale modern applications in one unified system. Whether teams want to start a new project using a traditional development workflow or prefer a vibe‑coding approach powered by its AI-native App Builder, Modelence supports both seamlessly. To show you how this works in practice, we have included two examples for each workflow that you can explore in more detail later in this blog.
Automotive After Sales Diagnostics Using GraphRAG and Multimodal AI
Modern vehicles act as distributed computing systems and generate terabytes of telemetry. However, the majority of after-sales diagnostic and repair workflows still depend on static documentation and basic keyword search. In 2025, J.D. Power reported that 12% of repairs are not completed correctly on the first visit.1 These repeat repairs increase costs, reduce workshop throughput, and erode customer trust.
High vs Low Ingestion: A Practical Study of MongoDB Time Series Bucket Behavior
Time series data captures any signal, metric, or observation whose state changes continuously over time. Infrastructure metrics, IoT sensor readings, financial market data, observability signals, and distributed system telemetry all qualify. What they share is the need to record an ordered sequence of measurements efficiently.
db.youtube.insert(): Our Developer YouTube Channel is Officially Live
If you’ve spent any time learning MongoDB on YouTube, you’ve likely visited our main channel. It’s been the hub for all video content—from company news and keynote highlights to the tutorials that help you get your first cluster up and running.
Near Real-time Analytics Powered by Mirroring in Microsoft Fabric for MongoDB Atlas
MongoDB’s accelerator for mirroring enables customers to bring operational data from MongoDB Atlas to Microsoft Fabric in near real-time for big data analytics, AI, and business intelligence (BI), combining it with the rest of the data estate of the enterprise. Open mirroring in Fabric provides a unique way to import data from operational data stores to the uniform data layer of OneLake in Fabric. Once mirroring is enabled for a MongoDB Atlas collection, the corresponding table in OneLake stays in sync with the changes in the source MongoDB Atlas collection, unlocking opportunities for various analytics and for AI and BI in near real-time.
Port Mapping for Google Private Service Connect on MongoDB Atlas
For organizations leveraging MongoDB Atlas on Google Cloud, network architecture is a critical component of performance and scalability. Today, we are excited to announce a significant architectural enhancement that simplifies the connection between these two platforms. This new feature, Port Mapping for Private Service Connect (PSC), reduces developer efforts and enables faster scaling by streamlining connection management and resource allocation.
A How-To Guide to Building Fast, Cheap, and Accurate Retrieval
Building Gen AI prototypes is straightforward. Whether you're building search, RAG, or agentic applications, the main focus when prototyping is often accuracy. But production is different. In production, you’re handling thousands or millions of queries instead of a handful of tests. Your users expect accurate responses, and they want them instantly. This requires optimizing for three things at once: accuracy, speed, and operating costs.
Building a Movie Recommendation Engine with Hugging Face and Voyage AI
This guest blog post is from Arek Borucki, Machine Learning Platform & Data Engineer for Hugging Face - a collaboration platform for the machine learning community. The Hugging Face Hub works as a central place where anyone can share, explore, discover, and experiment with open-source ML. HF empowers the next generation of machine learning engineers, scientists, and end users to learn, collaborate and share their work to build an open and ethical AI future together. With the fast-growing community, some of the most used open-source ML libraries and tools, and a talented science team exploring the edge of tech, Hugging Face is at the heart of the AI revolution. Traditional movie search relies on filtering by genre, actor, or title. But what if you could search by how you feel? Imagine typing: "something uplifting after a rough day at work" "a movie that will make me cry" "I need adrenaline, can't sleep anyway" "something to watch with grandma who hates violence" This is mood-based semantic search: matching your emotional state to movie plot descriptions using AI embeddings. In this tutorial, you will build a mood-based movie recommendation engine using three powerful technologies: voyage-4-nano (a state-of-the-art open-source embedding model), Hugging Face (for model and dataset hosting), and MongoDB Atlas Vector Search (for storing and querying embeddings at scale). Why mood-based search? Genre tags are coarse. A "drama" can be heartwarming or devastating. A "comedy" can be light escapism or dark satire. Traditional filters cannot capture these nuances. Semantic search solves this by understanding meaning. When you search for "feel-good movie for a rainy Sunday", the system doesn't look for those exact words. It understands the intent and matches it against plot descriptions that evoke similar feelings. Architecture overview The system combines three components from the Hugging Face ecosystem with MongoDB: voyage-4-nano (Hugging Face Hub): Converts text to embeddings (up to 2048 dimensions, we use 1024) MongoDB/embedded_movies(Hugging Face Datasets): 1500+ movies with plot summaries, genres, cast MongoDB Atlas Vector Search: Stores embeddings and performs similarity search Understanding voyage-4-nano voyage-4-nano is the smallest model in Voyage AI's latest embedding series, released with open-weights under the Apache 2.0 license. Voyage AI was acquired by MongoDB, and the Voyage 4 series models are now available through MongoDB Atlas. All models in the series (voyage-4-large, voyage-4, voyage-4-lite, and voyage-4-nano) produce compatible embeddings in a shared embedding space, allowing you to mix and match models within a single use case. Although voyage-4-nano natively supports embeddings up to 2048 dimensions, we deliberately truncate them to 1024 dimensions using its Matryoshka embedding property. In practice, this provides a strong balance between semantic quality, storage efficiency, and vector search latency, while preserving stable ranking behavior. Sentence Transformers This tutorial uses Sentence Transformers, a Python library built on top of Hugging Face Transformers. It is specifically designed for working with embedding models and provides a simple API for generating text embeddings. Why Sentence Transformers instead of raw Transformers? When working with embedding models, you need to handle tokenization, pooling, normalization, and prompt formatting. Sentence Transformers does all of this automatically in a single method call. The code is cleaner, there are fewer potential errors, and you get built-in features like batch processing with progress bars. Under the hood, Sentence Transformers still uses Hugging Face Transformers to load and run the model. Configure the development environment Let's get started! Create the Project Structure Code Snippet 1 mkdir mood-movie-search 2 cd mood-movie-search 3 mkdir src 4 touch requirements.txt .env Install dependencies Create the requirements.txt file: Code Snippet 1 cat < requirements.txt 2 fastapi>=0.109.0 3 uvicorn>=0.27.0 4 pymongo>=4.6.1 5 sentence-transformers>=3.0.0 6 python-dotenv>=1.0.0 7 datasets>=2.16.0 8 torch 9 EOF Create a Python virtual environment and install dependencies: to be continued...
Optimizing the MongoDB Java Driver: How minor optimizations led to macro gains
Donald Knuth, widely recognized as the ‘father of the analysis of algorithms,’ warned against premature optimization—spending effort on code that appears inefficient but is not on the critical path. He observed that programmers often focus on the wrong 97% of the codebase. Real performance gains come from identifying and optimizing the critical 3%. But, how can you identify the critical 3%? Well, that’s where the philosophy of ‘never guess, always measure’ comes in.