MongoDB Updates
The newest releases and freshest updates
Vector Search and Dedicated Search Nodes: Now in General Availability
Today we’re excited to take the next step in adding even more value to the Atlas platform with the general availability (GA) release of both Atlas Vector Search and Search Nodes. Since announcing Atlas Vector Search and dedicated infrastructure with Search Nodes in public preview, we’ve seen continued excitement and demand for additional workloads using vector-optimized search nodes. This new level of scalability and performance ensures workload isolation and the ability to better optimize resources for vector search use cases. Atlas Vector Search allows developers to build intelligent applications powered by semantic search and generative AI over any data type. Atlas Vector Search solves the challenge of providing relevant results even when users don’t know what they’re looking for and uses machine learning models to find results that are similar for almost any type of data. Within just five months of being announced in public preview, Atlas Vector Search has already received the highest developer net promoter score (NPS) — a measure of how likely someone is to recommend a solution to someone else — and is the second most widely used vector database, according to Retool’s State of AI report . There are two key use cases for Atlas Vector Search to build next-gen applications: Semantic search: searching and finding relevant results from unstructured data, based on semantic similarity Retrieval augmented generation (RAG): augment the incredible reasoning capabilities of LLMs with feeds of your own, real-time data to create GenAI apps uniquely tailored to the demands of your business. Atlas Vector Search unlocks the full potential of your data, no matter whether it’s structured or unstructured, taking advantage of the rise in popularity and usage of AI and LLMs to solve critical business challenges. This is possible due to Vector Search being part of the MongoDB Atlas developer data platform, which starts with our flexible document data model and unified API providing one consistent experience. To ensure you unlock the most value possible from Atlas Vector Search, we have cultivated a robust ecosystem of AI integrations, allowing developers to build with their favorite LLMs or frameworks. Our ecosystem of AI integrations for Atlas Vector Search To learn more about Atlas Vector Search, watch our short video or jump right into the tutorial . Atlas Vector Search also takes advantage of our new Search Nodes dedicated architecture, enabling better optimization for the right level of resourcing for specific workload needs. Search Nodes provide dedicated infrastructure for Atlas Search and Vector Search workloads, allowing you to optimize compute resources and fully scale search needs independent of the database. Search Nodes provide better performance at scale, delivering workload isolation, higher availability, and the ability to better optimize resource usage. In some cases we’ve seen 60% faster query time for some users' workloads, leveraging concurrent querying in Search Nodes. In addition to the compute-heavy search nodes we provided in the public preview, this GA release includes a memory-optimized, low CPU option that is optimal for Vector Search in production. This makes resource contention or the possibility of a resulting service interruption (due to your database and search sharing the same infrastructure previously) a thing of the past. Coupled Architecture (left) compared with the decoupled Search Node architecture (right) We see this as the next evolution of our architecture for both Atlas Search and Vector Search, furthering the value provided by the MongoDB developer data platform. At this time Search Nodes are currently available on AWS single-region clusters (with Google Cloud and Azure coming soon), as customers can continue using shared infrastructure for Google Cloud and Microsoft Azure. Read our initial announcement blog post to view the steps of how to turn on Search Nodes today, or jump right into the tutorial . Both of these features are available today for production usage. We can’t wait to see what you build, and please reach out to us with any questions.
You Asked, We Listened. It's Here - Dark Mode for Atlas is Now Available in Public Preview
We are thrilled to announce a much-anticipated feature for MongoDB Atlas. Dark mode is now available in Public Preview for users worldwide. Dark mode has been the number one requested feature in MongoDB's feedback forum , and we've taken note. Users have tried browser plugins and other makeshift fixes, but now the wait is over. Our development team diligently worked to introduce a dark mode option, improving user experience with a new and refreshing perspective to the familiar interface of Atlas. This update—which includes 300 converted pages—is not just for our community. It also benefits us as developers, promoting a seamless dark mode experience across different tools in the developer workflow. Dark mode is sleek and sophisticated, aligning with the preferred working styles of many of our developers. Remember that this is an ongoing project, and there may be areas within Atlas that need refining. Rest assured, we will be monitoring our feedback channels closely. Not just a sleek interface We took a thoughtful approach to the overall dark mode user experience, particularly with respect to accessibility considerations. We ensured that our dark mode theme met accessibility standards by checking and adjusting all text, illustrations, and UI elements for color and contrast to help reduce eye strain and address those with light sensitivities while making sure it was still easy to read. We also focused on accommodating the overall light-to-dark background contrast while staying mindful of how they may layer or interact with other elements. Beyond aesthetics, dark mode is a proven method for extending battery life. For our users with OLED or AMOLED screens dark mode ensures the device’s battery life stretches even further by illuminating fewer pixels and encouraging lower brightness levels. Health benefits A typical engineer spends no fewer than eight hours a day in front of a computer, exposing their eyes to multiple digital screens, according to data from Medium . This screen usage can lead to dry eyes, insomnia, and headaches. While dark text on a light background is best for legibility purposes, light text on a dark background helps reduce eye strain in low-light conditions. Enable dark mode preview today To update the theme at any time, navigate to the User Menu in the top right corner, then select User Preferences . Under Appearance , there will be three options. Light Mode: This is the default color scheme. Dark Mode: Our new dark theme. Auto (Sync with OS): This setting will match the operating system's setting. A few things to keep in mind This is a user setting and does not impact other users within a project or organization. Dark mode is not currently available for Charts, Documentation, University, or Cloud Manager. Since we are releasing this in Public Preview , there might be some minor visual bugs. The goal of Public Preview releases is to generate interest and gather feedback from early adopters. It is not necessarily feature-complete and does not typically include consulting, SLAs, or technical support obligations. We have conducted comprehensive internal testing, and we did not find anything that prevents users from using Atlas. While we are still making a few finishing touches feel free to share any feedback using this form . Thank you to all our users who provided valuable feedback and waited patiently for this feature! Keep the feedback coming . We hope you enjoy dark mode, designed to improve accessibility, reduce eye strain and fatigue, and enhance readability. We invite you to experience the difference. Try dark mode today through your MongoDB Atlas portal .
Perfect Your CI/CD Pipelines with MongoDB's New GitHub Action and Docker Image for the Atlas CLI
Do you use GitHub Actions for your CI/CD workflows? Or build using Docker containers? If so, you’ll probably be excited to hear that MongoDB has released: 1. An official GitHub Action and 2. A dedicated Docker image for the Atlas CLI. Together, these two releases make it easier than ever to develop applications with MongoDB Atlas. Since MongoDB announced the Atlas CLI at MongoDB World in 2022, it has become one of our most popular tools for building with the Atlas developer data platform. One of the great things about the Atlas CLI is that it not only caters to the individual developer wanting a mouseless terminal experience—it also makes it easy to programmatically manage Atlas resources throughout the entire development lifecycle. With the new releases for the Atlas CLI with GitHub Actions and Docker, you can easily use the Atlas CLI to build with Atlas while still working natively within your preferred CI/CD platforms. Within GitHub Actions, you now have access to a dedicated Action that allows you to seamlessly manage Atlas resources using your favorite Atlas CLI commands. You can use the predefined workflows available or create custom workflows leveraging native Atlas CLI commands. For example, with one of the predefined workflows you can: create a project, set up the Atlas CLI with an Atlas deployment, retrieve your connection string, and tear down your project and deployment. If you use a platform other than GitHub Actions to manage your CI/CD pipelines, or simply use Docker in your toolchain, you can now also use the Atlas CLI by pulling the Docker image with just one command: docker pull mongodb/atlas From there, you can enter an interactive shell to run Atlas CLI commands as you normally would: docker run --rm -it mongodb/atlas bash atlas --help You can also find detailed information in the MongoDB Documentation on how to run Docker in interactive mode or as a daemon (detached mode) for working with the Atlas CLI. Ready to get started? You can find the Atlas CLI GitHub Action in the GitHub Marketplace and the Atlas CLI Docker image on Docker Hub . If you have any feedback on either experience, share your thoughts with us in the Atlas CLI section of the MongoDB Feedback Engine .
MongoDB Laravel Integration Now Officially Supported
We are excited to share that MongoDB has taken over development of the community-driven MongoDB integration for Laravel framework, making it a first-class citizen in our product portfolio. Formerly known as jenssegers/laravel-mongodb , the library delivers a seamless experience for the PHP developers using MongoDB by extending Eloquent , Laravel’s built-in ORM, adding functionalities like Eloquent models, query builder, and transactions. As many in the Laravel community must be aware, the library was in need of a higher degree of support and investment; users have been requesting ongoing maintenance and updates, and we've heard you loud and clear. We understand the importance of an official, robust, and well-maintained library that integrates seamlessly with Laravel. With MongoDB taking over the library, you can expect regular updates with improvements to the functionality, bug fixes, and compatibility with most recent Laravel and MongoDB releases. We are strongly committed to our PHP Laravel community, providing you with the tools to have a modern and elegant developer experience with MongoDB. Thanks to everyone who contributed to the development of the library as we highly value community contribution and engagement. To use MongoDB with Laravel framework, check out the latest release of this library, which added support for Laravel 10 - Laravel MongoDB 4.0.0 . If you’re just getting started with MongoDB PHP projects, we have a tutorial on how to build a Laravel + MongoDB back end service and documentation for the library. Give it a try today and let us know what you think! Please report any ideas, bugs, or feedback in the PHPORM JIRA project.
Announcing LangChain Templates for MongoDB Atlas
Since announcing the public preview of MongoDB Atlas Vector Search back in June, we’ve seen tremendous adoption by developers working to build AI-powered applications. The ability to store, index, and query vector embeddings right alongside their operational data in a single, unified platform dramatically boosts engineering velocity while keeping their technology footprint streamlined and efficient. Atlas Vector Search is used by developers as a key part of the Retrieval-Augmented Generation (RAG) pattern. RAG is used to feed LLMs with the additional data they need to ground their responses, providing outputs that are reliable, relevant, and accurate for the business. One of the key enabling technologies being used to bring external data into LLMs is LangChain. Just one example is healthcare innovator Inovaare who is building AI with MongoDB and LangChain for document classification, information extraction and enrichment, and chatbots over medical data. Now making it even easier for developers to build AI-powered apps, we are excited to announce our partnership with LangChain in the launch of LangChain Templates ! We have worked with LangChain to create a RAG template using MongoDB Atlas Vector Search and OpenAI . This easy-to-use template can help developers build and deploy a Chatbot application over their own proprietary data. LangChain Templates offer a reference architecture that’s easily deployable as a REST API using LangServe . We have also been working with LangChain to release the latest features of Atlas Vector Search, like the recently announced dedicated vector search aggregation stage $vectorSearch, to both the MongoDB LangChain python integration as well as the MongoDB LangChain Javascript integration . Similarly, we will continue working with LangChain to create more templates, that will allow developers to bring their ideas to production faster. If you’re building AI-powered apps on MongoDB, we’d love to hear from you. Sign up to our AI Innovators program where successful applicants receive no-cost MongoDB Atlas credits to develop apps, access to technical resources, and the opportunity to showcase your work to the broader AI community.
Search Nodes Now in General Availability: Performance at Scale with Dedicated Infrastructure
We’re excited to announce Search Nodes are now in General Availability for AWS, including a memory optimized, low CPU option that is optimal for Vector Search (also in GA). This makes resource contention or the possibility of a resulting service interruption a thing of the past. Read the blog below for the full announcement and list of benefits. While scalability has become a common buzzword in today’s enterprise vernacular, it’s something we take extremely seriously at MongoDB. Whether it’s increasing a certain capability to be used in additional contexts, or continuing to increase the capacity of a certain technology in size or scale, our product teams are always looking to maximize scalability for our customers’ most demanding workloads. Today we are excited to take the next step in this journey with the announcement of Search Nodes , now available in GA. Search Nodes provide dedicated infrastructure for Atlas Search and Vector Search workloads, allowing you to fully scale search independent of database needs. Incorporating Search Nodes into your Atlas deployment allows for better performance at scale, and delivers workload isolation, higher availability, and the ability to optimize resource usage. We see this as the next evolution of our architecture for both Atlas Search and Vector Search, furthering our developer data platform, including the benefits of a fully managed sync without the need for an ETL or index management. We have listened to the feedback from our customer base and are excited to take the next step in bringing this feature closer to general availability. So what exactly is changing, and what are the benefits of Search Nodes? To see where we’re going, let’s take a brief look at where we have been. Previously, Atlas Search (mongot) has been co-located with Atlas (mongod) on Atlas Nodes (see diagram below). The pros of this configuration are that it is simple and cheap, enabling a large portion of our current user base to get started quickly. Figure 1: Diagram of architecture Atlas Search configuration on Atlas Nodes However, there are a couple of consequences from this setup. Because Search and Vector Search are co-located on Atlas Nodes and clusters, users have to try and size their workload based on both Search and Database requirements using traditional Atlas deployment. This introduces potential issues, including the possibility of resource contention between a database and search deployment, which has the potential to cause service interruptions. It also becomes difficult having both resources commingled, as you lack the granularity to set limits on the share of the overall workload from your database or search. With our announcement of Search Nodes available in GA, these considerations are a thing of the past, as we now offer the developer greater visibility and control, with benefits including: Workload isolation Better performance at scale (40% - 60% decrease in query time for many complex queries) Higher availability Improved developer experience Figure 2: Diagram of dedicated architecture with Search Nodes Getting started with Search Nodes is super simple — to begin, just follow these steps in the MongoDB UI: Navigate to your “Database Deployments” section in the MongoDB UI Click the green “+Create” button On the “Create New Cluster” page, change the radio button for AWS for “Multi-cloud, multi-region & workload isolation” to enabled Toggle the radio button for “Search Nodes for workload isolation” to enabled. Select the number of nodes in the text box Check the agreement box Click “Create cluster” For existing Atlas Search users, click “Edit Configuration” in the MongoDB Atlas Search UI and enable the toggle for workload isolation. Then the steps are the same as noted above. Figure 3: How to enable Search Nodes in the Atlas UI We’re excited to be offering customers the option of dedicated infrastructure that Search Nodes provides and look forward to seeing the next wave of scalability for both Atlas Search and Vector Search workloads. We’ll also be announcing a more cost and performance efficient configuration for Vector Search coming soon. For further details you can jump right into our docs to learn more. We can’t wait to see what you build!
MongoDB Provider for Entity Framework Core Now Available in Public Preview
We are pleased to announce that the MongoDB Provider for Entity Framework Core (EF Core) is now available in Public Preview. This makes it possible for developers using EF Core to build C#/.NET applications with MongoDB and take advantage of our powerful developer data platform while continuing to use APIs and design patterns they already know and love. Building for the C#/.NET community Nearly one-third of all developers use C# to build applications, with the population of C# developers reaching upwards of 10 million developers worldwide . Forty-one percent of C# developers use EF Core , which is beloved as an abstraction layer to simplify working with data during development. In the past, C# developers could use MongoDB’s C# driver but didn’t have first-party support for EF Core; some turned to community-built projects that could be helpful but lacked official backing or ongoing support from MongoDB. With the official MongoDB Provider for EF Core now available in Public Preview, developers can use C# and EF Core with confidence when building with MongoDB. What's in the New Provider for EF Core In this initial Public Preview release, the MongoDB Provider for EF Core offers developers the following capabilities: Support for code-first workflows : Allows users to build without an initial database; you first create the classes for your application and then match your data model to the classes, not the other way around. Basic CRUD methods: Basic create, read, update, and delete (CRUD) operations are supported. String and numeric type operators: String and numeric type operators needed for basic CRUD operations will be supported. We anticipate adding support for more complex operators in future iterations of the Provider. Embedded documents: The Provider supports embedded documents, making it easier to store related information in the same database record. Class mapping and serialization: Your classes in C# will map to MongoDB in a predictable way, including when working with IDs as well as date and/or time values. LINQ query support: The Provider will support LINQ3 queries with fluent query syntax. Change tracking: The Provider allows you to track and save changes made to entities with each DbContext instance back to your MongoDB database. And this is just a start. Stay tuned for more advanced functionality when we release the MongoDB provider in General Availability (targeted for 2024). Benefits of using the provider for EF Core With the MongoDB Provider for EF Core, C# developers can unlock the full power of MongoDB's developer data platform to build modern applications while leveraging a familiar API interface, query paradigm (LINQ), and design patterns. Developers looking to modernize their data layer can do so with MongoDB while remaining free from cloud vendor lock-in since MongoDB works with all major cloud providers and for multi-cloud deployments How to get started with MongoDB Provider for Entity Framework Core All you need to do is download the MongoDB Provider for EF Core from the NuGet package manager and build a DbContext that points to a MongoDB Provider instance. The Provider connects to MongoDB and handles the rest, so you can quickly harness the joint value of EF Core and MongoDB. You can learn more by diving into our documentation . After you try the new Provider for EF Core, feel free to leave us feedback in our user feedback portal . Your input is important for helping us continue to improve the product experience. Get started today to unleash the power of your data with MongoDB and EF Core.
Recap of Product Announcements at MongoDB.local London, 2023
This post is also available in: Deutsch , Français , Español , Português . We’re now more than three months into our MongoDB.local world tour that kicked off in NYC earlier this June. Since then, we’ve continued to introduce product enhancements and new capabilities, from the GA of MongoDB for VS Code to MongoDB 7.0 and Queryable Encryption . Today, we're excited to share the highlights of recent product announcements from our London conference this morning. Efficient and intelligent developer experiences for building with MongoDB We’ve always been committed to providing the best developer experience because we know that developer time is one of the most precious commodities in any organization. When we looked at the most common tasks developers perform on a daily basis, we recognized two areas for improvement: making development against Atlas more efficient and making it easier to write MongoDB queries. We want to give developers the most ergonomic way to work with MongoDB Atlas throughout their entire journey. For many developers, that journey begins by working with MongoDB locally before moving to the cloud - which is why we’re investing in a great local development experience. Starting today , developers can use the Atlas CLI to manage local development environments with the same experience as Atlas clusters in the cloud. Beyond making it easy to deploy and manage development instances, we also want to bring the breadth of our developer data platform to local environments. The new Atlas CLI experience, available in public preview, also comes with integrated Atlas Search and Atlas Vector Search so developers can create and manage search indexes and queries within their development workflows. This is the first of more investments to come as we continue to build a seamless experience for services in Atlas from sandbox to testing and production. The other problem we want to solve is speed, and we’re excited to use generative AI technology to introduce several new intelligent developer experiences . Querying data should be as easy as asking a question in a language that feels natural to you. Developers can now ask questions in plain English and Compass , our MongoDB GUI, will generate the corresponding query in MongoDB query language syntax. From simple queries to more complex aggregations, this experience will reduce the friction of learning MongoDB’s query language and help developers iterate and build new features more quickly. We’re also introducing a new language interface for Atlas Charts so developers can easily visualize data in MongoDB and an AI chatbot for our documentation resources. For customers embarking on a migration journey from using relational databases to using MongoDB, one of the most difficult and important steps is converting hundreds, if not thousands, of queries and application code. Available now in private preview, SQL query conversion in Relational Migrator can convert queries and stored procedures to MongoDB query language syntax at scale, shifting resources from query creation to review and implementation. Run MongoDB anywhere - from edge to cloud One of the benefits of MongoDB that we’ve been proud of since the beginning is the flexibility to build with it anywhere - on a local machine for development, fully managed across multiple public clouds , on-premises or in a private cloud, and even on mobile and edge devices. As mobility and IoT become more essential to operations across industries, one of the key requirements is being able to sync and move data across environments. Today , we’re excited to announce Atlas for the Edge , which brings data processing and storage capabilities closer to where it’s often most needed - right where data is generated. With Atlas Edge Servers that can be deployed anywhere and built-in conflict resolution, customers can easily create hub and spoke architectures to power customer experiences that require ultra-low latency or heavier computation close to where data is generated. From manufacturing to retail to healthcare , Atlas for the Edge enables customers to unlock more use cases that rely on a connected data layer across public clouds, on-premise or edge computing locations, and sensors and devices. Build the next generation of AI-powered applications with a developer data platform Since our public preview announcement earlier this year, we’ve seen a lot of interest in Atlas Vector Search, particularly in building RAG (retrieval augmented generation) architectures for applications powered by Generative AI . From startups to established companies, customers are eager to build more intelligent applications with the backing of a modern, highly scalable, and performant platform. The ability to store vector embeddings alongside source and metadata has simplified how developers build GenAI into new and existing applications, and with the introduction of the $vectorSearch aggregation stage, it will be even easier to pre-filter and tune results using the MongoDB query language, all in a single platform on Atlas. Finally, we recognize the need to empower developers with practical resources to expand their skills and knowledge. In addition to new content available on MongoDB University , we announced MongoDB Press , a medium for publishing technical and leadership knowledge about MongoDB. The first two books are on aggregations and mastering MongoDB 7.0. We also added a solutions library on our website with use cases organized by industry verticals to show the art of what’s possible with our developer data platform. To see more announcements and get the latest product updates, visit our What’s New page. Head to the MongoDB.local hub to see where we'll be showing up next.
New Intelligent Developer Experiences for Compass, Atlas Charts, Relational Migrator, and Docs
This post is also available in: Deutsch , Français , Español , Português . Today, MongoDB announced a range of innovations in its developer data platform, creating new, intelligent developer experiences in familiar tools like MongoDB Compass, Atlas Charts, Relational Migrator, and MongoDB Documentation that radically simplify and accelerate how developers build modern applications. These new experiences provide developers with guided and intelligent assistance for their development processes in: MongoDB Compass: Where developers can use natural language to compose everything from simple queries to sophisticated, multi-stage aggregations. MongoDB Relational Migrator: Where developers can convert SQL queries to MongoDB Query API syntax. MongoDB Atlas Charts: Where developers can use natural language to generate basic data visualizations. MongoDB Documentation: Where developers can ask questions to an intelligent chatbot, built on top of MongoDB Atlas and Atlas Vector Search, to enable lightning-fast information discovery and troubleshooting during software development. Developer time is one of the most precious commodities in any organization, and with business and customer expectations continuing to rise, developers are under increasing pressure to deliver applications quickly. With more intelligent experiences across the MongoDB developer data platform, it is now simpler and easier than ever to build modern applications for virtually any use case. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Natural Language Queries in Compass Building queries and aggregations is one of the most prominent developer use cases for Compass , MongoDB’s popular, downloadable GUI tool. Compass’ new, intelligent experience allows developers to use natural language to compose sophisticated aggregations to query, transform, and enrich data, reducing the complexity and learning curve to build queries into application code. The new experience is being released in Public Preview in version 1.40.0 and will be rolled out incrementally to users starting today until the end of October. To get started, make sure you have 1.40.0 downloaded on your machine and have access to the feature. Then you can navigate to the Documents tab and click on the Generate Query button in the query bar, which opens a second bar below the standard query bar where you can enter natural language prompts to generate the Query API syntax for you to execute against your data. Be sure to hit the “thumb’s up” or “thumb’s down” button to rate the helpfulness of the query generated. SQL Query Conversion in Relational Migrator Migrations are part of many developers’ journeys with MongoDB. Earlier this summer at MongoDB.Local NYC, we announced Relational Migrator to help teams with these projects, and we’re continuing to make it easier to modernize application code. Many legacy systems have hundreds, if not thousands of SQL queries that must be modernized as part of any migration effort, and that can be a time-consuming, if not daunting task. Now in Private Preview, developers can use Relational Migrator to convert existing SQL queries and stored procedures into development-ready MongoDB Query API syntax. With SQL query conversion, developers can leverage Relational Migrator to eliminate the manual effort of creating MongoDB queries at scale - speeding up migration projects. SQL query conversion is currently available in Private Preview, and access can be requested directly from the latest version of Relational Migrator. Natural Language Support in Atlas Charts Atlas Charts is the best way for developers to visualize Atlas data. By offering an effortless and powerful solution for gaining data-driven insights, Charts empowers developers and the businesses they help scale. What has always been easy is now becoming more intelligent too! Available in Private Preview, a new natural language mode allows developers to visualize their data through a simple language query, for example: “show me a comparison of annual revenue by country and product.” This is just the start. Later this year, natural language support will extend to more complex queries and chart types. Sign up today to try out natural language support for building charts! Stay tuned for more updates from the team and check out our documentation to learn more about what’s supported by natural language during Private Preview! Intelligent Chatbot for MongoDB Documentation Documentation is critical to the developer experience, making it easier to discover product features and capabilities and troubleshoot common challenges during software development. MongoDB is now super-charging your experience with an intelligent chatbot that improves information discovery by surfacing and summarizing the most relevant documentation. Built with MongoDB Atlas and Atlas Vector Search, the chatbot allows you to ask questions in natural language like “How do I get started with MongoDB Atlas?” or “How do I add a new IP address to the IP access list for my Atlas project?” and receive a response with reference articles, code examples, and other relevant information. MongoDB will also be open-sourcing and providing educational materials about how we built the intelligent chatbot, making it that much easier for others in the community to use the power of MongoDB Atlas and Atlas Vector Search to create dynamic and educational experiences for their end users. Data Privacy and Security MongoDB is trusted by some of the world's most security-conscious organizations, who use the developer data platform’s robust data security and privacy controls to manage their most sensitive data assets. To maintain this trust, these new developer experiences will always be transparent about what data is accessed and used, allowing customers to make informed decisions within the boundaries of their unique security, privacy, and compliance concerns. Get Started Today With new, intelligent features that allow developers to interact with their data using natural language in Compass, Relational Migrator, and Charts, as well as an intelligent chatbot for MongoDB Documentation, it’s easier than ever to take advantage of the flexibility and scalability of MongoDB's document data model to build any class of application. If you have feedback on these experiences, you can enter a suggestion in our user feedback portal .
Introducing a Local Experience for Atlas, Atlas Search, and Atlas Vector Search with the Atlas CLI
This post is also available in: Deutsch , Français , Español , Português . Today, MongoDB is pleased to announce in Public Preview a new set of features for building software locally with MongoDB Atlas, giving developers greater flexibility and reducing operational overhead throughout the entire software development lifecycle. Developers can now develop locally with MongoDB Atlas deployments, including Atlas Search and Vector Search , using the Atlas CLI , empowering them to create full-text search or AI-powered applications no matter their preferred environment for building with MongoDB. Developers can use the Atlas CLI to set up, connect to, and automate common management tasks from early development through testing, staging, and production. For full-text search use cases, developers can now use the Atlas CLI to create and manage Atlas Search indexes regardless of whether they are working locally or in the cloud. Similarly, developers building applications powered by semantic search and generative AI on MongoDB can now use the Atlas CLI to create and manage local development instances with Vector Search indexes regardless of their development environment. Developer time is one of the most precious commodities in any organization building innovative new application experiences. But all too frequently, developers are burdened with managing repeatable tasks such as setting up development environments. They also often have to wrestle with the cognitive overhead of switching between different user experiences for local versus cloud development, distracting from delivering value. By giving developers the power of Atlas at their fingertips no matter their preferred development environment, MongoDB continues to expand the scope and capabilities of its developer data platform while placing a premium on developer experience. Create a Local Atlas Database Ready to create a local Atlas database, but don’t have the Atlas CLI yet? It’s easy to install with your favorite package manager. To install the Atlas CLI with Homebrew, use the following command: brew install mongodb-atlas In addition to installing via the Homebrew package manager, you can install the MongoDB Atlas CLI via Apt, Yum, Chocolatey, directly downloading the binary, or pulling the Docker image (learn more about our documentation ). You can also download it directly from the MongoDB Download Center . To create a local Atlas deployment with default settings in interactive mode, enter: atlas deployments setup --type local If you want to list your Atlas deployments enter: atlas deployments list If you’re authenticated to Atlas, you will see both your local and cloud Atlas deployments. If you aren’t authenticated to Atlas, you will only see your local deployments. Get Started with Local Atlas Search Building an application with a full-text search feature powered by Atlas Search? If you’re a developer who tends to build and prototype locally, you may be interested in using the Atlas CLI to work with Atlas Search in your local environment. To get started, first, connect to the local deployment on which you’d like to create a Search index: atlas deployments connect Next, you can use the MongoDB Shell to create your Search index. Below you’ll see an example of how to create an Atlas Search index: db.YOURCOLLECTION.createSearchIndex( "example-index", { mappings: { dynamic: true } } ) Then, if you want to run a query you can use the $search stage of an aggregation pipeline. You can learn more about managing Atlas Search indexes in our documentation . Get Started with Local Vector Search If you’re building an application with generative AI or semantic search and MongoDB Atlas, chances are you’ll be interested in our Atlas Vector Search offering. And now with the Atlas CLI, you can work with Vector Search in the cloud and your local environment. To get started with Vector Search locally you can use MongoDB Shell to create a Vector Search index. Notice that this is similar to the Atlas Search example above, except that in this case there is a vector embedding accounted for in search index creation. db.YOURCOLLECTION.createSearchIndex({ "mappings": { "dynamic": true, "fields": { "plot_embedding": { "type": "knnVector", "dimensions": 1536, "similarity": "euclidean" } } } } ) To learn more about running Vector Search queries visit our documentation . Additionally, if you're already familiar with handling your cloud Search indexes using the Atlas CLI, you'll appreciate a fresh set of interactive commands designed to help you efficiently manage Atlas Search and Vector Search indexes both locally and in the cloud: atlas deployments search indexes create From there you can move through an interactive flow that guides you through index creation. For detailed instructions visit our tutorial . Ready to Move to the Cloud? If you’re ready to create an Atlas database in the cloud, that is easy to do with the Atlas CLI. Simply use the following command: atlas deployments setup --type atlas From there, the setup wizard will guide you to: Register for an Atlas account or authenticate to an existing account Create a free MongoDB Atlas database Load sample data Add your IP address to the access list Create a database user and password Connect to the cluster using the MongoDB Shell ( mongosh ) so you can begin interacting with your data To learn more about the Atlas CLI, visit our documentation . And be sure to let us know what you think of the Atlas CLI in our user feedback portal . With the new local experience with the Atlas CLI, it’s easier than ever to work with your data on Atlas no matter your preferred development environment. Get started today with the Atlas CLI as the ultimate developer tool to manage MongoDB Atlas, including Atlas Search and Vector Search, throughout the entire software development lifecycle, from your local environment all the way to the cloud.
Introducing Atlas for the Edge
This post is also available in: Deutsch , Português . We are thrilled to introduce MongoDB Atlas for the Edge at MongoDB.local London. This new solution is designed to streamline the management of data generated across various sources at the edge, including devices, on-premises data centers, and the cloud. Edge computing, which brings data processing closer to end-users, offers significant advantages. At the same time, it often proves challenging due to complex networking, data volume management, and security concerns, which can deter many organizations. They are also costly to build, maintain, and scale. Some challenges organizations face include: Significant technical expertise to manage the complexity of networking and high volumes of distributed data required to deliver reliable applications that run anywhere Stitching together hardware and software solutions from multiple vendors, resulting in complex and fragile systems that are often built using legacy technology that is limited by one-way data movement and requires specialized skills to manage and operate Constant optimization of edge devices due to their constraints — like limited data storage and intermittent network access — which makes keeping operational data in sync between edge locations and the cloud difficult Security vulnerabilities and frequent firmware patches and updates to ensure data privacy and compliance MongoDB Atlas for the Edge simplifies all of these manual tasks. It allows MongoDB to run on diverse edge infrastructure, from self-managed, on-premises servers to cloud deployments offered by major cloud providers. Data seamlessly flows between and is kept synchronized across all sources, ensuring real-time data delivery with minimal latency. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. With MongoDB Atlas for the Edge, organizations can now use a single, unified interface to deliver a consistent and frictionless development experience from the edge to the cloud — and everything in between. Together, the capabilities included with MongoDB Atlas for the Edge allow organizations to significantly reduce the complexity of building edge applications and architectures: Run MongoDB on a variety of edge infrastructure for high reliability with ultra-low latency: With MongoDB Atlas for the Edge, organizations can run applications on MongoDB using a wide variety of infrastructure, including self-managed, on-premises servers, such as those in remote warehouses or hospitals, in addition to edge infrastructure managed by major cloud providers including AWS, Google Cloud, and Microsoft Azure. For example, data stored in MongoDB Enterprise Advanced on self-managed servers can be automatically synced with MongoDB Atlas Edge Server on AWS Local Zones and MongoDB Atlas in the cloud to deliver real-time application experiences to edge devices with high reliability and single-digit millisecond latency. MongoDB Atlas for the Edge allows organizations to deploy applications anywhere, even in remote, traditionally disconnected locations — and keep data synchronized between edge devices, edge infrastructure, and the cloud — to enable data-rich, fault-tolerant, real-time application experiences. Atlas Edge Server is now in private preview, learn more on our product page . Run applications in locations with intermittent network connectivity: With Atlas Edge Server and Atlas Device Sync , organizations can use a pre-built, local-first data synchronization layer for applications running on kiosks or on mobile and IoT devices to prevent data loss and improve offline application experiences. MongoDB Edge Servers can be deployed in remote locations to allow devices to sync directly with each other—without the need for connectivity to the cloud—using built-in network management capabilities. Once network connectivity is available, data is automatically synchronized between devices and the cloud to ensure applications are up to date for use cases like inventory and package tracking across supply chains, optimizing delivery routes in remote locations, and accessing electronic health records with intermittent network connectivity. Build and deploy AI-powered edge computing applications: Data is required for generative AI and machine learning technologies to function and Atlas for the Edge provides provides the data transport necessary to provide low-latency, intelligent functionality at the edge directly on devices—even when network connectivity is unavailable. For example, data stored on MongoDB Atlas can be enhanced with embeddings with Atlas Vector Search . These documents can be synchronized down to mobile or edge devices using Atlas Device Sync. The embeddings can then be used with platform specific libraries like CoreML to perform ML classification. Additionally in reverse, data is the oil for training AI models and edge computing developers spend a ton of time writing non-differentiated code to synchronize data to the cloud, particularly in poor connectivity locations. By gather data at the edge and then using Atlas Device Sync to synchronize the data to the cloud - the data can then be used to train models or use Atlas Vector Search to generate embeddings and relevance search. Store and process real-time and batch data from IoT devices to make it actionable: With MongoDB Atlas Stream Processing , organizations can ingest and process high-velocity, high-volume data from millions of IoT devices (e.g., equipment sensors, factory machinery, medical devices) in real-time streams or in batches when network connectivity is available. Data can then be easily aggregated, stored, and analyzed using MongoDB Time Series collections for use cases like predictive maintenance and anomaly detection with real-time reporting and alerting capabilities. MongoDB Atlas for the Edge provides all of the tools necessary to process and synchronize virtually any type of data across edge locations and the cloud to ensure consistency and availability. Easily secure edge applications for data privacy and compliance: MongoDB Atlas for the Edge helps organizations ensure their edge deployments are secure with built-in security capabilities. The Atlas Device SDK provides out-of-the-box data encryption at rest, on devices, and in transit over networks to ensure data is protected and secure. Additionally, Atlas Device Sync provides fine-grained role-based access, with built-in identity and access management (IAM) capabilities that can also be combined with third-party IAM services to easily integrate edge deployments with existing security and compliance solutions. Some of the leading organizations are leveraging Atlas for the Edge today. For example: Cathay Pacific , Hong Kong’s home airline providing passenger and cargo services to destinations around the world, understood the need for digital transformation in their critical pilot briefing process and in-flight operations. With MongoDB Atlas, they were the very first to digitize their flight operations process with an iPad app, Flight Folder, enabling one of the first zero paper flights in the world in September of 2019. MongoDB’s developer data platform met their requirements for this and many other projects, successfully improving costs, operational efficiency, and accuracy, while also reducing environmental impact. Read the case study to learn more. Cloneable provides low/no-code tools to enable instant deployment of AI applications to a spectrum of devices—mobile, IoT devices, robots, and beyond. “We collaborated with MongoDB because Atlas for the Edge provided capabilities that allowed us to move faster while providing enterprise-grade experiences,” said Tyler Collins, CTO at Cloneable. “For example, the local data persistence and built-in cloud synchronization provided by Atlas Device Sync enables real-time updates and high reliability, which is key for Cloneable clients bringing complex, deep tech capabilities to the edge. Machine learning models distributed down to devices can provide low-latency inference, computer vision, and augmented reality. Atlas Vector Search enables vector embeddings from images and data collected from various devices to allow for improved search and analyses. MongoDB supports our ability to streamline and simplify heavy data processes for the enterprise.” To learn more about the solution announced today, and find out how retailers and healthcare organizations are leveraging the solution, please visit the web page for Atlas for the Edge .
View and Analyze Your Monthly MongoDB Atlas Usage with Cost Explorer
In today's macroeconomic climate, knowing where your money's going is a big deal. From optimizing costs to boosting efficiency, understanding your software expenses can be a total game-changer for your business. That’s why we’re excited to announce the release of Cost Explorer in MongoDB Atlas. Cost Explorer is a new visual interface available in the Billing section of the Atlas UI that is meant to help you view and analyze your monthly MongoDB Atlas usage in one convenient location. How can Cost Explorer help you? Cost Explorer allows you to easily filter your Atlas usage data by what’s most important to you and your business, with filters to segment your view by organization (if you have cross-org billing enabled), projects, clusters, or services, within a time window of up to 18 months. With Cost Explorer, you can now quickly pinpoint trends or outliers in your month-over-month usage to identify opportunities to potentially improve or optimize your Atlas usage going forward. If you’re looking for additional customization beyond what is available in Cost Explorer, you can also create your own billing dashboards in Atlas Charts that are fully tailored to your needs. Cost Explorer is viewable for any Atlas user assigned the Organization Owner, Billing Admin, or Organization Billing Viewer roles. To learn more about Cost Explore and how to manage your Atlas billing, view our documentation on managing billing .