Product Updates

The most recent MongoDB product releases and updates

Workload Isolation for More Scalability and Availability: Search Nodes Now on Google Cloud

Today we’re excited to take the next step in bringing scalable, dedicated architecture to your search experiences with the introduction of Atlas Search Nodes, now in general availability for Google Cloud. This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Since our initial announcement of Search Nodes in June of 2023, we’ve been rapidly accelerating access to the most scalable dedicated architecture, starting with general availability on AWS and now expanding to general availability on Google Cloud. We'd like to give you a bit more context on what Search Nodes are and why they're important to any search experience running at scale. Search Nodes provide dedicated infrastructure for Atlas Search and Vector Search workloads to enable even greater control over search workloads. They also allow you to isolate and optimize compute resources to scale search and database needs independently, delivering better performance at scale and higher availability. One of the last things developers want to deal with when building and scaling apps is having to worry about infrastructure problems. Any downtime or poor user experiences can result in lost users or revenue, especially when it comes to your database and search experience. This is one of the reasons developers turn to MongoDB, given the ease of use of having one unified system for your database and search solution. With the introduction of Atlas Search Nodes, we’ve taken the next step in providing our builders with ultimate control, giving them the ability to remain flexible by scaling search workloads without the need to over-provision the database. By isolating your search and database workloads while at the same time automatically keeping your search cluster data synchronized with operational data, Atlas Search and Atlas Vector Search eliminate the need to run a separate ETL tool, which takes time and effort to set up and is yet another fail point for your scaling app. This provides superior performance and higher availability while reducing architectural complexity and wasted engineering time recovering from sync failures. In fact, we’ve seen a 40% to 60% decrease in query time for many complex queries, while eliminating the chances of any resource contention or downtime. With just a quick button click, Search Nodes on Google Cloud offer our existing Atlas Search and Vector Search users the following benefits: Higher availability Increased scalability Workload isolation Better performance at scale Improved query performance We offer both compute-heavy search-specific nodes for relevance-based text search, as well as a memory-optimized option that is optimal for semantic and retrieval augmented generation (RAG) production use cases with Atlas Vector Search. This makes resource contention or availability issues a thing of the past. Search Nodes are easy to opt into and set up — to start, jump on into the MongoDB UI and follow the steps do the following: 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 Google Cloud for “Multi-cloud, multi-region & workload isolation” to enable Toggle the radio button for “Search Nodes for workload isolation” to enable. 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. Jump straight into our docs to learn more! MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More

March 28, 2024

Introducing Semantic Caching and a Dedicated MongoDB LangChain Package for Gen AI Apps

We are in an unprecedented time in history where developers can build transformative AI applications quickly, without being AI experts themselves. This ability is enabling new classes of applications that can better serve customers with conversational AI for assistance and automation, advanced reasoning and analysis using AI-powered retrieval, and recommendation systems. Behind this revolution are large language models (LLMs) that can be prompted to solve for a wide range of use cases. However, LLMs have various limitations, like knowledge cutoff and a tendency to hallucinate. To overcome these limitations, they must be integrated with proprietary enterprise data sources to build reliable, relevant, and high-quality generative AI applications. That’s where MongoDB plays a critical role in the modern generative AI stack. Developers use MongoDB Atlas Vector Search as a vital part of the generative AI technique known as retrieval-augmented generation (RAG). RAG is the process of feeding LLMs the supplementary data necessary to ground their responses, ensuring they're dependable and precise. LangChain has been a critical part of this journey since the public launch of Atlas Vector Search, enabling developers to build better retriever systems powered by vector search and store conversation history in the operational database. Today, we are excited to announce support for two enhancements: Semantic cache powered by Atlas vector search, which improves the performance of your apps A dedicated LangChain-MongoDB package for Python and JS/TS developers, enabling them to build advanced applications even more efficiently The MongoDB Atlas integration with LangChain can now power all the database requirements for building modern generative AI applications: vector search, semantic caching (currently only available in Python), and conversation history. Earlier, we announced the launch of MongoDB LangChain Templates , which enable the developers to quickly deploy RAG applications, and provided a reference implementation of a basic RAG template using MongoDB Atlas Vector Search and OpenAI and a more advanced Parent-document Retrieval RAG template using MongoDB Atlas Vector Search. We are excited about our partnership with LangChain and will continue innovating. Improve LLM application performance with semantic cache Semantic cache improves the performance of LLM applications by caching responses based on the semantic meaning or context within the queries themselves. This is different from a traditional cache that works based on exact keyword matching. In the era of LLM the value of semantic cache is increasing tremendously, enabling sophisticated user experiences that closely mimic human interactions. For example, if two different users enter two different prompts, “give me suggestions for a comedy movie” and “recommend a comedy movie”, the semantic cache can understand that the intent behind the queries are same and return a similar response, even though different keywords are used, whereas a traditional cache will fail. Figure 1: Semantic cache using MongoDB Atlas Vector Search Check out this video walkthrough for the semantic cache: Accelerate development with a dedicated package With a dedicated LangChain-MongoDB package, MongoDB is even more deeply integrated with LangChain. The Python and Javascript packages contain the following LangChain Integrations: MongoDBAtlasVectorSearch ( Vector stores ) and MongoDBChatMessageHistory ( Chat Messages Memory ). In addition, the Python package includes the MongoDBAtlasSemanticCache ( LLM Caching ). The new package langchain-mongodb contains all the MongoDB-specific implementations and needs to be installed separately from langchain, which includes all the core abstractions. Earlier, everything was in the same package, making it challenging to correctly version and communicate what version should be used and whether any breaking changes were made. Find out more about the langchain-mongodb package: Python: Source code , LangChain docs , MongoDB docs Javascript: Source code , LangChain.js docs , MongoDB docs Get started today Check out this accompanying tutorial and notebook on building advanced RAG with MongoDB and LangChain, which contains a walkthrough and use cases for using semantic cache, vector search, and chat message history. Check out the “ PDFtoChat ” app to see langchain-mongodb JS in action. It allows you to have a conversation with your proprietary PDFs using AI and is built with MongoDB Atlas, LangChain.js, and TogetherAI. It’s an end-to-end SaaS-in-a-box app and includes user authentication, saving PDFs, and saving chats per PDF. Read the excellent overview of semantic caching using LangChain and MongoDB.

March 20, 2024

Announcing Search Index Management in MongoDB Compass

You can now create and manage Atlas Search and Atlas Vector Search indexes on the interface many of you know and love: MongoDB Compass . Seamlessly build full-text and semantic search applications on top of your Atlas database, delivering swift and relevant results for a range of use cases including e-commerce sites, customer support chatbots, recommendation systems, and more. Gone are the days of juggling multiple tools to bring your search queries to fruition. And, with a variety of templates to choose from, Compass simplifies learning search index syntax so you can focus on what’s most important to you: building exceptional end-user experiences on top of your search queries. Try it out To get started, connect to an Atlas cluster from Compass. If you don’t have one, sign up . From there, simply navigate to Compass’ Indexes tab and select Create Search Index . It’s easy to build your first search index using one of our templates. Select either Search or Vector Search, and use the appropriate template. In this example, we’re going to create a Vector Search index. Once you're satisfied with your index definition, click Aggregate to start testing out your pipeline in Compass. Compass’ new search index experience leads you to results in just three guided steps, all without leaving the comfort of Compass. To learn more about search indexing in Compass, visit our documentation . If you have feedback about Compass’ search index experience, let us know on our feedback forum . Happy indexing!

March 18, 2024

Atlas Data Federation and Online Archive Can Now Be Deployed in Azure

This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Exciting developments are on the horizon for users of Microsoft Azure, marking a significant leap in data management capabilities. First off, Atlas Data Federation is now Generally Available on Azure. This means you can now deploy it directly within Azure and even query data from Microsoft Azure Blob Storage. And that's not all. We've also launched the General Availability of Atlas Online Archive on Azure. These advancements usher in a new era of efficient archiving solutions for Azure-based data solutions. Both updates are big steps forward in making data management on Azure more powerful and flexible. Let's dive into what this means for you! Azure support in Atlas Data Federation (General Availability) With Atlas Data Federation, users can seamlessly query, transform, and create views across multiple Atlas databases and cloud object storage solutions, such as Amazon S3 and now Microsoft Azure Blob Storage. This feature, previously exclusive to AWS, is a game-changer, allowing direct deployment within Azure and the ability to tap into Microsoft Azure Blob Storage for data insights. Figure 1: Tap into Azure Blob Storage easily from the Atlas UI Key features of Atlas Data Federation Cloud flexibility: Choose between AWS and Azure for hosting federated database instances. Diverse data sources: Incorporate MongoDB Atlas clusters or Azure storage solutions (Azure Blob Storage and Azure Data Lake Storage Gen2) as data sources for comprehensive queries, including cross-region. Advanced aggregation: Comprehensive aggregation capabilities with operators inclusive of $match, $lookup, $queryHistory, $merge, $out, etc. Direct $out support for Azure Blob Storage and Azure Data Lake Storage Gen2. Atlas SQL queries on Azure: Execute SQL queries on Azure, integrating MongoDB data for a unified analysis experience. Atlas Data Federation simplifies accessing and analyzing complex data sets by combining data across multiple sources into a single, federated view, providing valuable insights for more informed business decisions. Explore Atlas Data Federation on Azure Today . Azure support in Atlas Online Archive (General Availability) Atlas Online Archive's expansion to Azure ensures that data tiering is not only efficient but also integrated, keeping archival data within the Azure ecosystem. This integration addresses the previous limitation of defaulting to AWS for storage, even for Azure-hosted clusters. Figure 2: Seamlessly select Azure when choosing a region Key features of Atlas Online Archive Provider choice: Opt for AWS or Azure to align with your cloud strategy. Automatic archiving: Set rules to move older data to cost-effective cloud storage automatically, eliminating manual offloading. Unified querying endpoint: Access all data through a single endpoint, ensuring quick insights without compromising data availability. Integrated MongoDB Atlas UI management: Manage your data tiering and archiving within the familiar Atlas interface, streamlining operations and maintenance. Seamlessly manage your MongoDB Atlas data tiering at scale with Atlas Online Archive. Atlas Online Archive empowers you to manage your data lifecycle efficiently, balancing cost and accessibility with ease. Finally, here are a few points to consider: Any newly created archive on an Azure cluster on or after 02/28 will default to Azure regions. Note that storage regions for Online Archive will default to Azure clusters only if there are no pre-existing AWS archives on that specific Azure cluster If there are any pre-existing AWS Online Archives on Azure clusters, then all newly created archives on that specific cluster will remain on AWS. Cloud providers or storage regions cannot be edited or modified once configured Embrace the Full Potential of Atlas Online Archive on Azure Today . We're thrilled to support your data management journey, offering enhanced control and flexibility over your data through these new Azure capabilities. As MongoDB Atlas continues to expand as a multi-cloud solution, we're here to ensure your data strategy is as dynamic and versatile as your business needs. For guidance on getting started, check out our documentation on Atlas Data Federation or Atlas Online Archive . Thank you for trusting MongoDB Atlas as your Developer Data Platform. Welcome to the future of multi-cloud data management!

February 29, 2024

Announcing the GA of the Atlas Device SDK for C++

This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . MongoDB's developer data platform was designed to offer unparalleled flexibility and scalability for developers. By streamlining the integration of complex data structures and real-time analytics, and accelerating the development and deployment of mission-critical applications, its adoption has added significant value to businesses across industries. Today we continue our mission to provide the best experience for developers and are excited to announce the general availability (GA) of the Atlas Device SDK for C++ . The updates in this release come after numerous iterations that were guided by feedback from our preview users and target performance and portability. The Atlas Device SDK for C++ enables developers to effortlessly store data on devices for offline access while seamlessly synchronizing data to and from the MongoDB Atlas cloud within their C++ applications. It serves as a user-friendly alternative to SQLite, offering simplicity due to its object-oriented database nature, removing the necessity for a separate mapping layer or ORM. Aligned with MongoDB's developer data platform mission of streamlining the development process - the C++ SDK incorporates networking retry logic and advanced conflict merging functionality, eliminating the traditional need for writing and maintaining extensive and complex synchronization code. Why choose the Atlas Device SDK for C++? The Atlas Device SDK for C++ is particularly well-suited for applications in embedded devices, IoT, and cross-platform scenarios. It serves as a comprehensive object-oriented persistence layer for edge, mobile, and embedded devices, offering built-in support for synchronization with the MongoDB Atlas as a cloud backend. In the evolving landscape of connected and smart devices, the demand for more data, including historical data for automated decision-making, highlights the importance of efficient persistence layers and real-time cloud-syncing technologies which are robust towards changing network connections and outages. The database included in the Atlas Device SDK for C++, comes with over a decade of history, and is a mature, feature-rich, and enterprise-ready technology, integrated into tens of thousands of applications on Google Play and the Apple App Store with billions of downloads. Its lightweight design is optimized for resource-constrained environments. It considers factors like compute, memory, bandwidth, and battery usage in its design. Embedding the SDK directly into application code eliminates the need for additional deployment tasks and simplifies the development process. The fully object-oriented nature of the SDK guides the data modeling, providing a straightforward and idiomatic approach. This stands in contrast to alternative technologies like SQLite database, which require an object-relational mapping library, adding complexity and making future development, maintenance, and debugging more challenging. Furthermore, the SDK’s underlying data store enables seamless integration with reactive UI layers across various environments. In the Atlas Device SDK for C++ we give examples of how to integrate with the Qt framework , but other UI layers can also be added. Improvements in the GA release The new API was developed based on performance measurements with a coordinated focus and effort to improve the read/write operations of the data layer. There has been great interest from major automotive and manufacturing OEMs and this feedback has been invaluable in guiding our final API. Some of the changes added to the Atlas Device SDK for C++ include: Aligning our APIs with other Atlas Device SDKs, e.g. improved control of the database state with monitoring and manual compaction HTTP tunneling Better control for the Atlas Device Sync sessions Windows support Compatibility with OpenWRT among other Linux distributions by supporting musl Android Automotive support with Blueprint/Soong build files What's next Looking ahead we are working towards geospatial support as well as the ability to build with a variety of package managers such as vcpkg and Conan. We welcome and value all feedback - if you have any comments or suggestions, please share them via our GitHub project . Ready to get started? Install the Atlas Device SDK for C++ — start your journey with our docs or jump right into example projects with source code . Then, register for Atlas to connect to Atlas Device Sync, a fully managed mobile backend as a service. Leverage out-of-the-box infrastructure, data synchronization capabilities, network handling, and much more to quickly launch enterprise-grade mobile apps. Finally, let us know what you think, and get involved in our forums . See you there!

February 22, 2024

Enhanced Atlas Functionality: Introducing Resource Tagging for Projects

We are thrilled to announce that Atlas has now extended its tagging functionality to include projects in addition to deployments . This enhancement enables users to apply resource tags to projects, further enriching the way you can associate metadata with your cloud resources. With this new capability, categorizing, organizing, and tracking your projects within Atlas becomes more intuitive and effective, offering a streamlined approach to managing your resources. Enhancing project management with resource tagging Incorporating resource tagging into projects significantly enhances visibility and streamlines project management. By applying tags, teams can categorize resources, making it easier to understand the purpose or specific metadata associated with a project. This practice is especially beneficial in large-scale projects, where organizing resources systematically can vastly improve productivity. Tags serve as versatile markers, representing various attributes of a project such as environment, criticality, cost center, or application, thereby simplifying project organization. Furthermore, tags lay the groundwork for supporting automation and policy enforcement within organizations. By utilizing tags, tasks related to access controls, compliance, and other policies can be automated, enhancing operational efficiency. Auditing processes also benefit from tagging, facilitating tracking, and ensuring resources meet specific business requirements. In environments where teamwork is essential, adding tags to projects aids in streamlined collaboration. Tags allow team members to quickly grasp the purpose or function of different resources, surfacing critical information about the project that can help reduce miscommunication and conflicts. Overall, adopting resource tagging in cloud resource management unlocks significant improvements in performance and efficiency, making it an invaluable tool for modern organizational needs. How to add tags to projects You can view and manage tagging on projects in multiple areas: Atlas UI: When creating a new project , on the Organization Project List, or within Project Settings. Admin API: Various operations on projects were enhanced to allow you to view, create, and manage tags applied to projects, such as CreateOneProject and ReturnAllProjects . Atlas CLI: various commands on projects were enhanced to all you to view, create, and manage tags applied to projects. Resource tagging best practices We recognize that the complexity of tagging use cases varies, tailored to an organization's unique structure and specific business requirements. With this in mind, we’ve designed resource tagging in Atlas to support a variety of use cases. We suggest defining tags that should be applied across all projects to get started. This will ensure your tagging approach is reliable and consistent across all resources. If you have multiple deployments within a project, apply more granular metadata on each deployment. In the simplified example below, an organization has three projects containing one or more deployments. Each project contains a deployment for each development environment. We’ve added common tags to the projects and more granular tags to identify the environment at the deployment level. Given the uniqueness of each organization, we've designed a flexible system with simplicity at its heart, using key-value pairs. If you have a flatter organization structure in Atlas (e.g. with one deployment per project), consider adding all tags at the level that makes the most sense for your organization. This may vary depending on how you manage your deployments, existing tag workflows, or where you desire to view tags in the Atlas UI. Finally, here are a few points to consider when tagging: Do not include any sensitive information such as Personally Identifiable Information (PII) or Protected Health Information (PHI) in your resource tag keys or values. Use a standard naming convention for all tags, including spelling, case, and punctuation. Define and communicate a strategy for enforcing mandatory tags. We recommend starting by identifying the environment and the application, service, or workload. Use namespaces or prefixes to easily identify tags owned by different business units. Use programmatic tools like Terraform or the Admin API to manage the database of your tags. In summary The introduction of resource tagging for projects marks an improvement in how users can intuitively categorize, organize, and track projects within Atlas, streamlining cloud resource management. We're eager to hear your thoughts and ideas on further applications of resource tagging in Atlas. Please share your feedback and suggestions at , as your input is invaluable in shaping the future of our platform.

February 15, 2024

Atlas Stream Processing is Now in Public Preview

This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국인 , 简体中文 . Today, we’re excited to announce that Atlas Stream Processing is now in public preview. Any developer on Atlas interested in giving it a try has full access. Learn more in our docs or get started today. Listen to the MongoDB Podcast to learn about the Atlas Stream Processing public preview from Head of Streaming Products, Kenny Gorman. Developers love the flexibility and ease of use of the document model, alongside the Query API, which allows them to work with data as code in MongoDB Atlas. With Atlas Stream Processing, we are bringing these same foundational principles to stream processing. A report covering the topic published by S&P Global Market Intelligence 451 Research had this to say, “A unified approach to leveraging data for application development — the direction of travel for MongoDB — is particularly valuable in the context of stream processing where operational and development complexity has proven a significant barrier to adoption." First announced at .local NYC 2023, Atlas Stream Processing is redefining the experience of aggregating and enriching streams of high velocity, rapidly changing event data, and unifying how to work with data in motion and at rest. How are developers using the product so far? And what have we learned? During the private preview, we saw thousands of development teams request access and we have gathered useful feedback from hundreds of engaged teams. One of those engaged teams is marketing technology leader, Acoustic : "At Acoustic, our key focus is to empower brands with behavioral insights that enable them to create engaging, personalized customer experiences. To do so, our Acoustic Connect platform must be able to efficiently process and manage millions of marketing, behavioral, and customer signals as they occur. With Atlas Stream Processing, our engineers can leverage the skills they already have from working with data in Atlas to process new data continuously, ensuring our customers have access to real-time customer insights." John Riewerts, EVP, Engineering at Acoustic Other interesting use cases include: A leading global airline using complex aggregations to rapidly process maintenance and operations data, ensuring on-time flights for their thousands of daily customers, A large manufacturer of energy equipment using Atlas Stream Processing to enable continuous monitoring of high volume pump data to avoid outages and optimize their yields, and An innovative enterprise SaaS provider leveraging the rich processing capabilities in Atlas Stream Processing to deliver timely and contextual in-product alerts to drive improved product engagement. These are just a few of the many use case examples that we’re seeing across industries. Beyond the use cases we’ve already seen, developers are giving us tons of insight into what they’d like to see us add to in the future. In addition to enabling continuous processing of data in Atlas databases through change streams, it’s exciting to see developers using Atlas Stream Processing with their Kafka data hosted by valued partners like Confluent , Amazon MSK , Azure Event Hubs , and Redpanda . Our aim with developer data platform capabilities in Atlas has always been to make for a better experience across the key technologies relied on by developers. What’s new in the public preview? That brings us to what’s new. As we scale to more teams, we’re expanding functionality to include the most requested feedback gathered in our private preview. From the many pieces of feedback received, three common themes emerged: Refining the developer experience Expanding advanced features and functionality Improving operations and security Refining the developer experience In private preview, we established the core of the developer experience that is essential to making Atlas Stream Processing a natural solution for development teams. And in public preview, we’re doubling down on this by making two additional enhancements: VS Code integration The MongoDB VS Code plugin has added support for connecting to Stream Processing instances. For developers already leveraging the plugin, teams can create and manage processors in a familiar development environment. This means less time switching between tools and more time building your applications! Improved dead letter queue (DLQ) capabilities DLQ support is a key element for powerful stream processing and in public preview, we’re expanding DLQ capabilities. DLQ messages will now display themselves when executing pipelines with sp.process() and when running .sample() on running processors, allowing for a more streamlined development experience that does not require setting up a target collection to act as a DLQ. Expanding advanced features and functionality Atlas Stream Processing already supported many of the key aggregation operators developers are familiar with in the Query API used with data at rest. We've now added powerful windowing capabilities and the ability to easily merge and emit data to an Atlas database or to a Kafka topic. Public preview will add even more functionality demanded by the most advanced teams relying on stream processing to deliver customer experiences: $lookup Developers can now enrich documents being processed in a stream processor with data from remote Atlas clusters, performing joins against fields from the document and the target collection. Change streams pre- and post-imaging Many developers are using Atlas Stream Processing to continuously process data in Atlas databases as a source through change streams. We have enhanced the change stream $source in public preview with support for pre- and post-images . This enables common use cases where developers need to calculate deltas between fields in documents as well as use cases requiring access to the full contents of a deleted document. Conditional routing with dynamic expressions in merge and emit stages Conditional routing lets developers use the value of fields in documents being processed in Atlas Stream Processing to dynamically send specific messages to different Atlas collections or Kafka topics. The $merge and $emit stages also now support the use of dynamic expressions. This makes it possible to use the Query API for use cases requiring the ability to fork messages to different collections or topics as needed. Idle stream timeouts Streams without advancing watermarks due to a lack of inbound data can now be configured to close after a period of time emitting the results of the windows. This can be critical for streaming sources that have inconsistent flows of data. Improving operations and security Finally, we have invested heavily over the past few months in improving other operational and security aspects of Atlas Stream Processing. A few of the highlights include: Checkpointing Atlas Stream Processing now performs checkpoints for saving a state while processing. Stream processors are continuously running processes, so whether due to a data issue or infrastructure failure, they require an intelligent recovery mechanism. Checkpoints make it easy to resume your stream processors from wherever data stopped being collected and processed. Terraform provider support Support for the creation of connections and stream processing instances (SPIs) is now available with Terraform. This allows for infrastructure to be authored as code for repeatable deployments. Security roles Atlas Stream Processing has added a project-level role, giving users just enough permission to perform their stream processing tasks. Stream processors can run under the context of a specific role, supporting a least privilege configuration. Auditing Atlas Stream Processing can now audit authentication attempts and actions within your Stream Processing Instance giving you insight into security-related events. Kafka consumer group support Stream processors in now use Kafka consumer groups for offset tracking. This allows users to easily change the position of the processor in the stream for operations and easily monitor for potential processor lag. A final note on what’s new is that in public preview, we will begin charging for Atlas Stream Processing, using preview pricing (subject to change). You can learn more about pricing in our documentation . Build your first stream processor today Public preview is a huge step forward for us as we expand the developer data platform and enable more teams with a stream processing solution that simplifies the operational complexity of building reactive, responsive, event-driven applications, while also offering an improved developer experience. We can’t wait to see what you build! Login today or get started with the tutorial , view our resources , or follow the Learning Byte on MongoDB University.

February 13, 2024

MongoDB Enterprise Advanced in Google Distributed Cloud Hosted

Today, we’re excited to strengthen our "run anywhere" approach and deepen our relationship with Google Cloud by announcing that MongoDB Enterprise Advanced is now available for use within Google Distributed Cloud Hosted (GDC Hosted). "Google Cloud is happy to welcome MongoDB as a preferred partner for our Google Distributed Cloud Hosted product," said Rohan Grover, Director of Product for GDC Hosted. "MongoDB's powerful document database aligns with our data analytics focus, empowering our shared customers to unlock the full potential of their sensitive data in an air-gapped private cloud." GDC Hosted is Google Cloud’s air-gapped private cloud that does not require connectivity to Google Cloud or the public internet to manage the infrastructure, services, APIs, or tooling. GDC Hosted enables public sector organizations and regulated enterprises to address strict data residency and security requirements, while continuing to deliver innovation to their users. MongoDB Enterprise Advanced combines the power of MongoDB —– the leading NoSQL, document-oriented database that supports a variety of data structures — with an industry-leading offering catering to customers with the most advanced security and data sovereignty needs. As a flexible and scalable solution, MongoDB allows diverse datasets to be stored in a schemaless format, ensuring easy data manipulation and real-time analytics. Together, GDC Hosted and MongoDB Enterprise Advanced offer a solution that enables users to scale their operations while adhering to the strictest data governance and security standards. The bridge between GDC Hosted and MongoDB Enterprise Advanced is Kubernetes : GDC Hosted is built on Kubernetes allowing teams to self-manage MongoDB through the use of the MongoDB Enterprise Kubernetes Operator. The MongoDB Enterprise Kubernetes Operator is the only officially supported way to run Enterprise Advanced deployments of MongoDB in Kubernetes. To enable customers to manage deployments within their environment of choice (GDC Hosted in this case), the operator works in conjunction with the MongoDB self-hosted Ops Manager, which the operator can also install and manage in Kubernetes. This gives customers the ability to deploy, monitor, back up, and scale MongoDB. The Enterprise Operator drastically simplifies both the setup and day-two operations like upgrades, making it possible to run MongoDB in Kubernetes with far less Kubernetes expertise. Creation and configuration of database deployments can be managed via a Git repo, saving developers from needing the permissions or knowledge needed to work directly with Kubernetes. By leveraging the Enterprise Kubernetes Operator, users can manage their MongoDB deployments with even greater power and scale, and maximize their investment in both MongoDB and Google Cloud. GDC Hosted is built to meet high regulatory, durability, and availability requirements, which aligns with MongoDB Enterprise Advanced’s commitment to giving users the tools and support they need to have complete control over the management and security of their self-managed MongoDB environments. While MongoDB Atlas is the best way to run MongoDB on Google Cloud, MongoDB Enterprise Advanced in GDC Hosted is the best option for teams that need absolute self-managed control over data governance and compliance, while still allowing for scalability. Once you have GDC Hosted up and running, you can get started with MongoDB Enterprise Advanced through the MongoDB Enterprise Advanced listing in the GDC Hosted Marketplace. Alternatively, teams can access MongoDB Enterprise Advanced through the Google Cloud Platforms Marketplace. MongoDB customers who want to get started using Enterprise Advanced in their GDC Hosted environments will need to sign up for a MongoDB Enterprise Advanced license through MongoDB first. For more information, reach out to . To learn more about the Enterprise Kubernetes Operator, visit our documentation . To learn more about Enterprise Advanced, visit our product page or download the latest version .

February 6, 2024

Introducing Auto-Index Creation for Atlas Serverless Instances

Atlas serverless instances now offer auto-index creation, a new capability that automatically generates indexes to help optimize performance and reduce the cost of your queries. Auto-index creation is now available in public preview and enabled by default for all serverless instance deployments - allowing developers to worry less about needing to manually optimize their serverless database. Simplify development with Atlas serverless instances Developers love serverless technology primarily because of its unparalleled ease of use. By abstracting away infrastructure management, serverless allows developers to focus on what they do best: writing code and building amazing applications. It’s expected that any great serverless offering just works out of the box, without a large learning curve or emphasis on implementation and setup. Atlas serverless instances, first announced as generally available in June 2022, deliver on this promise by allowing you to deploy a database that seamlessly scales with demand in seconds with minimal configuration and a consumption-based pricing model that only charges for what you use. The addition of auto-index creation now further reduces management overhead by automating index creation for common queries to ensure fast response time. How auto-indexing works Indexes are special data structures that store a small portion of the collection's data set in an easy-to-traverse form. Without indexes, MongoDB must perform a collection scan—i.e., scan every document in a collection—to select those documents that match the query statement. By adding an index to appropriate queries, you can dramatically reduce the number of documents the query engine must inspect in order to return a result and in turn benefit from improved query performance and a reduction in the read operations you are charged for. With auto-index creation enabled, Atlas will analyze your recent query workload and automatically create high-impact indexes based on index suggestions in the Performance Advisor . This helps promote good index hygiene for your data by creating high-impact indexes without requiring you to regularly check for suggestions or create indexes manually. You can view newly created indexes in the Atlas UI in the Collections tab of the Data Explorer. You can also continue to manually add additional indexes in the Collections tab or via the Performance Advisor at any time. To learn more about auto-index creation, visit our documentation . Create a serverless instance in Atlas today.

December 18, 2023

Vector Search and Dedicated Search Nodes: Now in General Availability

This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 繁體中文 . 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 . Check out our AI resource page to learn more about building AI-powered apps with MongoDB. 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.

December 4, 2023

MongoDB Atlas AWS CloudFormation and CDK Integration Expansion

At MongoDB, we meet our developers where they’re at and offer multiple ways to get started and work with MongoDB Atlas . Since our GA launch of the MongoDB Atlas integration with the AWS CloudFormation Registry at the start of this year, users have had the freedom to manage their MongoDB Atlas resources using familiar YAML or JSON CloudFormation Templates. This provided developers and DevOps teams the core Infrastructure as Code (IaC) benefits: enhanced automation, version control, infrastructure consistency, and improved compliance. In addition to these updates, we went further and announced support for CDK at MongoDB.Local NYC in June 2023, which allowed development teams to leverage MongoDB Atlas resources natively in the language of their choice: JavaScript, TypeScript, Python, Java, Go, and C#. Today, just ahead of AWS re:Invent , we are excited to announce several key improvements and expansions to our AWS CloudFormation and CDK integrations that we hope will continue to make developers' lives even easier. New MongoDB Atlas resources on the AWS CloudFormation Registry Nine new MongoDB Atlas Resources have been published including Federated Database Instance , Serverless Private Endpoint , Programmatic API Keys Management , MongoDB Atlas Gov Support , and MongoDB Atlas Organization Management . This brings the total MongoDB Atlas Resources count on CloudFormation Registry to 42 and allows developers to do more with MongoDB Atlas and AWS CloudFormation. AWS region expansion Are you a developer based in or have your end customers in Hyderabad India , Melbourne Australia , Spain , Switzerland , or the UAE ? The good news, we have published all 42 Atlas Resources in each of these new AWS regions as well. Benefits include reduced latency and improved compliance with data sovereignty regulations. This brings the total MongoDB Atlas availability from 22 to 27 AWS regions on the AWS CloudFormation and CDK. New CDK level 3 resources The CDK provides different levels of abstraction for defining cloud resources: L1 constructs, which are direct mappings to AWS CloudFormation resources, and higher-level constructs like L2 and L3, which can provide high levels of abstraction. L3 constructs, also known as "Design Patterns" or "High-Level Constructs," combine multiple resources together in commonly used architectures with intelligent defaults, saving developers from manually having to glue L1 and L2 constructs together each time. Hence, we are happy to announce several new AWS CDK L3 resources including support for MongoDB Atlas Serverless . Migration to the Atlas Go SDK Lastly, we are delighted to have migrated our AWS CloudFormation resources to the new Atlas Go SDK . This is the middle layer that translates AWS CloudFormation calls to the Atlas Admin API (which is ultimately responsible for provisioning your MongoDB Atlas infrastructure). This migration goes a long way in accelerating our internal development velocity and enabling us to publish more MongoDB Atlas Resources on AWS CloudFormation soon after they go GA. Learn more about the key benefits of the Atlas Go SDK . Start building today These MongoDB Atlas integrations with AWS CloudFormation are free and open-source, licensed under the Apache License 2.0 . Users only pay for underlying MongoDB Atlas and AWS resources created and can get started building with the Atlas always-free tier ( M0 clusters ). Getting started today is faster than ever with MongoDB Atlas and AWS CloudFormation. We can’t wait to see what you will build next. Learn more on our MongoDB Atlas and AWS CloudFormation page.

November 27, 2023

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 .

November 15, 2023

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