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 feedback.mongodb.com , as your input is invaluable in shaping the future of our platform.
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.
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 firstname.lastname@example.org . 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 .
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.
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.
MongoDB Atlas AWS CloudFormation and CDK Integration Expansion
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
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.