Product Updates
The most recent MongoDB product releases and updates
Top 4 Reasons to Use MongoDB 8.0
October 2, 2024
Updates
Introducing: Multi-Kubernetes Cluster Deployment Support
Resilience and scalability are critical for today's production applications. MongoDB and Kubernetes are both well known for their ability to support those needs to the highest level. To better enable developers using MongoDB and Kubernetes, we’ve introduced a series of updates and capabilities that makes it easier to manage MongoDB across multiple Kubernetes clusters. In addition to the previously released support for running MongoDB replica sets and Ops Manager across multiple Kubernetes clusters, we're excited to announce the public preview release of support for Sharded Clusters spanning multiple Kubernetes clusters (GA to follow in November 2024). Support for deployment across multiple Kubernetes clusters is facilitated through the Enterprise Kubernetes Operator. As a recap for anyone unaware, the Enterprise Operator automates the deployment, scaling, and management of MongoDB clusters in Kubernetes. It simplifies database operations by handling tasks such as backups, upgrades, and failover, ensuring consistent performance and reliability in the Kubernetes environment. Multi-Kubernetes cluster deployment support enhances availability, resilience, and scalability for critical MongoDB workloads, empowering developers to efficiently manage these workloads within Kubernetes. This approach unlocks the highest level of availability and resilience by allowing shards to be located closer to users and applications, increasing geographical flexibility and reducing latency for globally distributed applications. Deploying replica sets across multiple Kubernetes clusters MongoDB replica sets are engineered to ensure high availability, data redundancy, and automated failover in database deployments. A replica set consists of multiple MongoDB instances—one primary and several secondary nodes—all maintaining the same dataset. The primary node handles all write operations, while the secondary nodes replicate the data and are available to take over as primary if the original primary node fails. This architecture is critical for maintaining continuous data availability, especially in production environments where downtime can be costly. Support for deploying MongoDB replica sets across multiple Kubernetes clusters helps ensure this level of availability for MongoDB-based applications running in Kubernetes. Deploying MongoDB replica sets across multiple Kubernetes clusters enables you to distribute your data, not only across nodes in the Kubernetes cluster, but across different clusters and geographic locations, ensuring that the rest of your deployments remain operational (even if one or more Kubernetes clusters or locations fail) and facilitating faster disaster recovery. To learn more about how to deploy replica sets across multiple Kubernetes clusters using the Enterprise Kubernetes Operator, visit our documentation . Sharding MongoDB across multiple Kubernetes clusters While replica sets duplicate data for resilience (and higher read rates), MongoDB sharded clusters divide the data up between shards, each of which is effectively a replica set, providing resilience for each portion of the data. This helps your database handle large datasets and high-throughput operations since each shard has a primary member handling write operations to that portion of the data; this allows MongoDB to scale up the write throughput horizontally, rather than requiring vertical scaling of every member of a replica set. In a Kubernetes environment, these shards can now be deployed across multiple Kubernetes clusters, giving higher resilience in the event of a loss of a Kubernetes cluster or an entire geographic location. This also offers the ability to locate shards in the same region as the applications or users accessing that portion of the data, reducing latency and improving user experience. Sharding is particularly useful for applications with large datasets and those requiring high availability and resilience as they grow. Support for sharding MongoDB across multiple Kubernetes clusters is currently in public preview and will be generally available in November. Deploying Ops Manager across multiple Kubernetes clusters Ops Manager is the self-hosted management platform that supports automation, monitoring, and backup of MongoDB on your own infrastructure. Ops Manager's most critical function is backup, and deploying it across multiple Kubernetes clusters greatly improves resilience and disaster recovery for your MongoDB deployments in Kubernetes. With Ops Manager distributed across several Kubernetes clusters, you can ensure that backups of deployments remain robust and available, even if one Kubernetes cluster or site fails. Furthermore, it allows Ops Manager to efficiently manage and monitor MongoDB deployments that are themselves distributed across multiple clusters, improving resilience and simplifying scaling and disaster recovery. To learn more about how to deploy Ops Manager across multiple Kubernetes clusters using the Enterprise Kubernetes Operator, visit our documentation . To leverage multi-Kubernetes-cluster support, you can get started with the Enterprise Kubernetes Operator .
Introducing Dark Mode for MongoDB Documentation
We’re excited to announce a highly requested feature: Dark mode is now available for MongoDB Documentation ! Every day, developers from all backgrounds—beginners to experts—turn to the MongoDB Documentation. It’s packed with comprehensive resources that help you build modern applications using MongoDB and the Atlas developer data platform. With detailed information and step-by-step guides, it’s an invaluable tool for improving your skills and making your development work smoother. From troubleshooting tricky queries to exploring new features, MongoDB Documentation is there to support your projects and help you succeed. With dark mode, you can now switch to a darker interface that’s easier on the eyes. Whether you’re working late or prefer a subdued color palette, dark mode enhances your MongoDB Documentation experience. How to enable dark mode Enabling dark mode is simple. Just click on the sun and moon icon at the top right of the page to switch between dark mode, light mode, and system settings. It will initially default to your system settings. This is a personal setting and won't affect other users within the project or organization. We’ve designed dark mode to provide the same user-friendly experience you’re used to and stay consistent across different tools in the developer workflow, including MongoDB Atlas, which is also available in dark mode . We're all about making your reading experience top-notch! Dark mode is here because you asked for it through our feedback widget on the Docs page. Whether you’re an early adopter of dark mode or just trying it out, we’d love your opinion. Just drop your feedback in the widget next to the color theme selector on the MongoDB Documentation page. Less strain, more gain Dark mode offers a sleek, modern look that brings a refreshing change from the traditional light mode. Beyond its stylish appearance, dark mode also provides significant practical benefits. Reducing the amount of bright light emitted from your screen helps minimize eye strain and fatigue, making extended periods of device use more comfortable. For those using OLED screens, dark mode can help conserve battery life, as these screens consume less power by displaying darker pixels. Whether you’re coding into the late hours or just looking for a more comfortable viewing experience, dark mode is a simple yet powerful tool to enhance your development experience. Try out dark mode on MongoDB Documentation today and enjoy a more comfortable, stylish, and efficient reading experience!
Vector Quantization: Scale Search & Generative AI Applications
We are excited to announce a robust set of vector quantization capabilities in MongoDB Atlas Vector Search . These capabilities will reduce vector sizes while preserving performance, enabling developers to build powerful semantic search and generative AI applications with more scale—and at a lower cost. In addition, unlike relational or niche vector databases, MongoDB’s flexible document model—coupled with quantized vectors—allows for greater agility in testing and deploying different embedding models quickly and easily. Support for scalar quantized vector ingestion is now generally available, and will be followed by several new releases in the coming weeks. Read on to learn how vector quantization works and visit our documentation to get started! The challenges of large-scale vector applications While the use of vectors has opened up a range of new possibilities , such as content summarization and sentiment analysis, natural language chatbots, and image generation, unlocking insights within unstructured data can require storing and searching through billions of vectors—which can quickly become infeasible. Vectors are effectively arrays of floating-point numbers representing unstructured information in a way that computers can understand (ranging from a few hundred to billions of arrays), and as the number of vectors increases, so does the index size required to search over them. As a result, large-scale vector-based applications using full-fidelity vectors often have high processing costs and slow query times, hindering their scalability and performance. Vector quantization for cost-effectiveness, scalability, and performance Vector quantization, a technique that compresses vectors while preserving their semantic similarity, offers a solution to this challenge. Imagine converting a full-color image into grayscale to reduce storage space on a computer. This involves simplifying each pixel's color information by grouping similar colors into primary color channels or "quantization bins," and then representing each pixel with a single value from its bin. The binned values are then used to create a new grayscale image with smaller size but retaining most original details, as shown in Figure 1. Figure 1: Illustration of quantizing an RGB image into grayscale Vector quantization works similarly, by shrinking full-fidelity vectors into fewer bits to significantly reduce memory and storage costs without compromising the important details. Maintaining this balance is critical, as search and AI applications need to deliver relevant insights to be useful. Two effective quantization methods are scalar (converting a float point into an integer) and binary (converting a float point into a single bit of 0 or 1). Current and upcoming quantization capabilities will empower developers to maximize the potential of Atlas Vector Search. The most impactful benefit of vector quantization is increased scalability and cost savings through reduced computing resources and efficient processing of vectors. And when combined with Search Nodes —MongoDB’s dedicated infrastructure for independent scalability through workload isolation and memory-optimized infrastructure for semantic search and generative AI workloads— vector quantization can further reduce costs and improve performance, even at the highest volume and scale to unlock more use cases. "Cohere is excited to be one of the first partners to support quantized vector ingestion in MongoDB Atlas,” said Nils Reimers, VP of AI Search at Cohere. “Embedding models, such as Cohere Embed v3, help enterprises see more accurate search results based on their own data sources. We’re looking forward to providing our joint customers with accurate, cost-effective applications for their needs.” In our tests, compared to full-fidelity vectors, BSON-type vectors —MongoDB’s JSON-like binary serialization format for efficient document storage—reduced storage size by 66% (from 41 GB to 14 GB). And as shown in Figures 2 and 3, the tests illustrate significant memory reduction (73% to 96% less) and latency improvements using quantized vectors, where scalar quantization preserves recall performance and binary quantization’s recall performance is maintained with rescoring–a process of evaluating a small subset of the quantized outputs against full-fidelity vectors to improve the accuracy of the search results. Figure 2: Significant storage reduction + good recall and latency performance with quantization on different embedding models Figure 3: Remarkable improvement in recall performance for binary quantization when combining with rescoring In addition, thanks to the reduced cost advantage, vector quantization facilitates more advanced, multiple vector use cases that would have been too computationally-taxing or cost-prohibitive to implement. For example, vector quantization can help users: Easily A/B test different embedding models using multiple vectors produced from the same source field during prototyping. MongoDB’s document model —coupled with quantized vectors—allows for greater agility at lower costs. The flexible document schema lets developers quickly deploy and compare embedding models’ results without the need to rebuild the index or provision an entirely new data model or set of infrastructure. Further improve the relevance of search results or context for large language models (LLMs) by incorporating vectors from multiple sources of relevance, such as different source fields (product descriptions, product images, etc.) embedded within the same or different models. How to get started, and what’s next Now, with support for the ingestion of scalar quantized vectors, developers can import and work with quantized vectors from their embedding model providers of choice (such as Cohere, Nomic, Jina, Mixedbread, and others)—directly in Atlas Vector Search. Read the documentation and tutorial to get started. And in the coming weeks, additional vector quantization features will equip developers with a comprehensive toolset for building and optimizing applications with quantized vectors: Support for ingestion of binary quantized vectors will enable further reduction of storage space, allowing for greater cost savings and giving developers the flexibility to choose the type of quantized vectors that best fits their requirements. Automatic quantization and rescoring will provide native capabilities for scalar quantization as well as binary quantization with rescoring in Atlas Vector Search, making it easier for developers to take full advantage of vector quantization within the platform. With support for quantized vectors in MongoDB Atlas Vector Search, you can build scalable and high-performing semantic search and generative AI applications with flexibility and cost-effectiveness. Check out these resources to get started documentation and tutorial . Head over to our quick-start guide to get started with Atlas Vector Search today.
MongoDB.local London 2024: Better Applications, Faster
Since we kicked off MongoDB’s series of 2024 events in April, we’ve connected with thousands of customers, partners, and community members in cities around the world—from Mexico City to Mumbai. Yesterday marked the nineteenth stop of the 2024 MongoDB.local tour, and we had a blast welcoming folks across industries to MongoDB.local London, where we discussed the latest technology trends, celebrated customer innovations, and unveiled product updates that make it easier than ever for developers to build next-gen applications. Over the past year, MongoDB’s more than 50,000 customers have been telling us that their needs are changing. They’re increasingly focused on three areas: Helping developers build faster and more efficiently Empowering teams to create AI-powered applications Moving from legacy systems to modern platforms Across these areas, there’s a common need for a solid foundation: each requires a resilient, scalable, secure, and highly performant database. The updates we shared at MongoDB.local London reflect these priorities. MongoDB is committed to ensuring that our products are built to exceed our customers’ most stringent requirements, and that they provide the strongest possible foundation for building a wide range of applications, now and in the future. Indeed, during yesterday’s event, Sahir Azam, MongoDB’s Chief Product Officer, discussed the foundational role data plays in his keynote address. He also shared the latest advancement from our partner ecosystem, an AI solution powered by MongoDB, Amazon Web Services, and Anthropic that makes it easier for customers to deploy gen AI customer care applications. MongoDB 8.0: The best version of MongoDB ever The biggest news at .local London was the general availability of MongoDB 8.0 , which provides significant performance improvements, reduced scaling costs, and adds additional scalability, resilience, and data security capabilities to the world’s most popular document database. Architectural optimizations in MongoDB 8.0 have significantly reduced memory usage and query times, and MongoDB 8.0 has more efficient batch processing capabilities than previous versions. Specifically, MongoDB 8.0 features 36% better read throughput, 56% faster bulk writes, and 20% faster concurrent writes during data replication. In addition, MongoDB 8.0 can handle higher volumes of time series data and can perform complex aggregations more than 200% faster—with lower resource usage and costs. Last (but hardly least!), Queryable Encryption now supports range queries, ensuring data security while enabling powerful analytics. For more on MongoDB.local London’s product announcements—which are designed to accelerate application development, simplify AI innovation, and speed developer upskilling—please read on! Accelerating application development Improved scaling and elasticity on MongoDB Atlas capabilities New enhancements to MongoDB Atlas’s control plane allow customers to scale clusters faster, respond to resource demands in real-time, and optimize performance—all while reducing operational costs. First, our new granular resource provisioning and scaling features—including independent shard scaling and extended storage and IOPS on Azure—allow customers to optimize resources precisely where needed. Second, Atlas customers will experience faster cluster scaling with up to 50% quicker scaling times by scaling clusters in parallel by node type. Finally, MongoDB Atlas users will enjoy more responsive auto-scaling, with a 5X improvement in responsiveness thanks to enhancements in our scaling algorithms and infrastructure. These enhancements are being rolled out to all Atlas customers, who should start seeing benefits immediately. IntelliJ plugin for MongoDB Announced in private preview, the MongoDB for IntelliJ Plugin is designed to functionally enhance the way developers work with MongoDB in IntelliJ IDEA, one of the most popular IDEs among Java developers. The plugin allows enterprise Java developers to write and test Java queries faster, receive proactive performance insights, and reduce runtime errors right in their IDE. By enhancing the database-to-IDE integration, JetBrains and MongoDB have partnered to deliver a seamless experience for their shared user-base and unlock their potential to build modern applications faster. Sign up for the private preview here . MongoDB Copilot Participant for VS Code (Public Preview) Now in public preview, the new MongoDB Participant for GitHub Copilot integrates domain-specific AI capabilities directly with a chat-like experience in the MongoDB Extension for VS Code .
Atlas Stream Processing: A Cost-Effective Way to Integrate Kafka and MongoDB
Developers around the world use Apache Kafka and MongoDB together to build responsive, modern applications. There are two primary interfaces for integrating Kafka and MongoDB. In this post, we’ll introduce these interfaces and highlight how Atlas Stream Processing offers an easy developer experience, cost savings, and performance advantages when using Apache Kafka in your applications. First, we will provide some background. The Kafka Connector For many years, MongoDB has offered the MongoDB Connector for Kafka (Kafka Connector). The Kafka Connector enables the movement of data between Apache Kafka and MongoDB, and thousands of development teams use it. While it supports simple message transformation, developers largely handle data processing with separate downstream tools. Atlas Stream Processing More recently , we announced Atlas Stream Processing—a native stream processing solution in MongoDB Atlas. Atlas Stream Processing is built on the document model and extends the MongoDB Query API to give developers a powerful, familiar way to connect to streams of data and perform continuous processing. The simplest stream processors act similarly to the primary Kafka Connector use case, helping developers move data from one place to another, whether from Kafka to MongoDB or vice versa. Check out an example: // Connect to MongoDB Atlas database using $source. s = { $source: { connectionName: 'myAtlasCluster', db: myDB', coll: ‘myCollection’ } } // Write your data to a Kafka topic using $emit. e = { $emit: { connectionName: 'myKafkaConnection', topic: myTopic } } // Create your processor and start it! sp.createStreamProcessor("mongoDBtoKafka", [s,e]) sp.mongoDBToKafka.start() Beyond making data movement easy, Atlas Stream Processing enables advanced stream processing use cases not possible in the Kafka Connector. One common use case is enriching your event data by using $lookup as a stage in your stream processor. In the example above, a developer can perform this enrichment by simply adding a lookup stage in the pipeline between source and sink. While the Kafka Connector can perform some single message transformations, Atlas Stream Processing makes for both an easier overall experience and gives teams the ability to perform much more complex processing. Choosing the right solution for your needs It’s important to note that Atlas Stream Processing was built to simplify complex, continuous processing and streaming analytics rather than as a replacement for the Kafka Connector. However, even for the more basic data movement use cases referenced above, it provides a new alternative to the Kafka Connector. The decision will depend on data movement and processing needs. Three common considerations we see teams making to help with this choice are ease of use, performance, and cost. Ease of use The Kafka Connector runs on Kafka Connect. If your team already heavily uses Kafka Connect across many systems beyond MongoDB, this may be a good reason to keep it in place. However, many teams find configuring, monitoring, and maintaining connectors costly and cumbersome. In contrast, Atlas Stream Processing is a fully managed service integrated into MongoDB Atlas. It prioritizes ease of use by leveraging the MongoDB Query API to process your event data continuously. Atlas Stream Processing balances simplicity (no managing servers, utilizing other cloud platforms, or learning new tools) and processing power to reduce development time, decrease infrastructure and maintenance costs, and build applications quicker. Performance High performance is increasingly a priority with all data infrastructure, but it’s often a must-have for use cases that rely on streams of event data (commonly from Apache Kafka) to deliver an application feature. Many of our early customers have found Atlas Stream Processing more performant than similar data movement in their Kafka Connector configurations. By connecting directly to your data in Kafka and MongoDB and acting on it as needed, Atlas Stream Processing eliminates the need for a tool in-between. Cost Finally, managing costs is a critical consideration for all development teams. We’ve priced Atlas Stream Processing competitively when compared to typical Kafka Connector configurations. Most hosted Kafka providers charge per task. That means each additional source and sink will generate a separate data transfer and storage cost that linearly scales as you expand. Atlas Stream Processing charges per Stream Processing Instance (SPI) worker and each worker supports up to four stream processors. This means potential cost savings when running similar configurations to the Kafka Connector. See more details in the documentation . Atlas Stream Processing launched just a few months ago. Developers are already using it for a wide range of use cases, like managing real-time inventories, serving contextually relevant recommendations, and optimizing yields in industrial manufacturing facilities. We can’t wait to see what you build and hear about your experience! Ready to get started? Log in to Atlas today. Already a Kafka Connector user? Dig into even more details and get started using our tutorial .
Exploring New Security, Billing, and Customization Features in Atlas Charts
MongoDB is excited to announce a few new updates to Atlas Charts that enable you to securely share insights, gain deeper visibility into expenses, and customize your most frequently used data visualizations. Based on specific feedback received from users of our native visualization tool, these significant improvements will make data analysis even more productive. We: Improved security in Atlas Charts for passcode-protected public dashboards Increased visibility into Atlas spending through an updated billing dashboard Introduced new customization for table charts through hyperlinks and hidden columns Secure insights with passcode-protected public dashboards First, there’s the new passcode-protected public dashboards feature that brings an extra layer of security to publicly shared dashboards—we understand that not everyone who benefits from Atlas Charts operates within MongoDB Atlas. Alongside the ability to schedule email reports and support for publicly-shared dashboards , we’re offering a new and secure way to spread insights with the launch of our latest feature. Add an extra layer of security to previously publicly shared dashboards, ensuring that only authorized users with the passcode can access your data. Enabling passcode protection on a dashboard is simple. As a dashboard owner, a new option is available to protect dashboard links with a passcode when sharing it publicly. Check the box to protect your public link with a passcode Once enabled, a passcode is automatically generated and can be copied to the clipboard (and regenerated on demand as needed). Viewers navigating to dashboards via the public link will see a new screen prompting them to enter a passcode. Once authenticated successfully, they can view the dashboard just as before. Easily access your dashboards by inputting your password when prompted Whether you're sharing insights with clients, stakeholders, or team members, rest assured that your data remains easily accessible yet secure. To learn more about the different ways we support dashboard sharing, check out our documentation . What’s new in the Atlas Charts billing dashboard Next, we continue to make enhancements to the MongoDB Atlas Charts billing dashboard , all of which provide insights into Atlas expenses. We are delighted to share that it’s now possible to see resource tags data, as well as billing data from all linked organizations inside the Atlas Charts billing dashboard. Additionally, users can now ingest billing data from another organization, provided they possess the organization’s API keys. These newly introduced features rely on the availability of billing data within the organization. And for those leveraging resource tags, the billing data will seamlessly integrate, empowering users to generate personalized charts or to incorporate tailored dashboard filters within the Atlas Charts billing dashboard. If cross-organization billing is enabled, editing the configuration will ingest the linked organization’s billing data for the last three months, with the option to extend this period to up to a year by creating a new ingestion. Project tags data in the Atlas Charts billing dashboard Resource tags are now seamlessly integrated into billing data and can be included in any of the charts or the dashboard filters inside the Atlas billing dashboard. For example, our MongoDB organization uses the Atlas auto-suggested tags “application” and “environment,” alongside a custom resource tag labeled "team." The following chart uses the tags data and shows the billing cost per team and per environment. A chart which depicts cost per team and environment using tags The subsequent chart presents the billing cost allocated per project and team, providing valuable insights into the primary cost drivers for each team's projects. A chart depicting cost allocated per project and team Users can also add a dashboard filter to the “tags” field, which will allow them to see the whole dashboard based on the selected tag values. In the next example, we have selected a specific “team” : “Charts” from the tags dashboard filter, so we can see all of the billing insights per team thanks to our custom tag. Billing insights filtered by specific "charts" team in an intuitive dashboard Linked organization’s data in the Atlas Charts billing dashboard For complex Atlas projects spanning multiple organizations, the Atlas Charts billing dashboard now seamlessly integrates billing data from all linked organizations. The most productive use case is to add a dashboard filter based on the "organizationId" to enable filtering data according to specific organizations for a more granular analysis of the spending. Dashboard filtered by the organizationID field to show insights for one organization Billing data from another organization Users can now ingest billing data from other organizations that are not directly linked, provided they possess authorization API keys, bringing the data you need to where you are. Provide the API key to ingest billing data from other organizations These new features in the Atlas Charts billing dashboard are designed to provide richer, more detailed insights into organization spend. Check out our documentation and our previous blog post to learn more about it. Hyperlinks and hidden columns for tables in Atlas Charts Of all the data visualization methods available in Atlas Charts, table charts rank as one of the most popular among our users. So it should come as no surprise that one of the most highly requested features from our customers is the ability to format columnar data as hyperlinks. We're excited to announce that this is now possible in Atlas Charts through the new hyperlink customization options available for table charts . With hyperlink customization, you can format columnar data as hyperlinks using any of the following URI protocols: http, https, mailto, or tel, and can be constructed statically or dynamically using encoded fields. Let’s assume we’ve created a table using the sample movies dataset in Atlas, with encodings like title, imdb.id, runtime, genre, poster_display—which is a calculated field —and more. Customization panel in Atlas Charts To turn movie titles into clickable links that direct users to their respective IMDB pages, navigate to the customization panel and click into the hyperlinking feature in the fields tab . We will format the title field as a hyperlink which links to the Internet Movie Database (IMDB) entry for that movie. IMDB URLs are formatted as follows, where id needs to be substituted with the value of the imdb.id field for each document. https://www.imdb.com/title/tt<id>/ Customize the “title” field in the table chart to link to IMDB using the “imdb.id” field in the URI input. Below, a preview displays the fully formatted URI with fields substituted for their values, helping to ensure it’s correct before we save it to be applied to the chart. Preview of URI in the hyperlinking panel Since we only need the imdb.id field to be encoded for the purpose of constructing the URI applied to the title field, we can hide the column from rendering using another new customization option. Select the imdb.id field in the customization panel, and toggle on the “Hide Column” option. Toggle "Hide Column" We also support using URI values directly from fields (provided they use one of the supported protocols). Let’s see this in action by creating a hyperlink to the movie poster. In the URI input, trigger the encoded field menu using the @ keyboard shortcut, and select the poster field. Similar to the previous example, a preview will be displayed. After saving and applying the hyperlink formatting, we can hide the rendering of the poster field as needed to keep the chart clean. Use the @ keyboard shortcut to trigger the encoded field menu All these options are accessible in the customization panel, making it straightforward to enhance table charts with interactive hyperlinks. For more detailed instructions, visit our documentation . As we conclude this roundup, we hope you’re as excited about these updates as we are. The Atlas Charts team is dedicated to continuously improving Atlas Charts to meet your needs and enhance your data visualization experience. Stay tuned for more updates, and happy charting! New to Atlas Charts? Get started today by logging into or signing up for MongoDB Atlas , deploying or selecting a cluster, and activating Charts for free.
MongoDB Atlas for Government Supports GCP Assured Workloads
We’re excited to announce that MongoDB Atlas for Government now supports the US regions of Google Cloud Assured Workloads, alongside existing support for AWS GovCloud and AWS US regions. This expansion offers greater flexibility and expanded support for public sector organizations and the independent software vendors (ISVs) that serve them as they modernize applications and migrate workloads to the cloud. Furthermore, MongoDB Atlas for Government is now available for purchase through the Google Cloud Marketplace . MongoDB Atlas for Government: Driving digital transformation in the public sector MongoDB Atlas for Government is an independent, dedicated version of MongoDB Atlas, designed specifically to meet the unique needs of the U.S. public sector and ISVs developing public sector solutions. This developer data platform provides the versatility and scalability required to modernize legacy applications and migrate workloads to the cloud, all within a secure, fully-managed, FedRAMP authorized environment. Refer to the FedRAMP Marketplace listing for additional information about Atlas for Government. By leveraging the full functionality of MongoDB's document database and application services, Atlas for Government supports a wide range of use cases within a unified developer data platform, including Internet of Things, AI/ML, analytics, mobile development, single view, transactional workloads, and more. Ensuring robust resilience and comprehensive disaster recovery, Atlas for Government maintains business continuity and minimizes downtime. With a ~99.995% uptime SLA , auto-scaling to handle data consumption fluctuations, and automated backup and recovery, organizations can have peace of mind that their data is always protected. Getting started with MongoDB Atlas for Government MongoDB Atlas for Government can be used to create database clusters deployed to a single region or spanning multiple US regions. Google Cloud Assured Workloads US regions are now supported in Atlas for Government projects tagged as “Gov regions only,” allowing for the use of both traditional Google Cloud regions as well as Assured Workloads US regions. To get started, create a project in Atlas for Government and make sure to select 'Designate as a Gov Cloud regions-only project' during the project creation process. After creating the project, you can set up a MongoDB cluster in the GCP regions. To do this, start the cluster creation process and select GCP as the Cloud Provider, as shown in the figure below. You'll then be prompted to choose one or more GCP regions for your cluster. You can find more details on supported cloud providers and regions in the Atlas for Government documentation . Creating multi-cloud clusters The introduction of support for Google Cloud Assured Workloads (US regions) makes MongoDB Atlas for Government the first fully managed multi-cloud data platform authorized at FedRAMP Moderate. This means that public sector organizations and ISVs can now deploy clusters across Google Cloud Assured Workloads US regions and AWS GovCloud regions, in addition to deploying database clusters across multiple US regions. Whether prioritizing performance, cost, or specific feature sets, Atlas for Government empowers teams to deploy application architectures that simultaneously take advantage of the best-of-class services from multiple cloud providers while meeting FedRAMP requirements. Multi-cloud support also provides additional resiliency and enhanced disaster recovery, safeguarding data and applications against potential service outages and failures with automatic failover. Ensuring robust data protection and seamless continuity MongoDB Atlas for Government now supports Google Cloud Assured Workloads US regions, expanding its multi-cloud capabilities alongside existing support for AWS GovCloud and AWS US regions. This enhancement provides public sector organizations and ISVs with the flexibility to modernize applications and migrate workloads in a secure, FedRAMP authorized environment. With robust resilience, comprehensive disaster recovery, and a ~99.995% uptime SLA, Atlas for Government ensures data protection and business continuity. By offering a unified developer data platform for a wide range of use cases, Atlas for Government empowers teams to leverage best-in-class cloud services while meeting stringent compliance requirements. How do I get started? Visit our product page to learn more about MongoDB Atlas for Government. Or, read the Atlas for Government documentation to learn how to get started today.
Atlas Search Nodes: Now with Multi-Region Availability
At MongoDB, we are continually refining our products to try and create the simplest and most seamless developer experience possible. This mantra has also been applicable to how we think about search, from the beginning with Atlas Text Search, to the announcement of the next paradigm with Atlas Vector Search. We have continued to expand this vision with the introduction of Search Nodes, initially launching on AWS , and then expanding to both Google Cloud and Microsoft Azure . Today we’re excited to take the next step in that journey with the announcement of multi-region availability on all three major cloud providers. Search Nodes: Isolation and scale As a quick refresher, Search Nodes provide dedicated infrastructure for Atlas Search and Vector Search workloads, enabling 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. Since our announcements, we’ve been thrilled with the excitement around Search Nodes and the desire for better control, flexibility, and availability for scaling both Atlas Search and Vector Search workloads. Incorporating Search Nodes into your deployment delivers workload isolation, and the ability to optimize resource usage. A visual of the evolution from the previous coupled architecture to dedicated nodes is shown below: Figure 1: Improved workload sizing alignment and enhanced scalability with Search Nodes Introducing Global Availability Another tenet of our builder's journey is making sure the flexibility, scalability, and performance with Search Nodes are available to everyone, regardless of the cloud you’re using or cloud region. Today, we’re excited to officially announce multi-region availability for Search Nodes to allow anyone to better optimize resource usage regardless of location. Now, with multi-region availability, you can take full advantage of global scalability by no longer being limited to one geographic area. Furthermore, you now have the peace of mind by having the redundancy needed to protect yourself in the case of any unforeseen outage event, whether due to technical issues or natural disasters that could cause data center downtime. Figure 2: Multi-region availability on all three major cloud providers Here is a quick video tutorial about how to enable Search Nodes, as well as take advantage of multi-region availability: Brief tutorial on how to enable multi-region Search Nodes With today’s announcements we’re excited to bring the power and control of dedicated Search Nodes to people using all clouds and regions across the globe. We’re excited to see the continued adoption and improved results from having greater ubiquity across your search implementations. As always, reach out to us with any feedback, as we’d love to hear what you think!
Atlas Stream Processing Adds AWS Regions, VPC Peering, & More!
Since announcing the general availability of Atlas Stream Processing —MongoDB’s native stream processing solution—it’s been exciting to see development teams across technology, retail, and manufacturing begin to run production stream processing workloads critical to their businesses. Today, we're announcing four key updates to Atlas Stream Processing. Support for AWS Regions across the US, Europe, and APAC First, we're thrilled to announce that Atlas Stream Processing now supports eight new AWS regions . This expansion enhances deployment flexibility across the US, Europe, and APAC. Adding these new AWS regions broadens our reach and opens up a world of possibilities for users. We're committed to further expanding our reach by adding more regions and cloud providers in the future. Newly supported regions launched today include: Region AWS Region Name Oregon, USA us-west-2 Sao Paulo, Brazil sa-east-1 Ireland eu-west-1 London, England eu-west-2 Frankfurt, Germany eu-central-1 Mumbai, India ap-south-1 Singapore ap-southeast-1 Sydney, Australia ap-southeast-2 Adding these new AWS regions for Atlas Stream Processing is the latest example of the close partnership between MongoDB and AWS. For example, over the past year, MongoDB announced integrations with Amazon Bedrock and Amazon Q Developer; MongoDB was named an AWS Generative AI Competency Partner ; we launched the MongoDB AI Applications Program —which helps customers rapidly build AI applications—with AWS and other tech leaders; and MongoDB was named the AWS Technology Partner of the Year at the AWS Partner Summit Taiwan. Support for VPC peering Next, Atlas Stream Processing now supports VPC peering for self-hosted Apache Kafka on AWS and Amazon Managed Streaming for Apache Kafka (AWS MSK) . VPC peering is a secure method for connecting between virtual private clouds . As stream processing solutions like Atlas Stream Processing inherently connect to external data sources outside of MongoDB, the ability to make these connections as if your resources are on the same private network is a critical security requirement for many organizations. Users can select from any VPC peer configured within an Atlas project when setting up Kafka connections. Because peering is at the stream processing connection level, developers can configure Atlas Stream Processing to consume events from one Kafka cluster and produce them to another in a different VPC. Note that this feature has an additional cost. You can learn more in our documentation . Expanded support for Apache Kafka Third, we’re expanding capabilities for Apache Kafka in this release. Kafka is one of two key data sources Atlas Stream Processing supports today. One of Kafka’s strengths is its flexibility, allowing developers to customize configurations to suit various use cases, including those that rely on continuous stream processing. That flexibility can also create complexity, but Atlas Stream Processing focuses on making Kafka’s critical features easily accessible using the MongoDB Query API. By adding support for Kafka keys, developers can now read and write Kafka keys on their events, which enables filtering, partitioning, and aggregating based on key values. This ability provides greater control over routing processed data and is powerful for many stream processing use cases. Expanded Atlas Admin API support Lastly, we have added support for creating and deleting stream processors, as well as fetching operational stats of stream processors using the Atlas Admin API. Developers relying on the Admin API as a primary interface for interacting with Atlas will find this a welcome addition for managing their stream processors. Learn more in the documentation . With these new capabilities—additional AWS region support, VPC peering, the ability to use Kafka keys, and improved stream processing support for the Atlas Admin API—we've made it easier than ever for developers to integrate stream processing into their applications. We're excited to see the innovative ways you'll use these features. Ready to unlock the full potential of Atlas Stream Processing? Log in to Atlas today and start exploring the new features. We're eager to hear your feedback, so don't hesitate to share it with us on UserVoice . Your insights help us continue to improve and innovate.
Introducing AI-Powered Natural Language Mode in Atlas Charts
At MongoDB, our mission is to empower developers to seamlessly and efficiently build modern applications. To that end, we’ve announced a number of tools and improvements to help developers build faster , from AI-powered SQL query conversion in Relational Migrator to the MongoDB Provider for Entity Core Framework. In the same spirit, today at MongoDB.local Sydney we’re excited to announce the general availability of Natural Language Mode in Atlas Charts . Not only does this release help developers move faster with AI, it also showcases the work of MongoDB’s nimble Sydney engineering team. Intelligent developer experiences: The next level of productivity Over the past year, we’ve introduced a range of intelligent developer experiences across our platform, all of which aim to simplify and accelerate development processes. Overall, our goal is to make tools faster, better, and more connected for our developers, and to enable developers to leverage the power of AI to enhance their experience. As IBM highlights , true developer productivity involves delivering high-quality outputs that satisfy customers but also avoid developer burnout. By reducing learning curves, saving time, and providing easily accessible insights, intelligent features enable developers to focus on their most important work: building modern applications that solve real-world problems through outputs that are truly worth developers’ time and effort. Here’s what we’ve introduced: MongoDB Compass —Developers can use natural language to compose everything from simple queries to sophisticated, multi-stage aggregations. MongoDB Relational Migrator —With natural language, developers can convert SQL queries to MongoDB Query API syntax, streamlining migration projects. MongoDB Documentation —An intelligent chatbot, built on top of MongoDB Atlas and Atlas Vector Search, enables lightning-fast information discovery and troubleshooting during software development. By integrating AI into our most important developer tools, we’re helping developers cut through the noise and focus on creating innovative solutions. Simplifying data visualization with Natural language Mode Visualizing data can be an incredibly effective way of gleaning insights from application data, but creating effective visualizations can require specialized knowledge and experience with business intelligence (BI) tools. Atlas Charts was built to level the playing field. Now, with Natural Language Mode, developers can create visualizations simply by asking questions in plain English. Natural Language Mode reduces technical barriers and makes data visualization accessible to anyone with data in MongoDB Atlas. This means faster chart creation at an advanced scale—all within the Atlas ecosystem. Since announcing its development in the fall of 2023, we’ve made significant enhancements to Natural Language Mode, including an expanded suite of chart types built for all kinds of data from patient records to financial trading flows. We’ve also improved performance to ensure faster and more accurate chart generation, including the ability to handle more sophisticated prompts, filtering, sorting, binning, and limiting. In the next few weeks, we will add more chart variation, as well as another upgrade to our model that will increase accuracy and responsiveness by up to 50%. Natural Language Mode in action Now let’s walk through a few examples of what using Natural Language Mode in an Atlas Charts looks like: Generating a chart using Natural Language Mode With a simple query like "Show me the sales performance by country and product for Q4 FY2023," developers can instantly generate a relevant chart. Customizing charts in Classic Mode After generating a chart, developers can pull it into Classic Mode to fine-tune and customize it to fit their dashboard needs. Scheduling Dashboards Developers can also schedule their dashboards to be shared via email, ensuring that key stakeholders receive up-to-date insights automatically. More data, more insights Customers are already excited about the possibilities of Atlas Charts and Natural Language Mode, from operational analytics to embedded analytics. For example, one of MongoDB’s early customers has been using Natural Language Mode to track server performance across various regions. Business analysts leverage the feature to gain insights into server performance and share these insights internally. They plan to embed these visualizations into their customer portal using the embedding SDK offered by Atlas Charts . Another customer said: "As a developer with no prior experience in analytics, I was excited to see Natural Language Mode generate a clear value proposition that showcased what the product is capable of. It makes me want to throw more data in the database to get more insights." So check out Natural Language Mode in Atlas Charts today, and experience firsthand how AI can simplify and accelerate your data visualization workflows. Try out Natural Language Mode in Atlas Charts to transform your data visualization process. New to Charts? Register for MongoDB Atlas , deploy a cluster, and activate Charts for free.
Elevate Your Python AI Projects with MongoDB and Haystack
MongoDB is excited to announce an integration with Haystack, enhancing MongoDB Atlas Vector Search for Python developers. This integration amplifies our commitment to providing developers with cutting-edge tools for building AI applications centered around semantic search and Large Language Models (LLMs). We’re excited to partner with MongoDB to help developers build top-tier LLM applications. The new Haystack and MongoDB Atlas integration lets developers seamlessly use MongoDB data in Haystack, a reliable framework for creating quality LLM pipelines for use cases like RAG, QA, and agentic pipelines. Whether you're an experienced developer or just starting, your gen AI projects can quickly progress from prototype to adoption, accelerating value for your business and end-users. Malte Pietsch, co-founder and CTO, deepset Simplifying AI app development with Haystack Haystack is an open-source Python framework that simplifies AI application development. It enables developers to start their projects quickly, experiment with different AI models, and to efficiently scale their applications. Indeed, Haystack is particularly effective for building applications requiring semantic understanding and natural language processing (NLP), such as chatbots and question-answering systems. Haystack’s core features include: Components: Haystack breaks down complex NLP tasks into manageable components, such as document retrieval or text summarization. With the new MongoDB-Haystack integration, MongoDB becomes the place where all your data lives, ready for Haystack to use. Pipelines: Haystack lets you link components together into pipelines for more complex tasks. With this integration, your MongoDB data flows through these pipelines. Agents: Haystack Agents use LLMs to resolve complex queries. They can decide which tools (or components) to use for a given question, leveraging MongoDB data to deliver smarter answers. Atlas Vector Search: Enhance AI development with Haystack At the heart of the new integration is MongoDB Atlas Vector Search , transforming how applications search and retrieve data. By leveraging vector embeddings, Atlas Vector Search goes beyond mere keyword matching: it interprets the intent behind queries, enabling applications to provide highly relevant, context-aware responses. This is a breakthrough for Python developers who aim to build applications that think and understand like humans. Building on this foundation, the Atlas Vector Search and Haystack integration gives Python developers a powerful toolkit for navigating the complexities of AI application development. MongoDB becomes a dynamic document store within Haystack's framework, optimizing data storage, processing, and retrieval. Additionally, the integration eases the use of advanced AI models from leading providers such as OpenAI and Cohere into your applications. Developers can thus create applications that do more than just answer queries—they grasp and act on the underlying intent, ensuring responses are both accurate and contextually relevant. What this means for Python developers For Python developers, this integration means: Faster development: Developers can focus on building and innovating rather than spending time configuring and managing infrastructure. MongoDB's integration with Haystack means you can get up and running quickly, leveraging the best of both technologies to accelerate your development cycles. Smarter applications: By utilizing Haystack's powerful Natural Language Processing tooling in combination with MongoDB Atlas Vector Search’s efficient data handling, developers can create applications that understand and process natural language more effectively. This results in applications that can provide more accurate and contextually relevant responses that resonate with user intent. Access to pre-trained AI models: With seamless integration of leading generative AI models from providers like OpenAI, Anthropic, Cohere, Hugging Face, and AWS Bedrock, Python developers can easily incorporate advanced AI functionalities into their projects. This means developers can quickly adopt state-of-the-art models without the need for extensive training or fine-tuning, saving time and resources. Flexible and scalable pipelines: Haystack's modular approach to building AI applications, through its use of components and pipelines, allows developers to create flexible and scalable solutions. With MongoDB data seamlessly flowing through these pipelines, you can easily adapt and expand your applications to meet growing demands and new challenges. Robust search capabilities: Atlas Vector Search transforms the way applications retrieve and interpret data, going beyond simple keyword searches. It enables applications to perform high-precision searches that return more relevant and semantically rich results. This advanced search capability is crucial for developing applications that require high levels of semantic understanding and accuracy. By integrating MongoDB with Haystack, Python developers are equipped with a powerful toolkit that not only simplifies the AI development process but also significantly enhances the intelligence and functionality of their applications. Whether you are building chatbots, search engines, or other AI-driven applications, this integration provides the tools you need to create innovative and impactful solutions. Get started now Start leveraging the MongoDB and Haystack integration for your AI development. Explore our tutorial , documentation , or check out our github repository to begin building smarter, more intuitive Python projects today!
Exact Nearest Neighbor Vector Search for Precise Retrieval
With its ability to efficiently handle high-dimensional, unstructured data, vector search delivers relevant results even when users don’t know what they’re looking for and uses machine learning models to find similar results across any data type. Rapidly emerging as a key technology for modern applications, vector search empowers developers to build next-generation search and generative AI applications faster and easier. MongoDB Atlas Vector Search goes beyond the approximate nearest neighbor (ANN) methods with the introduction of exact nearest neighbor (ENN) vector search . This innovative capability guarantees retrieval of the absolute closest vectors to your query, eliminating the accuracy limitations inherent in ANN. In sum, ENN vector search can help you unleash a new level of precision for your search and generative AI applications, improving benchmarking and moving to production faster. When exact nearest neighbor (ENN) vector search benefits developers While ANN shines in searching across large datasets, ENN vector search offers advantages in specific scenarios: Small-scale vector data: For datasets under 10,000 vectors, the linear time complexity of ENN vector search makes it a viable option, especially considering the added development complexity of tuning ANN parameters. Recall benchmarking of ANN queries: ANN queries are fast, particularly as the scale of your indexed vectors increases, but it may not be easy to know whether the retrieved documents by vector relevance correspond to the guaranteed closest vectors in your index. Using ENN can help provide that exact result set for comparison with your approximate result set, using jaccard similarity or other rank-aware recall metrics. This will allow you to have much greater confidence that your ANN queries are accurate since you can build quantitative benchmarks as your data evolves. Multi-tenant architectures: Imagine a scenario with millions of vectors categorized by tenants. You might search for the closest vectors within a specific tenant (identified by a tenant ID). In cases where the overall vector collection is large (in the millions) but the number of vectors per tenant is small (a few thousand), ANN's accuracy suffers when applying highly selective filters. ENN vector search thrives in this multi-tenant scenario, delivering precise results even with small result sets. Example use cases The small dataset size allows for exhaustive search within a reasonable timeframe, making exact nearest neighbor approach a viable option for finding the most similar data point, improving accuracy confidence in a number of use cases, such as: Multi-tenant data service: You might be building a business providing an agentic service that understands your customers’ data and takes actions on their behalf. When retrieving relevant proprietary data for that agent, it is critical that the right metadata filter be applied and that ENN be executed to retrieve the right sets of documents only corresponding to the appropriate data tenant IDs. Proof of concept development: For instance, a new recommendation engine might have a limited library compared to established ones. Here, ENN vector search can be used to recommend products to a small set of early adopters. Since the data is limited, an exhaustive search becomes practical, ensuring the user gets the most relevant recommendations from the available options. How ENN vector search works on MongoDB Atlas The ENN vector search feature in Atlas integrates seamlessly with the existing $vectorSearch stage within your Atlas aggregation pipelines. Its key characteristics include: Guaranteed accuracy: Unlike ANN, ENN always returns the closest vectors to your query, adhering to the specified limit. Eventual consistency: Similar to approximate vector search, ENN vector search follows an eventual consistency model. Simplified configuration: Unlike approximate vector search, where tuning numCandidates is crucial, ENN vector search only requires specifying the desired limit of returned vectors. Scalable recall evaluation: Atlas allows querying a large number of indexed vectors, facilitating the calculation of comprehensive recall sets for effective evaluation. Fast query execution: ENN vector search query execution can maintain sub-second latency for unfiltered queries up to 10,000 documents. It can also provide low-latency responses for highly selective filters that restrict a broad set of documents into 10,000 documents or less, ordered by vector relevance. Build more with ENN vector search ENN vector search can be a powerful tool when building a proof of concept for retrieval-augmented generation (RAG), semantic search, or recommendation systems powered by vector search. It simplifies the developer experience by minimizing overhead complexity and latency while giving you the flexibility to implement and benchmark precise retrieval. Explore more use cases and build applications faster, start experimenting with ENN vector search. Head over to our quick-start guide to get started with Atlas Vector Search today.