Recap of Product Announcements at MongoDB.local NYC, 2023

Leo Zheng
June 22, 2023 | Updated: November 1, 2023

This post is also available in: Deutsch, Français, Español, Português, 中文

Today at MongoDB.local NYC, we unveiled several new capabilities across our developer data platform to help users and customers build, iterate, and scale their applications with MongoDB.

Development teams are being asked to deliver compelling and differentiated user experiences that are faster and smarter than ever before. At the same time, they must do so as quickly and as efficiently as possible.

MongoDB’s developer data platform is essential for teams who strive to innovate quickly and efficiently. It allows developers to support a wide variety of application use cases in their organization through a unified API and eliminates the need to bolt-on, learn, and maintain separate database point solutions.

Expanding the range of modern applications you can build on MongoDB

A new generation of AI-powered experiences are being developed where vectors serve as a foundational element that makes these applications possible. Vectors are mathematical representations of the characteristics of unstructured data — including text, images, videos, audio files, and more — and occupy n-dimensional space where n is the number of characteristics in the dataset. Whether data is similar or not is based on the distance between vectors in this n-dimensional vector space. A vector database allows users to query vectors to determine what’s similar or related, without relying on keyword matching. Announced today, Atlas Vector Search allows you to store, index, and query vectors alongside your operational and transactional data in documents without the overhead of adding, learning, and maintaining another database system. The addition of vector search to MongoDB Atlas enables teams to deliver more relevant, context-aware results to end-users, including the ability to augment applications built on Large Language Models (LLMs) with proprietary data to improve accuracy and performance. Atlas Vector Search is available today in public preview.

Applications today also need to be more real-time than ever, but processing streams of data and bringing them into applications is complex and challenging. Most organizations introduce a stream processing point solution with a different set of APIs, drivers, and tools that create a fragmented developer experience, operational complexity, and additional cost. Coming soon in private preview, Atlas Stream Processing will transform how development teams build event-driven applications. Developers can use the same query language and flexible document data model to work with both streaming data and their data in the database for use cases ranging from monitoring network traffic for intrusions to conducting live route planning based on current road conditions.

Companies such as Albertsons, Glassdoor, and Anywhere Real Estate, the largest franchisor of residential real estate brands in the world, depend on Atlas Search to deliver full-text functionality in their applications without having to deploy and sync data from their database to a separate search engine. With new search query analytics, developers will gain insight into what their end users are searching for, enabling them to better refine and customize their search logic. In addition, Atlas Search indexes can now be created and managed in language drivers (starting with Node.js), MongoDB Compass, and the MongoDB Shell, making it easy for developers who prefer to work with their indexes programmatically. And finally, dedicated search nodes were announced today in private preview, which will enable teams to independently scale and optimize resources for their search workloads for improved performance at scale, higher availability, and faster index builds.

Improving the foundation of performance, scale and security

The work we’re putting into improving the performance, scalability, and security of our developer data platform is just as important as increasing its range of functional capabilities.

Starting with MongoDB 7.0, improvements to query execution will reduce the number of disk reads, compute resources, and memory required to execute certain queries, delivering improved performance and more efficient resource consumption. In particular, the new query execution strategy will speed up grouping and reshaping documents, filtering and sorting documents, and $lookups used for joining data across collections.

Last year we introduced queryable encryption in preview. This industry-first searchable encryption scheme allows users to encrypt sensitive data fields — such as personally identifiable information — client-side, and store that data as fully randomized encrypted data in the database, all while preserving the ability to run expressive queries. With MongoDB 7.0, queryable encryption will support equality search, with support for range, prefix, suffix, and substring queries to follow.

MongoDB 7.0 will be generally available later this summer.

Continued focus on a first-class developer experience

Few things are more critical to software development than ensuring a top-notch developer and operational experience. We remain dedicated to improving the usability of our products and expanding the set of tools available to developers for building with MongoDB.

Kotlin is emerging as a popular language for both mobile development and server-side development. Today we unveiled a new official Kotlin driver, allowing Kotlin developers to build applications on MongoDB with confidence, knowing we are committed to supporting this fast-growing language community. We also released PyMongoArrow, a new library for developers and data analysts to easily export data in MongoDB to Python-based analytics stacks, including Apache Arrow, Pandas, and NumPy.

As more companies optimize their DevOps pipeline, tooling that enables programmatic automation can greatly improve efficiency and productivity. Developers can now provision resources on MongoDB Atlas using the Amazon Web Services Cloud Development Kit (AWS CDK) in C#, Go, Java, and Python, as well as with Node.js and Typescript. Companies who leverage Kubernetes can use the MongoDB Atlas CLI to install the Atlas Kubernetes Operator and export existing deployments for simpler infrastructure management and provisioning.

Path to application modernization

Application modernization continues to be a key investment for many enterprises to unlock rapid software iteration to meet ever-evolving application requirements. MongoDB Relational Migrator, now generally available, helps accelerate and de-risk migrations from common relational databases — including Oracle, SQL Server, MySQL, and PostgreSQL — to MongoDB. Not only does this tool handle the data migration itself, it also allows migration teams to view MongoDB data modeling recommendations, define schema changes with a visual interface, and generate application code in their programming language or framework to get a head start on refactoring their applications to reflect the newly designed MongoDB schema.

To see more announcements and get the latest product updates, visit our What’s New page.

Head to the MongoDB.local hub to see where we'll be showing up next.

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News From MongoDB.local NYC: Game-changing Migration Tool Now Available

This post is also available in: Deutsch , Français , Español , Português The database has always been the heart of an application. Teams recognize that the choice of a database is one of the most critical decisions in developing software, influencing not only the speed at which developers can build and iterate, but also an application’s performance, functionality, and overall user experience. Today, tens of thousands of organizations are being held back by their legacy systems built on relational databases. They struggle with the headaches, high costs, and poor developer agility caused by data silos and architecture sprawl built up over decades as they try to address new requirements and build transformative experiences. Modernizing the data infrastructure means reducing architectural complexity, and adopting flexible data platforms like MongoDB that can support a wider range of use cases and data types. But every IT leader knows that transitioning away from legacy databases is easier said than done, and that they are often overwhelming and time-consuming projects. This is even more true when trying to shake the relational database mindset and instead adopt a developer-first approach to data. MongoDB's Relational Migrator is now available to help organizations streamline their transition from relational databases to MongoDB by tackling some of the most common challenges we see in migration projects: effective data modeling, migrating data, and modernizing app code. Today, Relational Migrator supports migrations from Oracle, SQL Server, MySQL, and PostgreSQL directly. By switching to MongoDB, companies can reap major rewards when it comes to modernizing an essential component of their software. Not only do they enjoy faster development and performance, but also improved reliability, scalability, and user experience. Additionally, MongoDB's advanced features, such as full-text search, time-series data support, and edge-to-cloud synchronization enable applications to take on more use cases on an efficient, easy-to-maintain data infrastructure. With Relational Migrator, teams preparing for app modernization projects can accelerate and reduce the risk of their relational database migrations so they can start taking advantage of everything MongoDB has to offer. Take the guesswork out of the migration process Data modeling in MongoDB is one of the most common barriers we see when organizations come over from a relational background. MongoDB can be treated like a relational database, but to unlock the full potential of the document model organizations need to change how they think about their data and model it for how their applications are going to use it. Relational Migrator takes the guesswork out of data modeling by analyzing an application's existing relational schema and proposing a recommended MongoDB schema based on best practices. As teams use the visual interface to compare and build their schema, Relational Migrator will continue to offer recommended mappings to further expedite schema design, but more importantly, help developers do it well. Not only does Relational Migrator visually compare schemas, but it also applies the transformations as it moves data into MongoDB in an efficient manner, providing users with the ability to run both systems in parallel and apply changes from the source database to MongoDB on an ongoing basis. Migrating data is only one part of application modernization. To give developers a head start, Relational Migrator even generates app code for various development languages and frameworks that reflects the new MongoDB schema they’ve designed. With the launch of Relational Migrator, MongoDB is unlocking the full potential of its powerful developer data platform , providing new implementation possibilities for customers and a wider range of applications. Learn more about what Relational Migrator can do for you. Head to the MongoDB.local hub to see where we'll be showing up next.

June 22, 2023
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MongoDB Database Observability: Integrating with Monitoring Tools

This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.

November 14, 2024