Understanding the MongoDB Stable API and Rapid Release Cadence

Shelby Carpenter
March 10, 2022 | Updated: June 6, 2022
#Stable API

MongoDB provides the world’s leading data platform, and we strive to make it as easy as possible for developers to build and evolve their applications. In MongoDB 5.0, we made two important updates to the way we release database versions and make them available to customers: One was the creation of the Stable API and the other was our new quarterly MongoDB Rapid Release cadence. Now that we have a few Rapid Releases under our belt (visit our blog to learn about MongoDB 5.1 and 5.2), we wanted to provide an update on the API and the process for choosing between the Major and Rapid Release tracks.

The MongoDB Stable API

The Stable API was created to make it easier for customers to upgrade to the latest MongoDB version without worrying about introducing breaking changes to their code base. It includes a subset of MongoDB commands that applications commonly use, and MongoDB ensures those commands remain consistent when we release new database versions. That effectively decouples the application lifecycle from the database lifecycle. Providing this level of consistency is especially important for helping customers consume our innovations faster and take advantage of MongoDB’s new release cadence.

It was previously known as the Versioned API, but we changed the name to Stable API to avoid potential confusion. From our conversations with users and customers, it became clear that the previous name gave the impression that the API would change with each incremental MongoDB version release. That is not the case, so we say “hello” to the MongoDB Stable API.

The MongoDB Rapid Release Cadence

MongoDB Atlas customers with clusters on a Dedicated Tier (M10+) can opt in to Rapid Releases to get the latest features from MongoDB on a quarterly basis. Atlas customers are initially on the Major Release track; Major Releases happen annually and contain the previous year’s Rapid Releases by default.

Customers who choose the Major Release track will have the following upgrade flow: 5.0 -> 6.0 -> 7.0, etc., and can schedule when they want to upgrade to each new Major Release after it enters general availability. Customers who opt in to the Rapid Release track will have the following upgrade flow: 5.0 -> 5.1 -> 5.2 -> 5.3 -> 6.0 -> 6.1 -> 6.2, etc. If you are on a Major Release and decide to change tracks, then you will automatically go to the next Rapid Release. (If you are on 6.0 and 6.2 is the latest Rapid Release, you can jump directly from 6.0 to 6.2 without having to upgrade to 6.1 first.) Customers on the Major Release track will still receive regular patch upgrades.

”

Users on the Rapid Release track who later decide to opt out will need to do so at the time of the next Major Release. If you’re MongoDB 5.2 and want to change back to the Major Release track, for example, you will wait to leave the Rapid Release track until the next Major Release, MongoDB 6.0, is available.

As another example, at the time of publication, the latest Major Release is 5.0 and the latest Rapid Release is 5.2. A customer on MongoDB 4.4 (an earlier Major Release prior to the new release cadence and numbering scheme) would need to manually upgrade from 4.4 to 5.0 before opting in to Rapid Releases and getting MongoDB 5.2.

To opt in to the Rapid Release cadence, choose the “Latest Release” option in the Atlas web UI. Rapid Releases are only supported for MongoDB Atlas. For on-premises environments, they should be used only for development builds and testing and not for production environments.

Apart from MongoDB Atlas Dedicated Tier clusters, Atlas supports Shared Tier clusters M0, M2, and M5 — which provide 512MB, 2GB, and 5GB of storage, respectively — as well as managed serverless instances, which are currently in public preview. Shared Tier clusters are always on the Major Release track, and serverless instances are on the Rapid Release track.

With options to get Major or Rapid Releases in MongoDB Atlas and to use the Stable API for consistency across versions, MongoDB customers have more flexibility than ever to choose how to take advantage of the latest MongoDB database upgrades. Stay tuned for the latest innovations from MongoDB in the 5.3 release this spring, and join us at MongoDB World this summer to learn about MongoDB 6.0 and more!

← Previous

How to Build the Right App For Your Mobile Workforce

The average turnover rate in the retail industry is slightly above 60%. This high turnover rate translates into more than 230 million days of lost productivity and $19 billion in costs associated with recruiting, hiring, and training, according to Human Resources Today . When surveyed by Harvard Business Review , 86% of the organizations polled said frontline workers need better technology-enabled insights to be able to make good decisions in the moment. The survey also pointed out that leading retailers are starting to consider the impact tech can have on productivity. Combined, the data points to a growing chorus of evidence that suggests a mobile workforce — where employees are empowered with the digital tools needed to not only provide a great customer experience but also make their own jobs easier — is less likely to feel burnout and be dissatisfied with their jobs. What a mobile workforce can do for your organization With an intuitive, modern app, you can accomplish key business objectives. Improve the customer buying experience: Frontline staff equipped with mobile-first technologies can better match the fluency of the customers. It enables them to serve the customer better by providing accurate, real-time information, such as what items are in stock, or make suggestions based on customer buying history. Increase employee productivity: According to Deloitte , workers spend as much as three hours each week looking for the information they need. Imagine the impact regaining those hours could have on worker productivity! Track and improve performance, sales, and buying experience through data analysis: The potential of workforce enablement apps extends beyond just identifying what items are in stock at which stores. They can also gather valuable data that can reveal key patterns in everything from customer purchase habits and target peak shopping times to individual worker metrics such as number of successful sales. With those data insights, you can better allocate workers, assign workers based on strengths, stock items based on buying trends, and more. Challenges when building a retail worker app An always-connected and innovative retail workforce enablement app sounds great, but building this kind of intuitive app from the ground up presents a lot of challenges for already strained IT teams. Many retailers still rely heavily on relational databases that require additional support from a sprawl of supporting databases and technologies. As shown in this typical retail tech stack, legacy architectures are often made up of specialist NoSQL and relational databases, and additional mobile data and analytics platforms — all resulting in siloed data, slow data processing, and unnecessary complexity. This “spaghetti” architecture has several drawbacks when it comes to building a mobile app that truly empowers developers. The data from all these systems ends up siloed, requiring time-consuming ETL maneuvers to bring it together into a single view. Real-time access to data and insights, required to know what’s out of stock, who made a purchase for pickup, and more becomes harder to orchestrate. It’s hard to ensure data synchronization between a worker’s app and the backend database when they’re moving in and out of connectivity (when workers walk to the back of a warehouse or stockroom, for instance). It’s even harder with a sprawling data architecture to account for. The added complexity managing multiple databases, analytics suites, and the connections between them slows down your development teams, burdening them with additional complexity and maintenance issues to manage. As a result, IT teams will spend more time managing data silos and supporting old systems and applications than enabling mobile platforms to support new applications and empower frontline staff. To learn more about these issues — and overcome them — read our latest whitepaper, Why It’s So Hard for Retailers to Build a Workforce Enablement App (and How to Do It Right) .

March 10, 2022
Next →

The Converged AI and Application Datastore for Insurance

In the inherently information-driven insurance industry, companies ingest, analyze, and process massive amounts of data, requiring extensive decision-making. To manage this, they rely on a myriad of technologies and IT support staff to keep operations running smoothly but often lack effectiveness due to their outdated nature. Artificial intelligence (AI) holds great promise for insurers by streamlining processes, enhancing decision-making, and improving customer experiences with significantly less time, resources, and staff compared with traditional IT systems. The convergence of AI and innovative application datastores is transforming how insurers work with data. In this post, we’ll look at how these elements are reshaping the insurance industry and offering greater potential for AI-powered applications, with MongoDB at the heart of the converged AI and application datastore. Scenario planning and flexible data layers One of the primary concerns for IT leaders and decision-makers in the insurance industry is making smart technology investments. The goal is to consolidate existing technology portfolios, which often include a variety of systems like SQL Server, Oracle, and IBM IMS. Consolidation helps reduce inventory and prepare for the future. But what does future-proofing really look like? Scenario planning is an effective strategy for future-proofing. This involves imagining different plausible futures and investing in the common elements that remain beneficial across all scenarios. For insurance companies, a crucial common thread is the data layer. By making data easier to work with, companies can ensure that their technology investments remain valuable regardless of how future scenarios unfold. MongoDB’s flexible developer data platform offers a distinct architectural advantage by making data easier to work with, regardless of the cloud vendor or AI application in use. This flexibility is vital for preparing for disruptive future scenarios, whether they involve regulatory changes, market shifts, or technological advancements. Watch now: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance The role of AI and data in insurance Generative AI is revolutionizing the insurance sector, offering new ways to manage and utilize data. According to Celent's 2023 Technology Insight and Strategy Survey, 33% of companies across different industries have AI projects in planning, 29% in development, and 19% in production (shown in Figure 1 below). This indicates a significant shift towards AI-driven solutions by insurers actively experimenting with gen AI. Figure 1: Celent Technology Insight and Strategy Survey 2023 However, there's tension between maintaining existing enterprise systems and innovating with AI. Insurance companies must balance keeping the lights on with investing in AI to meet the expectations of boards and stakeholders. The solution lies in integrating AI in a way that enhances operational efficiency without overwhelming existing systems. However, data challenges need to be addressed to achieve this, specifically around access to data. According to a Workday Global Survey , only 4% of respondents said their data is fully accessible, and 59% say their enterprise data is somewhat or completely siloed. Without a solid data foundation, insurers will struggle to achieve the benefits they are looking for from AI. Data architectures and unstructured data When adopting advanced technologies like AI and ML, which require data as the foundation, organizations often grapple with the challenge of integrating these innovations into legacy systems due to their inflexibility and resistance to modification. A robust data architecture is essential for future-proofing and consolidating technology investments. Insurance companies often deal with a vast amount of unstructured data, such as claim images and videos, which can be challenging to manage. By leveraging AI, specifically through vector search and large language models, companies can efficiently process and analyze this data. MongoDB is ideal for managing unstructured data due to its flexible, JSON-like document model, which accommodates a wide variety of data types and structures without requiring a predefined schema. Additionally, MongoDB’s flexibility enables insurers to integrate seamlessly with various technologies, making it a versatile and powerful solution for unstructured data management. For example, consider an insurance adjuster assessing damage from claim photos. Traditionally, this would require manually reviewing each image. With AI, the photos can be converted into vector embeddings and matched against a database of similar claims, drastically speeding up the process. This not only improves efficiency but also enhances the accuracy of assessments. The converged AI and application datastore with MongoDB Building a single view of data across various systems is a game-changer for the insurance industry. Data warehouses and data lakes have long provided single views of customer and claim data, but they often rely on historical data, which may be outdated. The next step is integrating real-time data with these views to make them more dynamic and actionable. A versatile database platform plays a crucial role in this integration. By consolidating data into a single, easily accessible view, insurance companies can ensure that various personas, from underwriters to data scientists, can interact with the data effectively. This integration allows for more responsive and informed decision-making, which is crucial for staying competitive in a rapidly evolving market. This can be achieved with a converged AI and application datastore, as shown in Figure 2 below. This is where operational data, analytics insights, and unstructured data become operationally ready for the applications that leverage AI. Figure 2: Converged AI and application datastore reference architecture The convergence of AI, data, and application datastores is reshaping the insurance industry. By making smart technology investments, leveraging AI to manage unstructured data, and building robust data architectures, insurance companies can future-proof their operations and embrace innovation. A versatile and flexible data platform provides the foundation for these advancements, enabling companies to make their data more accessible, actionable, and valuable. The MongoDB Atlas developer data platform puts powerful AI and analytics capabilities directly in the hands of developers and offers the capabilities to enrich applications by consolidating, ingesting, and acting on any data type instantly. Because MongoDB serves as the operational data store (ODS)—with its flexible document model—insurers can efficiently handle large volumes of data in real-time. By integrating MongoDB with AI/ML platforms, insurers can develop models trained on the most accurate and up-to-date data, thereby addressing the critical need for adaptability and agility in the face of evolving technologies. With built-in security controls across all data, whether managed in a customer environment or through MongoDB Atlas, a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies. If you would like to learn more about the convergence of AI and application datastores, visit the following resources: Video: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance Paper: Innovation in Insurance with Artificial Intelligence The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects

July 18, 2024