Fine-Tune Relevance in MongoDB Atlas Search with Function Scoring and Synonyms
MongoDB Atlas Search is an embedded full-text search solution in MongoDB Atlas that gives developers a seamless and scalable experience for building fast, relevance-based application features. We announced its general availability last year at MongoDB.live 2020 and over the past year we’ve introduced many new features, including a visual index builder, search query tester, custom analyzers , and wildcard path queries . This year at MongoDB.live 2021 , we’re excited to highlight two new capabilities that help developers tune the relevance of search results. See how easy it is to get started with MongoDB Atlas Search in this demo video by Marcus Eagan, Senior Product Manager for Atlas Search. Building relevance into search results Understanding the behavior of your users is essential when thinking about search result relevance. People don’t always tell you what they want, and they sometimes use words or phrases that don’t match your content exactly. To cover these scenarios, you can use full-text search features like function scoring and synonyms. Influence search rankings with function scoring There are often multiple factors that influence how search results should be ranked. For example, let’s say you have a restaurant finder application. The explicit inputs are things like the user’s location and what they’re searching for, but what’s implied is that they likely want to see highly rated restaurants or ones with more reviews. What’s Cooking: a sample restaurant finder application using MongoDB Atlas Search Function scoring allows you to influence the order of results returned by manipulating the score of each result. In Atlas Search, that means you can use a numeric field in a document and apply a mathematical expression to it. For example, you might want to increase the score of restaurants that are sponsored or have higher star ratings. This can easily be accomplished within the same search query by simply adding the function option to the score parameter of your query. Learn more about how to use function scores in our developer tutorial . Show results for more search queries with synonyms Synonyms are often used to define terms that are semantically similar to each other to improve search results. For example, someone searching for “noodles” might want to find results for “spaghetti”, “chow mein”, or “pad thai”. Synonyms can also help with typos, especially on mobile and small keyboards. In Atlas Search, you can define collections of synonyms for a search index via the API. Synonyms can be explicit (one-way) or equivalent (two-way). Explicit synonyms are good for defining relationships between terms that are subsets of each other, like the noodle example above: “spaghetti”, “chow mein”, and “pad thai” are all explicit synonyms for “noodles”, but not each other (you don’t want results for “chow mein” in a search for “spaghetti”). Equivalent synonyms are often used for terms that have regional variations or are otherwise interchangeable both ways, like soda and pop, or Kleenex and tissues. What's next for Atlas Search Developers are increasingly turning to full-text search to make content more discoverable and relevant for application end users. With Atlas Search, we hope to not only make building full-text search easier, but also more powerful and expressive. Join our community to ask questions and find out what other developers are building with Atlas Search and let us know what you think we should build next in our feedback forums .
Introducing Serverless Instances on MongoDB Atlas, Now Available in Preview
Since we first launched MongoDB Atlas in June 2016, we’ve been working towards building a cloud database that not only delivers a first-class developer experience, but also simply just works: no setup, tuning, or maintenance required. Over the years, this has led to features like auto-scaling and click-to-create index suggestions , along with numerous optimizations to our automation engine. We’re excited to announce that we’re one more step closer to realizing this vision with the introduction of serverless databases on MongoDB Atlas . Think less about your database, and more about your data Serverless computing and NoOps have emerged as popular trends in modern application development. Cloud functions are commonly used to power business logic in applications, and many teams rely on completely automated IT operations. The appeal of serverless technology is hard to deny: elastic scaling eliminates the need for upfront resource provisioning and ongoing maintenance, and consumption-based pricing means paying only for resources that are used. It abstracts and automates away many of the lower-level infrastructure decisions that developers don’t want to have to learn or manage so they can focus on building differentiated features. When it comes to databases, compute and storage resources have traditionally been tightly coupled. Applying a serverless model to databases means decoupling them and changing the way engineering teams think about infrastructure. Rather than asking a developer to predict an application’s future workload patterns, break them down into individual resource requirements, and then map them to arbitrary units of database instance sizes, serverless databases offer a much simpler experience: define where your data lives, and get a database endpoint you can use. This not only streamlines the database deployment process, it also eliminates the need to monitor and adjust capacity on an ongoing basis. Developers are free to focus on thinking about their data rather than their databases, and leave the lower-level infrastructure decisions to intelligent, behind-the-scenes automation. Serverless instances on MongoDB Atlas All customers now have the ability to create a serverless database on MongoDB Atlas with the introduction of serverless instances , announced at MongoDB.live 2021 . It’s incredibly easy to get started: simply choose a cloud region and you’ll receive an on-demand database endpoint for your application. Serverless instances always run on the latest MongoDB version so you never have to worry about backwards compatibility or upgrades. You can view and manage them using the same UI and API as your existing database deployment on Atlas (i.e., clusters), and they come with end-to-end security, continuous uptime, metrics, alerts, and backups. Watch this demo of how to create a serverless instance on MongoDB Atlas This new deployment type will be available in preview, so it doesn’t yet support all of the features and capabilities available on clusters today. It’s ideal for infrequent or sparse workloads, or development and testing workloads in the cloud. If you’re running a high-throughput production workload, dedicated clusters are still the recommended deployment option. A hands-free database experience This is the first of many releases, and we have an ambitious roadmap ahead. We will continue to invest in making working with data ever more seamless and delightful for developers, from adding support for newer Atlas capabilities like full-text search and native visualizations , to even more intelligent automation and optimization. Create your own serverless instance on MongoDB Atlas. Try the Preview If you have feedback or questions, we’d love to hear them! Join our community forums to meet other MongoDB developers and see what they’re building with serverless instances. What's next for MongoDB Atlas Serverless instances are just one of many new additions to Atlas that we hope will make developers’ lives easier. Earlier this year, we added index removal suggestions to Performance Advisor and released a quick start for creating and managing clusters via the command line with the MongoDB CLI . We are also working on integrations with Vercel and Netlify , two popular serverless application platforms, to give developers an easy way to get started on MongoDB Atlas. What would make your development experience better on MongoDB Atlas? Share your feature requests in our feedback forums .
Deploy and Manage MongoDB Atlas from AWS CloudFormation
As a premier launch partner for the recent GA announcement of the AWS CloudFormation Public Registry , we’re delighted to share that you can now deploy and manage MongoDB Atlas directly from your AWS environment. Amazon and MongoDB have been pioneers in the cloud computing space, providing mission critical systems for over a decade. Before MongoDB Atlas was launched in June 2016, tens of thousands of customers were running MongoDB themselves on AWS EC2 instances, and many of them were originally spun up using the legacy MongoDB on the AWS Cloud: Quick Start Reference Deployment. This Quick Start was among the top five most popular guides for AWS and allows users to take advantage of AWS CloudFormation 's seamless automation and MongoDB’s flexible data model and expressive query API. In April 2021, we launched a new AWS Quick Start for MongoDB Atlas , which allows AWS customers to quickly and easily launch a basic MongoDB Atlas deployment from the AWS CLI or console. Now, with the availability of the MongoDB Atlas resource types on the CloudFormation Public Registry, customers have more flexibility over their deployment configurations to better meet their cloud workflows. Let’s walk through how it works. Setup your AWS account for MongoDB Atlas CloudFormation Support The first step is to sign up for MongoDB Atlas , if you haven’t done so already. Once you create your account, follow these steps: Skip the cluster deployment options Go to Billing and add a credit card to your account Create an organization-level MongoDB Atlas Programmatic API Key with an IP Access List entry. The key needs Organization Project Creator permissions. Next, open the AWS console in your browser and navigate to CloudFormation. On the left-side navigation, select the Public extensions option. From there you will be able to find the MongoDB Atlas resource types by selecting the “Resource Types” and “Third Party” options. For each of the MongoDB::Atlas resource types, click “Activate”, and then follow on screen prompts to complete the process. Once you have activated the MongoDB Atlas resources in a region, you’re ready to launch apps with MongoDB Atlas directly from your AWS control plane. Build apps faster with Cloud Automation Context switching is a hassle for developers. Launching and deploying application stacks with MongoDB Atlas directly from the AWS console is now more seamless than ever. Whether you use the AWS Quick Start deployment guide as a template or create your own MongoDB Atlas CloudFormation templates, you can leverage the latest in cloud automation to reduce the pain of infrastructure provisioning and management. Try out the new MongoDB Atlas CloudFormation Resources today, and stay tuned for an in depth look at building apps with AWS Lambda and SAM CLI in an upcoming DevHub article!
Global, Multi-Cloud Security at Scale with MongoDB Atlas
In October 2020, we announced the general availability of multi-cloud clusters on MongoDB Atlas . Since then, we’ve made several key improvements that allow customers to take advantage of the full breadth of MongoDB Atlas ’ best-in-class data security and privacy capabilities across clouds on a global scale. Cross-Cloud Security with MongoDB Atlas A common question we get from customers about multi-cloud clusters is how security works. Each cloud provider offers protocols and controls to ensure that data within its ecosystem is securely stored and accessed. But what happens when your data is distributed across different clouds? Don’t worry–we have you covered. MongoDB Atlas is designed to ensure that our built-in best practices are enforced regardless of which cloud providers you choose to use, from dedicated network peering connections to customer-managed keys for data encryption-at-rest and client-side field-level encryption. Private Networking to Multiple Clouds You can now create multiple network peering connections and/or private endpoints for a multi-cloud cluster to access data securely within each cloud provider. For example, say your operational workload runs on Azure, but you want to set up analytics nodes in Google Cloud and AWS so you can compare the performance of Datalab and SageMaker for machine learning. You can set up network peering connections for all three cloud providers in Atlas to allow each of your cloud environments to access cluster data in their respective nodes using private networks. For more details, take a look at our documentation on network peering architecture . Integrate with Cloud KMS for Additional Control Over Encryption Any data stored in Atlas can be encrypted with an external key from AWS KMS, Google Cloud KMS, or Azure Key Vault for an extra layer of encryption on top of MongoDB’s built-in encrypted storage engine . You can also configure client-side field level encryption (client-side FLE) with any of the three cloud key management services to further protect sensitive data by encrypting document fields before it even leaves your application ( support for Azure Key Vault and Google Cloud KMS is available in beta with select drivers ). This means data remains encrypted even while it is in memory and in-use within your live database. Even though the data is encrypted, it remains queryable by the application but is inaccessible to any administrators running the database or underlying cloud infrastructure for you. Beyond security, client-side FLE is also a great way to comply with right to erasure requests that are part of modern privacy regulations such as the GDPR or the CCPA. You simply destroy the user’s encryption key and their PII is unreadable and irrecoverable in memory, on disk, in logs, and in backups. For multi-cloud clusters, this means you can take advantage of multiple layers of encryption that use keys from different clouds. For example, you can have PII data encrypted client-side with AWS KMS keys, then stored in both an AWS and Google Cloud region on Atlas and further encrypted at rest with a key managed via Azure Key Vault. Global, Multi-Cloud Clusters on MongoDB Atlas For workloads that reach users across continents, our customers leverage Global Clusters . This gives you the unique ability to shard clusters across geographic zones and pin documents to a specific zone. Now that Atlas is multi-cloud, you can now choose from the nearly 80 available regions across all three providers, expanding the potential reach of your client applications while making it easy to comply with data residency regulations. Consider a sample scenario where you’re based in the US and want to expand to reach audiences in Europe. To comply with GPDR , you must store EU customer data within that region. With Global Clusters, you can configure a multi-cloud cluster with a US zone and an EU zone. In the US, you choose to run on AWS, but in Europe, you decide to go with Azure because it has more available regions. All of this can be configured in minutes using the Atlas UI: simply define your zones and ensure that your documents contain a location field that dictates which zone they should be stored in. For more details, follow our tutorial for how to configure a multi-cloud Global Cluster on Atlas . Future-Proof Your Applications with Multi-Cloud Clusters There are many reasons why companies are considering a multi-cloud strategy , from cross-cloud resiliency to geographical reach to being able to leverage the latest tools and services on the market. With MongoDB Atlas, you get best-in-class data security and operations and intuitive admin controls, regardless of how many cloud providers you want to use. To learn more about how to deploy a multi-cloud cluster on MongoDB Atlas, check out our step-by-step tutorial , which includes best practices for node distribution, instructions for how to test failing over to another cloud, and more. Safe Harbor The development, release, and timing of any features or functionality described for our products remains at our sole discretion. This information is merely intended to outline our general product direction and it should not be relied on in making a purchasing decision nor is this a commitment, promise or legal obligation to deliver any material, code, or functionality.
Splitit & MongoDB Atlas: Racing to Capture a Global Opportunity
Splitit is a global payment solution that allows businesses to offer installment plans for their customers. Unlike with other buy now, pay later (BNPL) solutions, Splitit shoppers can split their online purchases into monthly installments by using their existing credit, without the need for registration, application, or approval. “We have a very different proposition than others in this space,” says Splitit’s CTO, Ran Landau. “We’re not a financing company. We utilize the customer’s existing credit card arrangement, which allows us to accommodate smaller average deal values and a broader range of installment schedules.” Splitit works with online retailers across all market sectors and diverse price points, and recently raised $71.5 million in investment to fund global expansion. Following its IPO in January 2019, the business had seen strong growth as more consumers moved from brick and mortar to ecommerce. Then COVID-19 hit, and online shopping boomed. Landau recognized that the company needed to quickly scale its infrastructure in order to capture this large opportunity. The Need for Speed Landau joined Splitit in May 2019 and worked to modernize the company’s infrastructure. At the time, the team was using a traditional relational database. “As tech leaders, we need to make the right decision,” he says. “When I came to Splitit, I knew I needed a powerful NoSQL server so that my developers could develop faster and so that we could scale – both things that our relational databases were failing to deliver.” In the interest of getting up and running quickly, Ran’s team thought that they could move faster using a cloud-provider database that mimicked MongoDB functionality. He had used MongoDB before and saw that this solution offered the same drivers he was familiar with and claimed compatibility with MongoDB 3.6. Initially, the new solution seemed fine. But as the team started to migrate more data into the database, however, Landau noticed a few missing features. Scripts for moving documents from one collection to another were failing, and overall performance was deteriorating. The application became slow and unresponsive even though the load on the database was normal. “We were having issues with small things, like renaming collections. I couldn’t search or navigate through documents easily,” recalls Landau. Offline Database: A Breaking Point Then one day, the application was unable to communicate with the database for 20 minutes, and when the database finally came back online, something wasn’t right. Landau contacted support, but the experience was not very helpful. “We were not pleased with the response from the database vendor,” he explains. “They insisted that the issue was on our side. It wasn’t so collaborative.” Fortunately, he had taken a snapshot of the data so Splitit was able to revert back to an earlier point in time. But the incident was troubling. Other teams also had been complaining about how difficult it was to debug problems and connect to the database successfully. Landau knew he needed to find a better solution as soon as possible. MongoDB Atlas: A Reliable, Scalable Solution Landau believed that MongoDB was still the right choice for Splitit, and investigated whether the company offered a cloud solution. He discovered MongoDB Atlas and decided to give it a try. “The migration to MongoDB Atlas was so simple. I exported whatever data I had, then imported it into the new cluster. I changed the connection strings and set up VPC peering in all of my environments,” says Landau. “It was incredibly easy.” Not only was MongoDB Atlas built on actual MongoDB database software, but it was also secure, easy to use, and offered valuable features such as Performance Advisor . “It can tell you which indexes need to be built to increase speed. It’s such a powerful tool — you don’t need to think; it analyzes everything for you,” explains Landau. Another great feature was auto-scaling. “My biggest concern as I scale is that things keep working. I don’t have to stop, evaluate, and maintain the components in my system,” says Landau. “If we go back to doing database operations, we can’t build new features to grow the business.” Auto-archival Made Easy with Online Archive As a business in the financial services industry, Splitit needs to comply with various regulations, including PCI DSS . A key requirement is logging every transaction and storing it for auditing purposes. For Splitit, that adds up to millions of logs per day. Landau knew that storing this data in the operational database was not a cost-effective, long-term solution, so he initially used an AWS Lambda function to move batches of logs older than 30 days from one collection to another periodically. A few months ago, he discovered Online Archive , a new feature released at MongoDB.live in June 2020. With it, Landau was able to define a simple rule for archiving data from a cluster into a more cost-effective storage layer and let Atlas automatically handle the data movement. “The gem of our transition to Atlas was finding Online Archive,” says Landau. “There’s no scripting involved and I don’t have to worry about my aging data. I can store years of logs and know that it’s always available if I need it.” Online Archive gives me the flexibility to store all of my data without incurring high costs, and feel safe that I won't lose it. It's the perfect solution. Ran Landau, CTO, Splitit With federated queries, the team can also easily analyze the data stored in both the cluster and the Online Archive for a variety of use cases. Ready for Hypergrowth and Beyond Looking back, Landau admits that he learned his lesson. In trying to move quickly, he selected a solution that appeared to work like MongoDB, but ultimately paid the price in reliability, features, and scalability. You wouldn't buy a fake shirt. You wouldn't buy fake shoes. Why buy a fake database? MongoDB Atlas is the real thing. Ran Landau, CTO, Splitit Landau is confident that his investment in MongoDB puts in place a core building block for the business’ continued success. With a fully managed solution, his team can focus on building features that differentiate Splitit from competitors to capture more of the market. “We saw our growth triple in March due to COVID-19, but the sector as a whole is expanding,” he says. “Our technology is patent protected. Everything we build moving forward will be on MongoDB. As a company that’s scaling rapidly, the most important thing is not having to worry about my scaling. MongoDB Atlas takes care of everything.”
MongoDB Atlas Powers Half a Billion Players of India's Favorite Mobile Pastime, Ludo King
Nothing is more human than playing games. Boards and pieces can be found from the beginnings of civilization — little scraps of technology we created to entertain ourselves. No wonder, then, that gaming is a dominant force in mobile tech. What's more surprising is that some of the most successful mobile games are versions of some of the oldest traditions. Take Ludo. A classic board game for up to four players, it can trace its direct ancestry to 6th-century India and is built from much older ideas. Players roll a die to move pieces from home along a track to a finish; the first to get all pieces there wins. You can't pass an opponent on the track, but if you land on them they go back to the start. That's it. Simple. But the way it brings players together has been enough to make Ludo the national game of the subcontinent. Now Ludo is king of the phones, in the shape of Gametion's Ludo King app. A faithful yet stylish rendition of the board game, it retains the game's simplicity and social interaction, but at an epic scale. It topped the charts for Google Play downloads in India and reached the top ten internationally, with tens of millions of players chalking up a quarter of a billion minutes of playing time a day. At one point, numbers quadrupled overnight. Yet all this was managed by a tiny team of developers who'd built their platform on MongoDB Atlas , the global cloud database service. Gametion Founder and CEO Vikash Jaiswal Ludo King's authentic board game emulation quickly tapped into the Indian psyche. "We had strong takeup right from 2016, when we launched the first version," says Gametion founder and CEO Vikash Jaiswal. "A million downloads in the first 25 days, and up to a million minutes of play a day by the start of 2020. We were doing very well already. Then came the lockdown and we went through the roof." "We Just Wanted to Concentrate on the Game" Gametion was the quintessential small gaming startup. In 2015, it had a couple of developers out of a staff of four or five, and they'd produced a suite of in-browser Flash games. The next move was obviously mobile. But at first, the company didn't move far from the idea of a simple gaming experience. Jaiswal says: "There was no database component to the Flash games, no login or user ID. We launched Ludo King in 2016 as a single player game, and soon got the user feedback that they wanted multiplayer features. You need user accounts and user data for that." The company takes pride in how quickly it can adopt and incorporate new technologies, explains Jaiswal, but that means finding the right technology to adopt. And the game was exhibiting demanding growth. "Ludo King was becoming very popular, so we knew we needed something that could scale. It had to be quick to learn — we didn't have time for complexity or long learning curves." MongoDB seemed a good fit for an underlying database. I knew it was fast and very flexible to build on, and it had lots of features. And it turned out to be a really good fit for mobile gaming — MongoDB integrates very well into our Node.js architecture. It's a native speaker. Vikash Jaiswal, Founder and CEO, Gametion Jaiswal's team was able to rely on MongoDB's flexible data model to continually expand the game's features, including more options for players and monetisation tactics. That's never stopped. In 2020, Gametion introduced two new in-game features: voice chat and egreetings to users. But they had no interest in the nuts and bolts of database administration. "We didn't want to make our own backend or worry about scaling, management or any of that. We just wanted to concentrate on the game," says Jaiswal. MongoDB Atlas hadn't made its debut yet at the time — Gametion being ahead of the game -- so the company chose the third-party mLab platform for hosting. Then in 2019, after mLab was acquired by MongoDB Inc, Gametion transitioned from mLab to MongoDB Atlas, the platform made and managed by the company behind the database. MongoDB Atlas: A 'Native Speaker' for Mobile Gaming Transitions can be challenging, but with the same underlying architecture and the support of MongoDB itself, this one was straightforward. In fact, it was so uneventful that Jaiswal says he can't remember it happening. "I don't recall any problems at all. There was no downtime, which I definitely would have remembered. MongoDB managed it all for us. The migration must have been very smooth." Once on MongoDB Atlas, running on AWS's cloud infrastructure, the team — which was now five developers — quickly found the features that mattered, such as Continuous Cloud Backup and Performance Advisor . "The dashboard is very cool. We can dial up the performance we need when we need it, and see exactly what's going on." Ludo King's Lockdown Gametion's emphasis on common open standards and a component approach has made it easy to add other functions as the game demands, maintaining a regular schedule of updates that keep the users engaged. "You can think of it as a microservices architecture. We use Kafka to manage data movement and synchronize between services. It's another way to optimize resource use across the board without sacrificing scalability or release cadence." Infrastructure Diagram for Ludo King That's something you need when you go from being one of the top mobile games in India to the uncontested champ. "At the start of March 2020, we had between 150,000 and 200,000 simultaneous users, but when lockdown hit that month, it jumped to a million, 1.5 million. We went from 8,000 IOPS to peaking at 35,000." "With 145 million downloads in the first week of lockdown alone, quickly finding the rights answers was important," says Jaiswal. "We have 50 million users a day, averaging 50 minutes of gameplay each. Some of them are on for five, six hours at a stretch." MongoDB is Integral to Future Growth The future will see more features on Ludo King, such as league tables and what Gametion sees as its primary revenue generator: in-app purchases. It'll also see some brand-new games. MongoDB is integral to this strategy, both to power innovation and to manage the consequences of success. And Gametion's roadmap is growing with its market, which means it will need features for economically managing huge numbers of casual users. " Atlas Data Lake looks useful," says Jaiswal. "We want to move inactive players — those who haven't been online in a while — away from the main database, but we don't want to just delete them." Efficiently managing hundreds of millions of users — and supporting near-instantaneous, 1,000% growth — would have once required the resources of a large corporation. But for Gametion, which still has fewer than 100 employees, these aren't limiting factors. In August 2020, India Prime Minister Narendra Modi even highlighted the success of the the game during his monthly radio programme. Ludo King is helping to fulfill the vision of popularising Indian games with a global audience. For now, Gametion's focus is growth. And MongoDB is part of that experience, the game piece that shows where you are and implements your strategy, quietly and efficiently. MongoDB Atlas is not just a database, it's a genuine game changer. Try MongoDB Atlas Free
Finer Grained Database User Authorization in MongoDB Atlas
We’re happy to announce that it is now possible to create database users with privileges scoped to a specific set of clusters or Atlas Data Lakes in a MongoDB Atlas project. Traditionally, database users have always been created on a project level in Atlas. This provided a centralized interface for database user management as user privileges were scoped to all clusters in your Atlas project. Customers could manage and revoke user permissions confidently, without fear that the updated permissions would be applied to one cluster but not another. However, this abstraction created limitations for use cases that required creating database users scoped to specific clusters or Data Lakes. After much anticipation, we now offer the flexibility to refine database access on a more granular level. Restrict privileges for different environments of the same application One reason for scoping database users to the resource level is to restrict privileges by environment. For example, you may have a dev cluster, a test/qa cluster, and a prod cluster in the same project, but you don’t want all users to have equal access to all three. Isolate teams working on different microservices in the same project Another scenario is to be able to split up users so that certain developers only have access to specific clusters within a project. This is especially useful for customers who have networking restrictions that require them to only use one or two Atlas projects, and therefore co-locate multiple microservices or applications within the same project. With database users per cluster, those customers can now scope team A to only accessing cluster A, and team B to cluster B. Grant other users to access Atlas Data Lakes for analytics Finally, this capability also allows admins to restrict a user’s privileges to only Atlas Data Lakes within the project. This is helpful for analysts, data engineers, and other employees who need access to those Data Lakes and not live operational data in a production cluster, for example. Finer-grained authorization for database users Today, Atlas customers can already use built-in roles or custom roles to grant privileges to database users. With this update, admins get additional flexibility in the database user authorization model while keeping the authentication model exactly the same––we made sure to maintain all of the great features that database users already have. Customers can continue to pick from any of the four authentication mechanisms (SCRAM, X.509, LDAP, AWS IAM) supported by Atlas, and choose to create temporary database users that automatically expire within a user-configurable 7-day period, which can be used in conjunction with database auditing to audit any activity performed by a user with elevated privileges. Database user settings can be modified at any point, so existing users can now be scoped to the cluster or Data Lake-level. Database users with the default authorization settings will continue to have the same access to all resources within an Atlas project. If you have feedback, please add or upvote requests to our feedback portal . Already managing users programmatically? You can create and edit database users with the Atlas API or the Terraform MongoDB Atlas Provider (use this example configuration for a head start!). Ready to try it out? Get started with MongoDB Atlas today! Sign up now