Introducing Multi-Cloud Clusters on MongoDB Atlas

Sahir Azam
October 20, 2020 | Updated: April 26, 2021
#MongoDB Atlas

One of the core pillars of MongoDB is the freedom to run anywhere. Since 2017, organizations have been able to use MongoDB Atlas, our fully managed global cloud database, across 70+ regions on the cloud provider of their choice: AWS, Azure, or Google Cloud. We’re increasingly seeing customers run independent workloads on different clouds — a common practice among enterprises with different applications and business units. However, we believe the real power of multi-cloud applications is yet to be realized in our industry.

So today, I’m proud to announce that multi-cloud clusters are generally available on MongoDB Atlas! With this groundbreaking capability, customers can distribute their data in a single cluster across multiple public clouds simultaneously, or move workloads seamlessly between them. Data — traditionally the hardest piece of an application stack to move — is now the easiest.

A New Multi-Cloud Paradigm

More organizations are moving towards a multi-cloud model, and they want the freedom and flexibility to use the best of each cloud provider for any and every application. The question is how engineering teams can do this efficiently and deliberately while dealing with challenges such as incompatible operations and the effects of data gravity.

Read our eBook, Why the World is Going Multi-Cloud, for a high-level guide to today's fast-emerging cloud architecture.

With multi-cloud clusters on MongoDB Atlas, customers can realize the benefits of a multi-cloud strategy with true data portability and a simplified management experience. Developers no longer have to deal with manual data replication, and businesses can focus their technical resources on building differentiated software.

This opens up a whole new set of possibilities that were previously difficult ― if not impossible ― to achieve, from being able to use best-of-breed services across multiple platforms to data mobility and cross-cloud resiliency.

MongoDB Atlas gives customers true data mobility with multi-cloud clusters.

Use best-in-class technology across multiple clouds in parallel

Developer productivity is critical to a company’s success, and CTOs know that enabling their teams to choose the best technology available is a major contributing factor. With MongoDB Atlas, developers get more freedom in deciding what building blocks to use, regardless of which cloud is storing application data.

Some examples of popular cloud services that our customers like to use include AWS Lambda, Google Cloud AI Platform, and Azure Cognitive Services. With multi-cloud clusters, developers can now run operational and analytical workloads using different cloud tools on the same dataset, with no manual data replication required.

Migrate workloads across cloud environments seamlessly

Data mobility is another reason companies want a multi-cloud strategy. The world is constantly changing, and businesses never know if, or how, their cloud requirements are going to change. They may face mergers and acquisitions, be subject to new regulatory controls for data portability, find themselves in direct competition with a cloud provider, or find significant cost savings on another platform.

With MongoDB Atlas, organizations can future-proof their applications and have the option to move them from one cloud to another if needed, without undergoing a costly data migration. Our built-in automation seamlessly handles cross-cloud data replication on a rolling basis so applications stay online and available to end-users.

Seamlessly migrate data from one cloud to another with multi-cloud clusters on MongoDB Atlas.

Improve high availability with cross-cloud redundancy

Any business with a mission-critical or user-facing application knows that downtime is unacceptable. Cloud disruptions vary in severity, from temporary capacity constraints to full-blown outages, and organizations need to mitigate as much risk as possible. By distributing data across multiple clouds, they can improve high availability and application resiliency without sacrificing latency. MongoDB Atlas extends the number of locations available by allowing users to choose from any of the nearly 80 regions available (with more coming) across AWS, Azure, and Google Cloud — the widest selection of any cloud database on the market.

This is particularly relevant for businesses that must comply with data sovereignty requirements, but have limited deployment options due to sparse regional coverage on their primary cloud provider. In some cases, only one in-country region is available, leaving users especially vulnerable to disruptions in cloud service. For example, AWS and Google Cloud each offer only one region in Canada. With multi-cloud clusters, organizations can take advantage of both regions, and add additional nodes in the Azure Toronto and Quebec City regions for extra fault tolerance. With MongoDB Atlas, customers no longer need to make a trade-off between availability and compliance.

Reach more users with flexible deployment options

In order to deliver a world-class application experience, organizations must at a minimum meet end-user requirements for their products and services. For SaaS providers and B2C businesses, this may include cloud provider preferences or regional availability.

While each of the cloud providers offer a large and growing list of regions globally, their data centers are still heavily concentrated in the USA, Europe, and eastern Asia. If multinational enterprises want to reach local users in other areas, they may not always find coverage on a single cloud. For example, AWS is the only provider to offer a cloud region in Bahrain, Azure Oslo is the only option in Norway, and only Google Cloud has data centers in Indonesia. To capture more global market share, companies may need a multi-cloud strategy to meet customers where they are.

Expand your reach to more regions with multi-cloud clusters on MongoDB Atlas.

An Integrated, More Secure Cloud Data Platform

MongoDB has consistently delivered innovations in the data management experience, including automated data tiering with Atlas Online Archive, integrated full-text search with Atlas Search, and Client-Side Field Level Encryption (FLE) for some of the strongest levels of data privacy available today.

Client-Side FLE currently works with AWS Key Management Service (KMS), and will soon offer beta support for Azure Key Vault and Google Cloud KMS. With this expansion, it will be easier for organizations to further enhance the privacy and security of sensitive and regulated workloads across all major public cloud platforms.

Read our guide to learn more about how Client-Side Field Level Encryption protects data in MongoDB.

Multi-Cloud Data Management Made Easy

Multi-cloud distribution can be enabled for both new and existing clusters starting today via the Atlas UI. Multi-cloud clusters come with all the features that our customers know and love, including built-in security defaults, fully managed backup and restores, automated patches and upgrades, intelligent performance advice, and more. While multi-cloud clusters are generally available, we are planning on releasing more capabilities in the coming months to deliver even more value to you.

Whether you’re a startup just getting off the ground or a global enterprise in the midst of a multi-year cloud transformation initiative, our multi-cloud database solution abstracts away the toughest roadblock to unlocking your multi-cloud strategy.

When your data can travel across clouds, there’s no limit to what you can build. Let us know where multi-cloud clusters on MongoDB Atlas take you - or tell us what you need to get there.

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Using MongoDB CLI to Get Atlas Performance Advice From Your Terminal

MongoDB CLI brings the power of your MongoDB cloud (MongoDB Atlas, MongoDB Cloud Manager, and MongoDB Ops Manager) to your terminal, helping you write advanced scripts and automate different workflows for your MongoDB cloud infrastructure. We released MongoDB CLI's first stable version back in June. So far we've seen more than 100 clusters deployed with the tool, as well as with many other operations such as getting metrics for these clusters or managing backups. Meanwhile, we've been busy adding new features and listening to your feedback as to what we should add next. With the latest release of mongocli , we have added the ability to get Atlas performance recommendations. Let's see how they work. $ mongocli atlas clusters create slowQueriesDemo \ --region EASTERN_US \ --members 3 \ --tier M30 \ --provider GCP \ --mdbVersion 4.4 \ --diskSizeGB 30 This command deploys a three-member replica named slowQueriesDemo, with GCP as the backing cloud provider. You can always use mongocli atlas clusters create --help to review all available options when creating a cluster. Getting your new cluster ready can take some time. Use mongocli to be notified when your changes are available with a watch : $ mongocli atlas clusters watch slowQueriesDemo Performance Advisor works by scanning your Atlas Cluster logs and finding any slow operations that could be affecting the performance of your queries. For this example, I loaded my demo Atlas cluster with some dummy data. I executed a series on MongoDB commands where I expect to see the Performance Advisor recommend new indexes to improve my queries. If you're not seeing any suggestions, please check our docs . The performance advisor command requires a process identifier. To check for the processes available to our project: $ mongocli atlas processes ls ID REPLICA SET NAME SHARD NAME VERSION atlas-00-00.gcp.mongodb.net:27017 atlas-fjszq1-shard-0 4.4.1 atlas-00-01.gcp.mongodb.net:27017 atlas-fjszq1-shard-0 4.4.1 atlas-00-02.gcp.mongodb.net:27017 atlas-fjszq1-shard-0 4.4.1 We want to pick one process ID related to our cluster, and then use it with the performance advisor: $ mongocli atlas performanceAdvisor suggestedIndexes ls --processName atlas-00-01.gcp.mongodb.net:27017 ID NAMESPACE SUGGESTED INDEX 5f75c6d38788f14f816f80ee loadTest.saildrone { timeUTC: 1 } 5f75c6d38788f14f816f80ef loadTest.saildrone { id: 1, arrayOperation: 1, decimal: 1, nested.field: 1, quotedReservedWordField: 1, date: 1, binary: 1, embeddedArrayDocument: 1, embeddedDocument: 1, embeddedArray: 1, embeddedDocumentArray: 1, nullValue: 1, coordinates.latitude: 1 } 5f75c6d38788f14f816f80f0 loadTest.saildrone { RH_MEAN: 1 } When running this command, you'll get all recommended indexes for the given process, along with the related namespace. You can use this information to make a decision on creating new indexes that could improve different operations over your database. To automate this process, we can write a small script to create all recommended indexes. This script will leverage the existing mongocli command to create rolling indexes. Then we’ll rerun our index recommendations and transform that into arguments to the create index command.: mongocli atlas performanceAdvisor suggestedIndexes ls \ --processName atlas-00-01.gcp.mongodb.net:27017 \ | awk 'BEGIN{ORS=" ";}{if (NR!=1){split($2,a,"."); print a[1]; print a[2]; for(i=4;i<=NF-1;++i)printf $i} printf"\n"}' \ | xargs -n3 sh -c 'echo "Creating index db: $1 collection: $2 index: $3"; mongocli atlas clusters indexes create --clusterName slowQueriesDemo --db $1 --collection $2 --key $3' sh This more advanced example shows how to combine or pipe different commands and automate different workflows, in this case saying goodbye to any slow queries. This is just one of the many things you can do with mongocli. We are constantly adding new features, so let us know what you would like to see next . Ready to see for yourself? Try MongoDB Atlas today! Sign up now Get MongoDB CLI! Download

October 19, 2020
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MongoDB and Partners: Building the AI Future, Together

If you’re like me, over the past year you’ve closely watched AI’s developments—and the world’s reactions to them. From infectious excitement about AI’s capabilities, to impatience with its cost and return on investment, every day has been filled with AI twists and turns. It’s been quite the roller coaster. During the ride, from time to time I’ve wondered where AI falls on the Gartner hype cycle, which gives "a view of how a technology or application will evolve over time." Have we hit the "peak of inflated expectations" only to fall into the "trough of disillusionment?" Or is the hype cycle an imperfect guide, as The Economist argues? The reality is that it takes time for any new technology—even transformative ones like AI—to take hold. And every advance, no matter how big, has had its detractors. A famous example is that of Picasso (!), who in 1968 said, “Computers are useless. They can only give you answers.” (!!) For our part, MongoDB is convinced that AI is a once-in-a-generation technology that will enhance every future application—a belief that has been reinforced by the incredible work our partners have shared at MongoDB’s 2024 events. Speeding AI development MongoDB is committed to helping organizations of all sizes succeed with AI, and one way we’re doing that is by collaborating with the MongoDB partner ecosystem to create powerful, user-friendly AI development tools and solutions. For example, Fireworks.ai —which is a member of the MongoDB AI Applications Program ecosystem —created an inference solution that hosts gen AI models and supports containerized deployments. This tool makes it easier for developers to build and deploy powerful applications with a range of easy-to-use tools and customization options. They can choose to use state-of-the-art, open-source language, image, and multimodal foundation models off the shelf, or they can customize and fine-tune models to their needs. Jointly, Fireworks.ai and MongoDB provide a solution for developers who want to leverage highly curated and optimized open-source models and combine these with their organization’s own proprietary data—and to do so with unparalleled speed and security. “MongoDB is one of the most sophisticated database providers, and it’s very easy to use,” said Benny Chen , cofounder of Fireworks.ai. "We want developers to be able to use these tools, and we want to work with providers who enable and empower developers." Nomic , another MAAP ecosystem member, also enables developers with best-in-class solutions across the entire unstructured data workflow. Their Embed offering, available through the Nomic API , allows users to vectorize large-scale datasets for use in text, image, and multimodal retrieval applications, including retrieval-augmented generation (RAG), using only their web browser. The Nomic-MongoDB solution is a highly efficient, open-weight model that developers can use to visualize the unstructured datasets they store in MongoDB Atlas . These insights help users quickly discover trends and articulate data-driven value propositions. Nomic also supported the recently announced vector quantization in MongoDB Atlas Vector Search , which reduces vector sizes while preserving performance. Last—but hardly least!—there’s our new reference architecture with MAAP partners AWS and Anthropic. Announced at MongoDB.local London , the reference architecture supports building memory-enhanced AI agents, and is designed to streamline complex processes and develop smarter, more responsive applications. For more—including a link to the code on Github— check out the MongoDB Developer Center . Making AI work for anyone and everyone The companies MongoDB partners with aren’t just making gen AI easier for developers—they’re building tools for everyone. For example, Capgemini has invested $2 billion in gen AI and is training 100,000 of its employees in the technology. GenYoda, a solution that helps insurance professionals with their daily work, is a product of this investment. GenYoda leverages MongoDB Atlas Vector Search to analyze large amounts of customer data, like policy statements, premiums, claims history, and health information. Using GenYoda, insurance professionals can quickly analyze underwriters’ reports to make informed decisions, create longitudinal health summaries, and streamline customer interactions to improve contact center efficiency. GenYoda can ingest 100,000 documents in just a few hours and respond to users’ queries in two to three seconds—a metric on par with the most widely used gen AI models. And it produces results: in one example, by using Capgemini’s solution an insurer was able to increase productivity by 15%, add new reports 25% faster (thus speeding decision-making), and reduce the manual effort of searching PDFs, increasing efficiency by 10%. Building the future of AI together So, what’s next? Honestly, I’m as curious as you are. But I’m also incredibly excited. At MongoDB, we’re active participants in the AI revolution, working to embrace the possibilities that lie ahead. The future of gen AI is bright, and I can’t wait to see what we’ll build together. To learn more about how MongoDB can accelerate your AI journey, explore the MongoDB AI Applications Program .

November 4, 2024