New to MongoDB Atlas: Cross-Region Replication, New Instance Sizes

Sahir Azam
October 6, 2017 | Updated: August 16, 2023

Our automated database service, MongoDB Atlas, now serves thousands of customers across a wide range of industries, providing high availability, consistent performance, and simplified operations.

Sega’s Hardlight Studio migrated to Atlas to support real-time data processing requirements in the cloud and improve the user experience for millions of mobile gamers.

MongoDB Atlas ensures high availability with a fault-tolerant and self-healing architecture. Replica set members are distributed across availability zones in a customer’s selected region; should a node fail, the election and failover process happen automatically without intervention. You can also choose from 41 regions across AWS, Azure, GCP, ensuring that your managed databases are in close proximity to your application servers for minimal geographic latency. While these deployment configurations serve a large portion of application requirements, customers have asked for more options and flexibility.

Today we are introducing cross-region replication for MongoDB Atlas. Within a single cloud, you can enable cross-region deployments for even better availability guarantees and improved performance for local reads.

Here’s how it works. When creating or modifying your deployment, you’ll now see a “Enable cross-region configurations options” link, shown below:

Selecting this link will present two non-exclusive options:

You can deploy across multiple regions for better availability guarantees, meaning that replica set members in additional regions will participate in the election and automated failover process should your primary fail. Note that you can also select your preferred region; this is the region where reads and writes will default to assuming that there are no active failure or failover conditions.
You can also deploy read-only replica set members. Read-only replica set members allow you to optimize for local reads (reduce read latency) across different geographic regions using a single MongoDB deployment. As the name suggests, these replica set members will not participate in the election and failover process and can never be elected to a primary replica set member.

Let’s look at a few examples. We’ll assume that your preferred region is us-east-1 in AWS, but not only do you want to withstand a partial region outage (an availability zone goes offline), you also want to withstand an outage of an entire AWS region. Your configuration might look something like what’s shown in the image below, where we’ve distributed our 3-node replica set across 3 regions in AWS. You might also increase the number of nodes in your replica set to 5 and keep 2 in us-east-1, your preferred region, 2 in us-east-2, and 1 in us-west-1. This would increase the likelihood that your primary node stays in your preferred region during a partial region outage. Note that Atlas will tell us the availability guarantees offered by our configuration, highlighted in the green lettering.

In the next example, let’s assume that you’re satisfied with the availability guarantees of having your cluster in the us-east-1 region, but you have a significant user base in the UK and want to provide them with an optimal user experience. You might set up a configuration like what’s shown below, deploying a read-only replica set member in London to serve local reads.

In the configuration shown below, we’ve enabled both options — our cluster is distributed across 3 regions in the US to ensure availability in the unlikely event of an entire regional outage AND we have a read-only replica set member for our user base in London.

Cross-region replication is now available on AWS, Azure and GCP. In addition, you can now have clusters in different regions within the same Project and easily move your deployments between regions while preserving availability.

Managed clusters at the right size (and price) for every project

We’re introducing new M2 and M5 instance sizes, available in select regions across all 3 clouds.

Built on a multi-tenant architecture, these new instance sizes allow you to jumpstart your project or scale beyond the M0 free tier, all for a low, predictable price.

As your storage, performance, or isolation requirements change, you can easily move beyond the initial boundaries and scale up to one of our dedicated cluster sizes.

Cross-region replication is now live in MongoDB Atlas. M2s and M5s are in public preview.

As always, we’d love to hear your feedback at mongodb-atlas@mongodb.com.

Not an Atlas user yet? Get started with a 512MB database for free.

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Investing in CS4All: One Year Later

When a couple of New York City high school teachers partnered with MongoDB to teach computer science, did they succeed? Their curriculum was untested, and they were teaching in difficult districts where most students are from poor and minority families. I talked with these two teachers, Jeremy Mellema and Timothy Chen, back in September , when they had completed a summer fellowship at MongoDB and had just started teaching their curriculum; at the end of the academic year this spring, I visited Jeremy and Tim again to find out the result. Their successes were sparse and partial. They discovered that their students' poor reading skills were a barrier to learning to code, and that teaching new coders how to solve problems is, itself, an unsolved problem. With a coarse unit of iteration—a school semester—it is painfully slow to experiment and find teaching methods that work. But even partial wins make a difference for individual kids, and the support of professional engineers at companies like MongoDB can be a powerful accelerant. What engages students Jeremy's main struggle was to get his students excited about code. He was assigned to teach a computer science class at Bronx Compass High School in the fall, using the curriculum he wrote during his fellowship at MongoDB last summer. In the beginning he spent too much time lecturing. “It felt weird,” he said. “It should be more like, ‘Let’s get down and dirty,’ and not, ‘Let’s have me talk to you.’” Even when his students did get their hands on computers, the first exercises were simply retyping Python scripts from a textbook. The payoff, watching a script run without throwing an exception, was hardly satisfying to them. Things started to click when he introduced Python turtle graphics , which gave the class more obvious evidence of their accomplishments. It also allowed Jeremy better opportunities to motivate and engage his students directly. “Some days I would challenge them to see who could make the craziest drawing,” Jeremy says. He would tell his students, “That’s so cool. I only made a star. You definitely beat me today.” Jeremy teaches both history and computer science, and he finds that some of his lowest-performing history students are his best CS students. “It’s satisfying to see them in their element,” he says. In Jeremy's view a computer science class can touch a student's intellect just as deeply as history. “People are multifaceted. You’re not only who you are when you’re in my history class.” Jeremy's computer science class was cancelled this spring; the students at Bronx Compass High School are behind on history credits and there are only three history teachers on staff. For now, computer science is merely an elective, so Jeremy is back teaching history full-time. “I really miss teaching CS,” he says. If he resumes the course, Jeremy thinks it must be livelier. He is reconsidering his use of the videos from How To Think Like a Computer Scientist, which he studied last summer on the recommendation of his mentor Shannon Bradshaw, MongoDB’s vice president of education. The content helped Jeremy train to teach CS, but when he showed the videos to his kids they were bored. Jeremy hopes to make new videos that will draw them in. His students from the fall semester say he should get their advice. Otherwise, they warned, “you might do something that you think is cool but it’s actually super corny.” A head start Although there is no computer science elective this semester, some students are pursuing the topic in other ways. A young woman from his class in the fall, Tatyana Camacho, now interns for the high school’s IT department. I had quoted her in my previous article, and Jeremy tells me she loved it. She commanded him to show her father in the next parent-teacher conference: “You need to show my dad that I’m one of the advanced students.” Jeremy still runs the afternoon Computer Club. I visited the club to meet a student, Daniel Rodriguez, who was tinkering with an Arduino and a circuit board that the school provided. “I don't have the ability to get this equipment otherwise, in my predicament,” says Daniel. He starts his Arduino projects by copying examples. The wiring is easier than the coding for him, he told me, "especially because I'm not the best speller in the world." Once he has the example working he modifies it to his own taste. Most recently, he wanted to show a message but, with only LEDs, he can’t display much. He researched Morse Code and made a light flash the code for “HELLO”, like any programmer demonstrating a system for the first time. “Most people think that once you plug something in, that’s it, it works,” says Daniel. “But I’m the person that makes the circuit run. I tell people, ‘I made it do that.’ And seeing them fascinated by what I did, it makes me, in turn, fascinated by what I'm doing.” Daniel has to return the Arduino at the end of the year. Next year he’ll go to a trade school for electricians. Working with the Arduino will give him an advantage, he hopes, and it seems plausible to me. As he finishes school and starts work as an electrician the world will be changing around him: smart appliances and programmable components will be everywhere. An electrician who loves to code will have a big head start. Making anything they want Timothy Chen teaches in Hell’s Kitchen, at Urban Assembly Gateway School for Technology. I visited his class in May to see how his students had progressed since I last saw them in September. They were involved in a multi-week project called the AP Create Task, part of a national Advanced Placement exam. “They are allowed to make literally anything they want, in any language,” says Tim. Students submit their code and a one-minute video of the program in action, and they may describe their project either in writing or in audio narration. I was surprised by how 21st Century the test is, and how accommodating it could be to students with a deficit in reading and writing. It must be remarkably difficult, however, to score fairly. The Create Task is many students’ first time scoping and integrating a sizable project, and there were flameouts. One young man tried to make a maze game drawn with ASCII characters; it proved too ambitious and he ran out of time. Tim isn’t supposed to help students define the scope of their projects, but if they announce they’re going to tackle something difficult he will push them to list all the components. In the best case, they realize they don’t know how to do most of the project and choose something simpler. One of Tim’s students, Jahseem Maxwell, was building a Go Fish card game in Python, and she was having trouble integrating the pieces. “It has to be a certain order and it's hard to make that order when you don't know, really, what you’re doing. I’m struggling, putting it all together.” Another student, Cecilia Gonzalez, was writing a Choose Your Own Adventure game. She says the AP Create Task encourages students to work in pairs. “We work sort of together but not exactly.” Each must create at least one significant part of the program independently. Cecilia’s game is based on a monster of urban legend called The Rake, which comes closer when you think about it. The game begins by asking questions such as the player’s name and height. She told me the player’s answers will determine “some things that are going to happen,” but she didn’t give away any spoilers. When Tim began teaching the class in September, he hadn’t written the ending yet, either. His greatest fear was his students would learn the curriculum faster than he could write it. By May it was clear that wasn’t a problem. “Some of the students can’t read very well, and that was a big barrier because all the things I made were text,” he says. “Everything just took longer than expected.” Problem solving How do you teach problem solving? This is Tim’s great unanswered question from the year. Perhaps if high school computer science were taught like math, as a series of small problems with only one right answer each, then how to solve those problems wouldn't be such a mystery. But high school CS is taught like art class. Tim’s students invent new projects and somehow solve the unpredictable problems that arise in them. Tim speculates that he would learn problem solving himself by watching an experienced programmer solve a new problem, hit roadblocks, and overcome them. Indeed, that is how I have taught problem solving to MongoDB interns. Together, we attack problems without knowing the answers beforehand. It requires an entire summer of one-on-one collaboration. “I don't think that model works very with the kids,” says Tim, “especially if they are not very good with sitting still for an extended period. I'm not sure how to reach them.” Falling in love Tim, like Jeremy, wants to make more multimedia to reach students despite their poor reading skills. “I want to rethink how it should be done before I start this time. I kind of jumped into it too quickly.” Tim’s main goal is to give kids the chance to fall in love with programming and continue on their own. Many other goals are still out of reach: students at his school score low on the AP test, and few of them are likely to get a college degree in CS or be professional coders. Still, Tim hopes that a more varied course, with audio and video, could bring students farther. “The big hurdle for everyone is teaching problem solving. If I can get that, everything else is easy. I'm still trying to figure out how to do that.”

October 3, 2017

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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