Built With MongoDB: Vectorly

Siya Raj Purohit and Adam Hughes
June 9, 2021 | Updated: October 17, 2022
#BuiltWithMongoDB#Startups

When Sam Bhattacharyya spent time in the Peace Corps as a teacher in Mexico, he learned how much of a barrier the lack of internet bandwidth was for his classes. The students simply did not have the resources needed to take advantage of online learning, which was a problem Sam soon became fixated on.

Years later, Sam founded his company Vectorly with a goal to fix that bandwidth issue via an AI-based video compression solution that streams low-resolution videos and turns them into a high-definition viewing experience.

Vectorly is a software development kit (SDK) that companies can integrate into their video applications. Vectorly released its minimal viable product (MVP) for use by early customers in February 2021 and has a total of 20 companies that are actively using the product.

In this edition of #BuiltWithMongoDB, we talked with Sam about how Vectorly’s software works, how he got started with MongoDB for Startups, and the future of this fast-growing industry.

MongoDB: What's Vectoryl's mission?

Sam: We’re building a technology that uses artificial intelligence (AI) to upscale and enhance video in real time on users’ devices, as they watch it. So, what that lets a user do is stream low-resolution video content and watch it in high definition.

We have about 100 AI models on our server. Most of them are for AI upscaling, for different kinds of content and different quality levels. Based on feedback from customers, we've also been building AI filters for, say, virtual background replacement.

All that data is loaded in real time from the server every time you load the library. With our SDK, you specify that you want to use this AI filter on that library, and you have an API token that calls our API and that returns the AI model in real time to your device so you can watch the upscaled video.

AI takes some computing power, which can be a concern especially on low-end devices, and we’re conscious of that, so we pay close attention to performance and frame rate to make sure our AI models do not overload the devices users are working on.

MongoDB: What are some of the use cases for Victory?

Sam: The first is to think of a user that is watching Netflix with a slow internet connection. Because the network is so slow, that user’s going to end up with a low-resolution version of the video. But we have AI filters that can pop in and start to upscale and enhance the video and make it look as if it’s high definition. The other use case is around video conferencing, where all kinds of things can affect call quality or user experience, from background noise to blurry video. You can use AI to correct any of those issues that come up.

MongoDB: What does your tech stack consist of?

Sam: Our product is a software library, which is for the web, and it’s all built in JavaScript. The main JavaScript functionality we’re using is called WebGL, which is a graphics pipeline that lets you access the GPU on devices. We have a bunch of AI models on our server, which are just numbers stored in JSON files. Our SDKs load the AI models in real time, and we use MongoDB to track and store event data, as well as basic metadata.

MongoDB: How did you choose MongoDB?

Sam: I've been using MongoDB since I started programming in 2012. Although the first programming course I took used the LAMP stack (Linux, Apache, MySQL, and PHP), SQL seemed unintuitive, and the LAMP stack in general just felt bulky.

When I started my first personal programming project, I looked for alternatives, and I found this new thing called the MEAN stack (MongoDB, Express.js, AngularJS, and Node.js). I thought it was the greatest thing in the world that you could use JavaScript in the front end and the back end, and that you could even use JSON like notation for the database. Having a full JavaScript stack made so much sense.

Every web development project I've started since has used the MEAN stack. When it came time to hack together the first version of Vectorly, MongoDB was our first choice for the database.

MongoDB: How has the experience been working with MongoDB?

Sam: It’s been fantastic. We had to come up with this model of tracking users and usage of our platform in a very short amount of time, because the first version we released had no tracking whatsoever. One of the things that saved us a lot of time was the MongoDB Charts function, because it really allows us to track what we’re doing. It was super quick to set up.

Looking to build something cool? Get started with the MongoDB for Startups program.

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Built With MongoDB: Phable

Hundreds of millions of people across India face chronic diseases. India has the second-highest number of diabetics in the world, and citizens with high blood pressure, thyroid conditions, and other chronic ailments are underserved in the country because there’s no robust system in place governing how the treatment and diagnosis will be handled. Given the lack of a proper infrastructure, diseases slip under the radar because they’re not caught early on. That’s where Phable comes in. According to TechCrunch , "Phable has created a more transparent and real-time communication channel that allows a doctor to nudge their patients to take their medicine on time, and make any necessary changes to the lifestyle or medication cycle, or request a follow-up appointment. The app itself can be used for tele-consultation, the demand for which has skyrocketed in recent quarters as coronavirus forced people to stay indoors.” The company, which has raised $12 million in funding from India’s Manipal Hospitals and venture capital and investment management firm SOSV, reaches 350,000 patients, 5,000 doctors, and a staff of 100 people across Chennai and Bangalore. In this edition of #BuiltWithMongoDB, we talk with Phable’s Creative and Marketing Consultant Ganesh Chandrashekar and Engineering Manager Venkatesh Walajabad about what drives their business. MongoDB: What is the Phable product offering right now? Ganesh: We currently have two products: patient facing, and doctor facing. For patients, we give them a sense of their everyday health and handhold them through the process of understanding their first symptoms, getting prescribed a treatment plan, and recording their ongoing lifestyle changes. We help map and manage those lifestyle changes at a fundamental level, while giving them intelligent insights to help them make small tangible changes to everyday habits. We also connect them to doctors in a more real-time manner, so doctors have deep visibility into a patient’s health, and the patients can get personalized recommendations from doctors. While our focus is on preventive and personalized care, we have some value-added services that ease our users’ journey. They can order medicines from the app, schedule video consultations with doctors, and request lab tests directly. We’re building a broader health tech ecosystem where we are able to partner with the relevant companies — including some leading names in health device manufacturing, insurance companies, and medicine providers. For doctors, we’ve built a full product suite with a decision support system and EMR. So we’re able to help them digitize their practice, prioritize and process patient data, simplify clinic management and build better relationships with their patients. MongoDB: Has COVID-19 impacted product adoption or any of the features that are being used? Ganesh: Our growth has been in parallel with the pandemic over the past year. The pandemic gave a sense of urgency, and put the spotlight back on healthcare and understanding health at a more granular level. A lot of the new features we have — especially virtual doctor consultations — were developed at a breakneck speed to cater to users at home during the pandemic. MongoDB: You released the video consultations really quickly, especially given the uncertainty surrounding COVID-19 in 2020. How did you approach that from the technical side? Ventkatesh: Because there were a lot of unknowns in building this, we wanted to experiment and release in certain phases so we could gather feedback and then add features on top of that. We are quite nimble at Phable as a whole: we started with consultations, moved on to an ecommerce platform, and then added wallet features. Similarly, for the video consultation product, we released in chunks — experimenting with users, analyzing their usage, and then shipping the feature more widely. MongoDB: How did the team decide to build with MongoDB? Venkatesh: The decision for MongoDB happened right from Day 1, because the team wanted to go with a MEAN [MongoDB, Express.js , AngularJS , and Node.js ] and MERN [MongoDB, Express.js, ReactJS, and Node.js] stack. Initially we used the community version, and then early last year we shifted to MongoDB Atlas. We wanted to use all the clustering capabilities and backup support, in addition to the profiling and detection of slow queries. We use a lot of those features to figure out where our bottlenecks are. We got some credits through MongoDB for Startups, but MongoDB Atlas is still on the more expensive side for us. Even though it is a little expensive, the advantages that we get from MongoDB Atlas far outweigh the cost. We use AWS for our server needs, and we have a fair bit of integration between AWS and MongoDB via VPC peering so all data is more secure, in addition to the encryption MongoDB provides. MongoDB: How is your engineering team structured? Venkatesh: There are 18 engineers on the team — and we’re trying to add more so we can launch more features and expand into new markets. Readers take note: We are hiring engineers for our India offices! MongoDB: How has scaling with MongoDB been, especially given how much you've grown during COVID_19? Venkatesh: MongoDB Atlas takes care of all the autoscaling for us. We worked closely with a consultant to figure out what minimum and maximum instances we need for our clusters, and then we rely on MongoDB to do the autoscaling. During a calmer period, MongoDB Atlas scales down perfectly well and reduces the costs; in a high-growth period, it scales up to accommodate for the traffic. We love that it automanages things so we don’t have to worry about it day to day. Because MongoDB’s features take care of most of the work, we don’t need a dedicated person to oversee this — we plan a few months ahead, and then we let MongoDB take care of the work. Looking to build something cool? Get started with the MongoDB for Startups program.

June 2, 2021
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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