Nexthink scales to trillions of events per day with Amazon MSK | Amazon Web Services

Real-time data streaming and event processing present scalability and management challenges. AWS offers a broad selection of managed real-time data streaming services to effortlessly run these workloads at any scale.

In this post, Nexthink shares how Amazon Managed Streaming for Apache Kafka (Amazon MSK) empowered them to achieve massive scale in event processing. Experiencing business hyper-growth, Nexthink migrated to AWS to overcome the scaling limitations of on-premises solutions. With Amazon MSK, Nexthink now seamlessly processes trillions of events per day, reaching over 5 GB per second of aggregated throughput.

In the following sections, Nexthink introduces their product and the need for scalability. They then highlight the challenges of their legacy on-premises application and present their transition to a cloud-centered software as a service (SaaS) architecture powered by Amazon MSK. Finally, Nexthink details the benefits achieved by adopting Amazon MSK.

Nexthink’s need to scale

Nexthink is the leader in digital employee experience (DeX). The company is shaping the future of work by providing IT leaders and C-levels with insights into employees’ daily technology experiences at the device and application level. This allows IT to evolve from reactive problem-solving to proactive optimization.

The Nexthink Infinity platform combines analytics, monitoring, automation, and more to manage the employee digital experience. By collecting device and application events, processing them in real time, and storing them, our platform analyzes data to solve problems and boost experiences for over 15 million employees across five continents.

In just 3 years, Nexthink’s business grew tenfold, and with the introduction of more real-time data our application had to scale from processing 200 MB per second to 5 GB per second and trillions of events daily. To enable this growth, we modernized our application from an on-premises single-tenant monolith to a cloud-based scalable SaaS solution powered by Amazon MSK.

The next sections detail our modernization journey, including the challenges we faced and the benefits we realized with our new cloud-centered, AWS-based architecture.

The on-premises solution and its challenges

Let’s first explore our previous on-premises solution, Nexthink V6, before examining how Amazon MSK addressed its challenges. The following diagram illustrates its architecture.

V6 was made up of two monolithic, single-tenant Java and C++ applications that were tightly coupled. The portal was a backend-for-frontend Java application, and the core engine was an in-house C++ in-memory database application that was also handling device connections, data ingestion, aggregation, and querying. By bundling all these functions together, the engine became difficult to manage and improve.

V6 also lacked scalability. Initially supporting 10,000 devices, some new tenants had over 300,000 devices. We reacted by deploying multiple V6 engines per tenant, increasing complexity and cost, hampering user experience, and delaying time to market. This also led to longer proof of concept and onboarding cycles, which hurt the business.

Furthermore, the absence of a streaming platform like Kafka created dependencies between teams through tight HTTP/gRPC coupling. Additionally, teams couldn’t access real-time events before ingestion into the database, limiting feature development. We also lacked a data buffer, risking potential data loss during outages. Such constraints impeded innovation and increased risks.

In summary, although the V6 system served its initial purpose, reinventing it with cloud-centered technologies became imperative to enhance scalability, reliability, and foster innovation by our engineering and product teams.

Transitioning to a cloud-centered architecture with Amazon MSK

To achieve our modernization goals, after thorough research and iterations, we implemented an event-driven microservices design on Amazon Elastic Kubernetes Service (Amazon EKS), using Kafka on Amazon MSK for distributed event storage and streaming.

Our transition from the v6 on-prem solution to the cloud-centered platform was phased over four iterations:

• Phase 1 – We lifted and shifted from on premises to virtual machines in the cloud, reducing operational complexities and accelerating proof of concept cycles while transparently migrating customers.
• Phase 2 – We extended the cloud architecture by implementing new product features with microservices and self-managed Kafka on Kubernetes. However, operating Kafka clusters ourselves proved overly difficult, leading us to Phase 3.
• Phase 3 – We switched from self-managed Kafka to Amazon MSK, improving stability and reducing operational costs. We realized that managing Kafka wasn’t our core competency or differentiator, and the overhead was high. Amazon MSK enabled us to focus on our core application, freeing us from the burden of undifferentiated Kafka management.
• Phase 4 – Finally, we eliminated all legacy components, completing the transition to a fully cloud-centered SaaS platform. This multi-year journey of learning and transformation took 3 years.

Today, after our successful transition, we use Amazon MSK for two key functions:

• Real-time data ingestion and processing of trillions of daily events from over 15 million devices worldwide, as illustrated in the following figure.

• Enabling an event-driven system that decouples data producers and consumers, as depicted in the following figure.

To further enhance our scalability and resilience, we adopted a cell-based architecture using the wide availability of Amazon MSK across AWS Regions. We currently operate over 10 cells, each representing an independent regional deployment of our SaaS solution. This cell-based approach minimizes the area of impact in case of issues, addresses data residency requirements, and enables horizontal scaling across AWS Regions, as illustrated in the following figure.

Benefits of Amazon MSK

Amazon MSK has been critical in enabling our event-driven design. In this section, we outline the main benefits we gained from its adoption.

Improved data resilience

In our new architecture, data from devices is pushed directly to Kafka topics in Amazon MSK, which provides high availability and resilience. This makes sure that events can be safely received and stored at any time. Our services consuming this data inherit the same resilience from Amazon MSK. If our backend ingestion services face disruptions, no event is lost, because Kafka retains all published messages. When our services resume, they seamlessly continue processing from where they left off, thanks to Kafka’s producer semantics, which allow processing messages exactly-once, at-least-once, or at-most-once based on application needs.

Amazon MSK enables us to tailor the data retention duration to our specific requirements, ranging from seconds to unlimited duration. This flexibility grants uninterrupted data availability to our application, which wasn’t possible with our previous architecture. Furthermore, to safeguard data integrity in the event of processing errors or corruption, Kafka enabled us to implement a data replay mechanism, ensuring data consistency and reliability.

Organizational scaling

By adopting an event-driven architecture with Amazon MSK, we decomposed our monolithic application into loosely coupled, stateless microservices communicating asynchronously via Kafka topics. This approach enabled our engineering organization to scale rapidly from just 4–5 teams in 2019 to over 40 teams and approximately 350 engineers today.

The loose coupling between event publishers and subscribers empowered teams to focus on distinct domains, such as data ingestion, identification services, and data lakes. Teams could develop solutions independently within their domains, communicating through Kafka topics without tight coupling. This architecture accelerated feature development by minimizing the risk of new features impacting existing ones. Teams could efficiently consume events published by others, offering new capabilities more rapidly while reducing cross-team dependencies.

The following figure illustrates the seamless workflow of adding new domains to our system.

Furthermore, the event-driven design allowed teams to build stateless services that could seamlessly auto scale based on MSK metrics like messages per second. This event-driven scalability eliminated the need for extensive capacity planning and manual scaling efforts, freeing up development time.

By using an event-driven microservices architecture on Amazon MSK, we achieved organizational agility, enhanced scalability, and accelerated innovation while minimizing operational overhead.

Seamless infrastructure scaling

Nexthink’s business grew tenfold in 3 years, and many new capabilities were added to the product, leading to a substantial increase in traffic from 200 MB per second to 5 GB per second. This exponential data growth was enabled by the robust scalability of Amazon MSK. Achieving such scale with an on-premises solution would have been challenging and expensive, if not infeasible.

Attempting to self-manage Kafka imposed unnecessary operational overhead without providing business value. Running it with just 5% of today’s traffic was already complex and required two engineers. At today’s volumes, we estimated needing 6–10 dedicated staff, increasing costs and diverting resources away from core priorities.

Real-time capabilities

By channeling all our data through Amazon MSK, we enabled real-time processing of events. This unlocked capabilities like real-time alerts, event-driven triggers, and webhooks that were previously unattainable. As such, Amazon MSK was instrumental in facilitating our event-driven architecture and powering impactful innovations.

Secure data access

Transitioning to our new architecture, we met our security and data integrity goals. With Kafka ACLs, we enforced strict access controls, allowing consumers and producers to only interact with authorized topics. We based these granular data access controls on criteria like data type, domain, and team.

To securely scale decentralized management of topics, we introduced proprietary Kubernetes Custom Resource Definitions (CRDs). These CRDs enabled teams to independently manage their own topics, settings, and ACLs without compromising security.

Amazon MSK encryption made sure that the data remained encrypted at rest and in transit. We also introduced a Bring Your Own Key (BYOK) option, allowing application-level encryption with customer keys for all single-tenant and multi-tenant topics.

Enhanced observability

Amazon MSK gave us great visibility into our data flows. The out-of-the-box Amazon CloudWatch metrics let us see the amount and types of data flowing through each topic and cluster. This helped us quantify the usage of our product features by tracking data volumes at the topic level. The Amazon MSK operational metrics enabled effortless monitoring and right-sizing of clusters and brokers. Overall, the rich observability of Amazon MSK facilitated data-driven decisions about architecture and product features.

Conclusion

Nexthink’s journey from an on-premises monolith to a cloud SaaS was streamlined by using Amazon MSK, a fully managed Kafka service. Amazon MSK allowed us to scale seamlessly while benefiting from enterprise-grade reliability and security. By offloading Kafka management to AWS, we could stay focused on our core business and innovate faster.

Going forward, we plan to further improve performance, costs, and scalability by adopting Amazon MSK capabilities such as tiered storage and AWS Graviton-based EC2 instance types.

We are also working closely with the Amazon MSK team to prepare for upcoming service features. Rapidly adopting new capabilities will help us remain at the forefront of innovation while continuing to grow our business.

To learn more about how Nexthink uses AWS to serve its global customer base, explore the Nexthink on AWS case study. Additionally, discover other customer success stories with Amazon MSK by visiting the Amazon MSK blog category.

About the Authors

Moe Haidar is a principal engineer and special projects lead @ CTO office of Nexthink. He has been involved with AWS since 2018 and is a key contributor to the cloud transformation of the Nexthink platform to AWS. His focus is on product and technology incubation and architecture, but he also loves doing hands-on activities to keep his knowledge of technologies sharp and up to date. He still contributes heavily to the code base and loves to tackle complex problems.

Simone Pomata is Senior Solutions Architect at AWS. He has worked enthusiastically in the tech industry for more than 10 years. At AWS, he helps customers succeed in building new technologies every day.

Magdalena Gargas is a Solutions Architect passionate about technology and solving customer challenges. At AWS, she works mostly with software companies, helping them innovate in the cloud. She participates in industry events, sharing insights and contributing to the advancement of the containerization field.

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• Source: https://aws.amazon.com/blogs/big-data/nexthink-scales-to-trillions-of-events-per-day-with-amazon-msk/