Architecting Scalable Microservices and Event Systems

When writing a piece of software, we are generally thinking about solving a particular problem or adding a specific functionality.

But, we are also thinking about how this piece of software will evolve in the future, because we know that our software will need to change: We carefully modularize our code, we write tests and even go the extra mile of configuring its continuous integration and continuous deployment scripts.

On the other hand, when designing systems, we are more concerned about:

• how it will scale to our user base ?
• how will the response time be affected ?
• will it still fulfil the initial requirements ?

I have been in the software engineering field for more than a decade and a half, within diverse and varied industries, and I can assert, from experience, that answering these questions is not always straightforward.

At the same time, we want to ensure that the systems we build age well at with the company, and that they keep our business nimble and our customers happy.

In this article, we explore how combining Kafka and Kubernetes support architecting for scale by integrating event streaming with microservices to produce robust, scalable event-driven architectures without burning piles of cash in required engineering effort.

A step-by-step guide to deploy both tools as well as setting-up basic monitoring for the clusters is provided at the end of this article.

The Need, and the Problem, for Autonomy

First of all, why do we need such tools? Is it only to keep up with the latest and greatest and buzz-word technologies ? Not really. Let me explain:

As a company grows it forms into teams, and those teams have different responsibilities and need to be able to make progress without extensive interaction with one another.

The larger the company, the more of this autonomy they need, and most importantly, a balance must be struck in terms of the way people, responsibility and communication structures are arranged in a company.

This balance translates inevitably to the software.

Say for instance that when working on a project and someone from another team asks if their application can pull data out of your database, you know that this is probably going to lead to problems down the road. You will keep wondering if your latest release will break the dependency they have on you.

That’s why, among other reasons, we design software systems where components are operated and evolved independently.

When architecting for scale, we are more concerned about how to build software that runs a company while organizing a large engineering effort.

The problem we face has three distinct, but linked, parts: organization, software and data.

What differentiates the good architecture from the bad, is their ability to manage these three factors as they evolve, independently, over time.

Now, let’s explore how combining microservices and event-based architectures can help, in this case, with Kubernetes and Apache Kafka.

Cloud-Native with Kafka and Kubernetes

Both Apache Kafka and Kubernetes are powerful technologies that, when used together, create a robust foundation for building scalable, resilient, and efficient cloud-native applications.

Apache Kafka, a distributed event streaming platform, excels at handling large volumes of real-time data, enabling high-throughput and low-latency communication between services.

Kubernetes, the de facto standard for container orchestration, automates the deployment, scaling, and management of containerized applications, ensuring resilience and scalability.

We can group the benefits of using them as follow :

1.Scalability

Kafka is designed to handle high-throughput data streams and scale horizontally. It can manage large volumes of data and a high number of concurrent producers and consumers.

On the other hand, Kubernetes excels at automating the deployment, scaling, and management of containerized applications. It can dynamically scale applications based on demand, ensuring optimal resource utilization.

Kafka’s partitioning and replication mechanisms ensure that data can be distributed and processed efficiently across multiple brokers.

Similarly for containers, Kubernetes manages their lifecycle, automatically restarting failed containers and scaling out additional instances as needed.

When Kafka is deployed on Kubernetes, Kafka brokers can be scaled up or down seamlessly as the data volume or processing demand changes. Kubernetes handles the orchestration, ensuring that the Kafka cluster remains balanced and operational.

2. Resilience and High Availability

Kafka ensures high availability through data replication across multiple brokers. If one broker fails, another broker with the replicated data can take over to support fault tolerance.

Kubernetes provides self-healing capabilities, such as automatic restarts, rescheduling of failed containers, and node health checks. This ensures that applications continue running smoothly even in the event of hardware or software failures.

3. Ease of Management

Kafka requires careful management of brokers, topics, partitions, and consumer groups to maintain performance and reliability. On the other hand, Kubernetes provides powerful management tools and declarative configuration for deploying and managing applications.

Tools like Helm and Kubernetes Operators (e.g., Strimzi for Kafka) simplify the deployment and management of Kafka clusters (Follow the link below for a step-by-step deployment with Strimzi).

Also, Kubernetes’ built-in monitoring and logging capabilities, along with integrations with Prometheus and Grafana, provide deep insights into the health and performance of Kafka clusters.

With Kubernetes, Kafka clusters can be managed declaratively, reducing the operational overhead. Kubernetes Operators like Strimzi automate complex tasks such as broker configuration, rolling upgrades, and scaling.

4. Resource Efficiency

Kafka’s performance is highly dependent on resource allocation and efficient use of CPU, memory, and disk I/O.

To help with this, Kubernetes efficiently schedules and allocates resources to containers based on their requirements and the overall cluster resources. It can ensure that no single application monopolizes resources at the expense of others.

The Takeway

Architecting at scale is not an easy task. We are organizing a large engineering effort to build software that is basically running the company.

By leveraging the strengths of both Kafka and Kubernetes, organizations can build highly scalable, resilient, and manageable cloud-native applications.

Kafka’s robust event streaming capabilities, combined with Kubernetes’ orchestration and management features, create an environment where applications can grow and adapt to changing demands seamlessly.

This synergy not only enhances performance and reliability but also simplifies the complexities of managing distributed cloud-native architectures.