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.
