Kubernetes Simplifies Your Apache Kafka Deployment with Strimzi

This article will explain how Kubernetes and Strimzi work together to simplify and enhance the management of Kafka instances within containerized environments. You might be familiar with all the details of Kafka, but if you need your knowledge refreshed, here’s a quick Kafka rundown.

In the meantime, we will explore the nuances and solutions for achieving efficient Kafka deployment on Kubernetes using Strimzi. Get ready, grab your coffee, and take a seat because we’re about to explore in 2200+ words why Strimzi is the perfect match for running Kafka on Kubernetes. At the end of this blog, you will understand why and how to deploy, manage, and scale Kafka on Kubernetes using Strimzi, along with best practices and common pitfalls to avoid.

First things first, what exactly is Kubernetes

Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services that facilitates explicit configuration and automation. It has a large, rapidly growing ecosystem. It automates operational tasks of container management and includes built-in commands for deploying applications, rolling out changes to your applications, scaling your applications up and down to fit changing needs, and monitoring your applications. Excited about Kubernetes and hungry for more knowledge? Here’s a comprehensive piece of content on Kubernetes to satisfy your curiosity.

Challenges in Deploying Apache Kafka on Kubernetes

Deploying Apache Kafka in containerized environments presents several challenges, particularly ensuring seamless integration and efficient management within Kubernetes clusters. Container orchestration platforms like Kubernetes offer flexibility and scalability but require solutions like Strimzi to overcome complexities such as resource allocation and networking configurations and ensure Kafka brokers’ high availability and performance. Balancing the dynamic nature of containers with Kafka’s persistence and reliability demands introduces unique challenges that must be addressed to achieve optimal deployment and operational efficiency.

These are the three biggest challenges:

Scalability

Scalability in Kubernetes presents a significant challenge as the number of containers increases within a cluster. Effectively managing scalability involves adding more containers and ensuring the infrastructure and resources can support the growing workload without compromising performance or stability. As containerized applications scale, complexities arise in orchestrating communication between containers, maintaining synchronization across distributed systems, and optimizing resource allocation to meet diverse demands.

Complexity

Managing numerous containers, each with its role in a larger application, adds complexity. You can think of pod-to-pod communication within clusters, service discovery, and load balancing. Ensuring low-latency, high-throughput network connections between Kafka brokers and clients while maintaining security and fault tolerance requires careful network configuration and monitoring.

Management

It takes a lot of work to keep track of and maintain these containers to ensure they are up-to-date and running smoothly. Although Kubernetes hides the infrastructure details, effective resource management is still very important for running Kafka. Apache Kafka needs careful allocation of CPU, memory, and storage resources to handle high throughput and low-latency data processing. It is crucial to balance these resources across Kafka brokers and other components to avoid performance issues or over-provisioning, which can impact cost efficiency.

Kubernetes and its Impact on Containerized Applications

Now that we have discussed the challenges of using containerized applications let’s see how Kubernetes handles them.

Consider a simple scenario: deploying a basic web application. In a non-Kubernetes world, this might involve a straightforward script or a set of well-understood commands from your end. It means understanding how a pod hosts your containers, how a deployment manages these pods, how services enable communication, and the role of stateful sets in managing stateful applications. Each element is crucial and requires significant time and effort to master.

Here Kubernetes steps in as a powerful platform to manage the time and effort required for deploying, scaling, and operating application containers. As an open-source system, it simplifies container management, allowing applications to run efficiently and consistently. Kubernetes orchestrates the lifecycle of containers, deciding how and where they run and managing them based on organizational policies.

Introducing a framework

By introducing a framework for container orchestration, Kubernetes fundamentally transforms application deployment and management. Unlike traditional methods that often involve manual intervention and complex scripting, Kubernetes automates these processes across clusters of hosts. This automation reduces overhead and enhances application deployments’ overall efficiency and reliability.

Kubernetes also abstracts the underlying infrastructure complexities, allowing developers and operators to focus on defining application requirements rather than directly managing hardware or virtual machines. Its horizontal scaling capabilities enable applications to dynamically adjust the number of running instances based on demand, ensuring optimal performance during peak usage and cost-efficiency during low activity periods.

As containerized applications grow in complexity and demand, Kubernetes provides mechanisms for seamlessly scaling resources such as CPU and memory across clusters. This scalability allows organizations to add more nodes or increase resources allocated to individual pods, ensuring their applications can handle increased loads efficiently.

Kafka on Kubernetes: Check out the process

1. Configuring the namespace

This step is not mandatory but is advisable. A namespace in Kubernetes serves as a separation between the scope and the functionalities of the system.

2. Node based deployment

The primary reason for node-based deployment is to run Kafka brokers on different machines and different availability zones. Then, if one availability zone or one machine goes down, the cluster is still active and serving applications with data.

First, you get the nodes. Then, you need to identify the nodes you want to deploy Kafka on and then tag them with a name.

3. Deploy KRaft

Kafka needs KRaft to manage service discovery for brokers that form the cluster. KRaft sends any new changes in topology to Kafka. With such information, each node in the cluster gets to know when a broker dies or joins and when a topic is added or removed. KRaft gives an in-sync view of Kafka Cluster configuration. In a nutshell, KRaft is a primary dependency for Apache Kafka, so it is crucial to deploy it first.

4. Deploy Kafka

After the process of deploying the KRaft cluster, use the service names to allow Kafka to communicate with the cluster. You can build the Kafka image through installation with a specific or ready-made configuration. To allow external apps to publish messages to Kafka, you can create a load balancer in service for Kafka pod.

Other Factors When Running Kafka on Kubernetes

Low latency network and storage

The ideal conditions for Kafka have low contention for data on the wire, low noise accessing storage and high throughput.

Disaster Recovery Strategy

Kafka provides data mirroring between clusters and replication of topics. So, it’s essential to consider the time it takes to rebuild replicas and the disaster recovery strategy that’s in place when a cluster or zone fails.

Data Security

Kafka’s in-built security features include encryption using SSL between brokers, access controls for operations and authentication. It’s equally important to consider the data’s level of security in the disk’s file systems. If the data is not adequately protected, bad actors can gain access to manipulate it.

How Apache Kafka steps in

Now that we have explored Kubernetes’ capabilities in managing containerized applications, it’s essential to consider how it can be applied to complex systems like Apache Kafka within containerized environments.

Introduction to Apache Kafka for Containerized Environments

Apache Kafka, known for its high throughput and low-latency data processing capabilities, presents unique challenges when deployed on Kubernetes clusters.

Kubernetes’ ability to automate deployment, scaling, and operational tasks is particularly advantageous for Kafka, where managing multiple brokers and ensuring network communication are critical. By leveraging Kubernetes, organizations can streamline the deployment of Kafka clusters, optimize resource allocation, and enhance the overall reliability and scalability of their streaming platforms. This merging of Kubernetes and Apache Kafka represents an improvement in modern application architecture, empowering enterprises to efficiently handle large-scale data processing and real-time analytics in cloud-native environments.

Kubernetes is a good container platform for running stateless applications or services. However, it is not a natural fit for stateful applications like Kafka.

Before deploying the Kafka cluster for production, you must conduct a health check on the Kubernetes pods. The liveness probe will automatically restart a pod if it fails to respond. Meanwhile, the readiness probe determines whether the Kafka pod can start processing incoming requests. And this is where Strimzi comes in.

With Strimzi, you don’t need to go through this complicated process of setting up the probes since it’s already implemented. If you need to take an in-depth look at Strimzi, click here. Are you already prepared for the next step? Keep on reading!

Role of Strimzi in Simplifying Apache Kafka Deployment on Kubernetes:

Strimzi is an open-source tool for managing and maintaining Kafka clusters. It offers several operators, including Kafka Connect, Kafka MirrorMaker, and Kafka Exporter. The platform emphasizes deployment and management, focusing on running Kafka components, managing brokers and users, and providing highly configurable access settings.

Strimzi Operators can extend Kubernetes functionality, automating everyday and complex tasks related to a Kafka deployment. By implementing knowledge of Kafka operations in code, Kafka administration tasks are simplified and require less manual intervention. Using Strimzi operators reduces the need for manual intervention and streamlines the process of managing Kafka in a Kubernetes cluster.

Deploying Kafka components onto a Kubernetes cluster using Strimzi is highly configurable using custom resources. These resources are created as instances of APIs introduced by Custom Resource Definitions (CRDs), which extend Kubernetes resources.

CRDs act as configuration instructions to describe the custom resources in a Kubernetes cluster. They are provided with Strimzi for each Kafka component used in deployment, as well as users and topics. CRDs and custom resources are defined as YAML files.

CRDs also allow Strimzi resources to benefit from native Kubernetes features like CLI accessibility and configuration validation.

Now, that’s quite a substantial amount of information, isn’t it? I could have simply summarized it as, “In short, Strimzi makes running Apache Kafka in a Kubernetes cluster easier.” But where’s the fun in that? So let’s dive a little deeper into this matter. Are you ready? Maybe get an extra cup of coffee. Here we go.

Strimzi’s Key features for specific advantages of using Strimzi within Kubernetes clusters

Strimzi offers a wealth of features designed to optimize Apache Kafka deployments within Kubernetes clusters. Let’s explore how these features translate into advantages for managing and scaling Kafka clusters with ease and efficiency.

Easy Installation

Strimzi offers an easy way to install Kafka on Kubernetes. It includes a set of Kubernetes manifests that can be deployed using standard Kubernetes tools such as kubectl. After deployment, Strimzi operators automatically manage the Kafka clusters.

Scalability and Elasticity

With Strimzi, Scaling Kafka clusters becomes hassle-free. It enables dynamic scaling based on workload, leveraging Kubernetes’ scaling capabilities to handle high loads without disruptions.

Integration with Kubernetes Ecosystem

Strimzi effortlessly integrates with a variety of Kubernetes tools and features. It supports:

• Kubernetes’ Service Discovery integration – for discovering Kafka brokers
• Kubernetes’ Storage Classes – for dynamically provisioning persistent volumes
• Kubernetes’ Ingress Controllers – for easy external access to Kafka from the cluster.

Monitoring and Alerting

Strimzi offers built-in monitoring and alerting features. It provides access to Kafka metrics through Prometheus, facilitating easy monitoring of the health and performance of Kafka clusters. It seamlessly integrates with popular alerting tools such as Grafana and Alertmanager, enabling users to configure alerts and notifications for critical events or anomalies.

Enhanced Security

Security is a critical aspect of any data infrastructure. Strimzi supports authentication and authorization mechanisms in Kafka clusters running on Kubernetes. It allows you to configure secure communication between Kafka components using SSL/TLS encryption and enables integration with external authentication systems like OAuth2 and LDAP.

These features make Strimzi an ideal choice for running Kafka on Kubernetes, enabling organizations to leverage the full potential of both technologies. That’s precisely the reason why we at Axual use Strimzi, and we think you should do the same.

Why and how Axual Uses Strimzi

For the Axual platform, we use Strimzi Cluster Operator to deploy Kafka in Kubernetes. We do not use Topic and User Operator, as our own Self-Service tool allows developers to manage their topics and ACLs with enterprise-level data governance and security.

As we discovered above, Strimzi can be crucial in simplifying the deployment and management of Kafka clusters on Kubernetes. Offering scalability, resilience, and operational efficiencies. By leveraging Strimzi, your organization can effectively handle the complexities of running Kafka in containerized environments, ensuring seamless integration with Kubernetes’ orchestration capabilities. However, there is one significant downside to the coin. Strimzi is an open-source system without support. All you have is online information and documentation. What do you do when this Strimzi documentation is insufficient to keep you going?

We from Axual have a solution for enterprises seeking to optimize their real-time data streaming solutions with Kafka on Kubernetes; Strimzi Incident Support.

Axual Strimzi Incident Support

Strimzi Incident Support services at Axual offer comprehensive assistance explicitly tailored to manage Strimzi environments in production and acceptance settings. Our support covers Level 1 and 2 incidents related directly to Strimzi, ensuring prompt resolution of critical issues that impact business operations. This includes ensuring Strimzi setups meet our stringent quality standards, whether provided by Axual or externally assessed for compatibility. As much as excited about it as I am? In this blog, you can read everything about it.

Wrapping up

Congratulations on making it through all that information! Now, let’s wrap this up with a flourish.

Kubernetes can completely change how containerized applications are managed by providing great flexibility and scalability. It unlocks the immense potential of containerized applications, conquering scalability challenges and boosting reliability while offering flexibility.

Strimzi simplifies deploying Apache Kafka with Kubernetes, making operations more efficient and enhancing scalability and flexibility even more. This makes it an excellent solution for modern, cloud-based architectures. It streamlines the deployment and management of Apache Kafka on Kubernetes, providing numerous benefits for developers and architects. By utilizing Kubernetes-native features and automation capabilities, Strimzi simplifies the operation of Kafka clusters in dynamic cloud-native environments. With Strimzi, developers can focus on building streaming applications without worrying about managing Kafka infrastructure. Strimzi makes Kafka more accessible and manageable, allowing organizations to harness the power of real-time data processing and analysis.

Choosing a platform like Axual further simplifies Kubernetes, empowering developers to build and deploy containerized wonders without getting tangled in the underlying complexity.

As we conclude this article after discussing and analyzing all of the information, it’s clear that with these tools available to you, the potential for creating scalable applications is truly limitless.

‍