Kafka is a message transportation system. It is a subscriber-based model where microservices/monoliths send messages to a topic that other services listen to.
Kubernetes uses declarative configuration and enforces immutability, making deploying, monitoring, and upgrading services easier. However, it requires skilled infrastructure teams to set up. In the classical sense, Kafka vs Kubernetes are incompatible since they are two distinct technologies with different uses.
Deployment
Many companies need help with the time it takes to build and deploy Kafka in Kubernetes. This process can require significant tech team resources. Getting buy-in from internal IT teams resistant to change is also difficult.
In addition, the default features of K8s, such as load balancing, can cause issues or break Kafka. It’s recommended that you disable these features.
A cloud operator can help you avoid some of these issues by deploying Kafka in an optimized cluster for your use case. It can also automate some tasks you would need to perform manually, such as creating certificates and configuring Kafka producers and consumers.
A managed Kafka solution can save your company money by preventing over-purchasing hardware and speeding up project completion times.
Scalability
Kafka serves as a central messaging hub between many services within your cloud. For example, the User service publishes a message about a new user to a Kafka topic, which the Email service then consumes and emails that new user. The communication between these services is asynchronous.
Kubernetes makes it easier to scale up a production Kafka cluster. You can add brokers with a single command or a configuration line and easily perform updates, restarts, and configuration changes. This declarative configuration also enforces immutability. If resources drift out of a declared specification, k8s destroy and rebuild them to bring them back into alignment.
A stateful Kafka application requires a persistent volume (PV) to prevent data loss from pod failures. With PV claims and storage classes, you can use pod-mounted storage to ensure a Pod has the same storage identity across all nodes in the cluster. This helps with data locality for the Kafka broker and improves performance. You can also limit the number of pods a node hosts with a Pod Disruption Budget, an integer between minAvailable and maxAvailable.
Performance
Running Kafka as a service in Kubernetes can add complexity to the platform. This can include a new role and additional responsibility for IT teams. If your team has limited experience with Kubernetes and Kafka, they’ll need to learn a whole new skill set to deploy and manage the application within a modern infrastructure.
Kafka workloads may have different update frequencies, scale requirements, and other factors. Combining those workloads in a single Kubernetes cluster could cause adverse performance impacts or consume more resources than necessary. This could impede the ability to meet business objectives, such as data sovereignty and compliance requirements.
Many organizations want the flexibility of open-source Kubernetes but need more IT staff or time to implement and maintain it themselves. Managing the cluster via a managed service provider can ease the burden and ensure consistent deployment performance. This also helps to shorten software CI/CD cycles, deliver a seamless user experience, and provide system self-healing through automatic restarts or replication. However, several important considerations are when choosing a managed Kafka provider, including hosting and price.
Security
Often, organizations need more than just Kafka to manage data. They must also integrate it with other tools to process data as close to the source as possible. This requires additional computing and storage power to ensure low latency. In addition, the organization may need to invest in the skills to set up and manage these tools.
For example, an organization might have to hire a developer or tech team to set up a Kafka cluster in their infrastructure and keep it running. This costs the org both the direct cost of a new employee and the indirect costs of training existing employees.
Managing Kafka can be challenging for a DevOps team. Many organizations end up with multiple production Kafka clusters, development environments, test/tryout environments, and blue-green deployment environments. A solution for this is a managed Kafka platform like Panoptica that can reduce complexity and save a slew of internal resources and expenses.
Backup
Kafka provides replication and mirroring to ensure data is available in case a broker or a host fails. It also supports a variety of encryption methods to ensure the communication between producers and consumers is secured. Integrating this with Kubernetes is straightforward, though there are a few caveats.
For example, if a producer Pod is deleted and re-created, it must reassign its partitions from other brokers in the cluster. This process requires additional monitoring and manual intervention from Kafka administrators. Additionally, it can cause significant downtime if it isn’t properly executed.
To avoid these issues, you should use a solution that automates moving data between hosts in the same cluster and between clusters.
