Kafka requires a low-latency network and fast, high-throughput access storage. It must also be replicated across the cluster for fault tolerance and, ideally, in multiple data centers for performance.
This can be a high upfront cost for companies adopting real-time event-driven architectures and systems. It will also require IT teams to have a wide range of skills to maintain the system.
Install Kafka on Kubernetes
The efficiencies of Kubernetes can be augmented even further by using Kafka for real-time data streaming between applications. Kafka is a distributed messaging system that collects, processes and organizes extensive amounts of data into groups known as topics. The information is fed to consumers, who act on it in real-time. How to deploy Kafka on Kubernetes? To deploy Kafka on Kubernetes, you can use Helm charts provided by Confluent, customizing the configuration as needed in values.yaml file and then install it with Helm. Ensure you have a running Kubernetes cluster and adequate persistent storage to support Kafka and ZooKeeper.
Organizations can use Kafka as an integrating component of their API ecosystems, enabling them to build and deploy real-time applications that are fast and reliable. However, installing Kafka on Kubernetes can be time-consuming and challenging for IT teams, especially when they have to set up the infrastructure and train existing staff to know how to administer it. Luckily, some tools simplify deploying and configuring Kafka on Kubernetes. For example, the CNCF-incubated Strimzi project provides different operators that make it easier to deploy Kafka clusters on Kubernetes. The Strimzi operators use custom resources and YAML to create, deploy, and manage Kafka clusters declaratively. Another great tool for deploying Kafka on Kubernetes is the open-source project Kafka-Svc. It offers a simple solution, allowing developers to connect their applications to Kafka brokers easily. This approach uses a single storage container, the “Kafka PersistentVolumeClaim,” shared between all Kafka pods in a StatefulSet. While it’s tempting to use a Kubernetes Service for this purpose, Services are load balancers. They would break how Kafka works by telling producers and consumers what the backend broker is through a publicly visible external IP.
Create a Kafka Cluster
Kafka is a message broker that allows you to create Real-Time Streaming Data Pipelines and Streaming Applications. It can take input from Data Producers (systems that transform data into a format ready for consumption) and distribute it to Data Consumers (systems that consume and manipulate data to produce alerts or insights). A Kafka cluster contains one or more brokers and uses a distributed, replicated log for storage. Each broker is responsible for one or more Topics, and each Topic is divided into Partitions. A Partition has a Leader who handles all writes and followers purely for failover purposes. Whenever the Leader becomes unavailable, one of the followers can become the new partition leader so that the cluster continues to function without interruption. When a client wants to connect to a Kafka cluster, it will first connect to a bootstrap server (one of the brokers). The bootstrapping server will return metadata to the client that will help it find other brokers in the cluster. Once the client sees the right broker, it can send or receive messages. As a Kafka cluster grows, it must be monitored and scaled appropriately. This is best done using the right tools and considering the options available for deployment. For example, many vendors offer managed Kafka services that can be deployed to your preferred cloud and can handle the cluster’s security, uptime and management for you.
Kafka is a platform that handles real-time data streams for many use cases. It provides a publish-subscribe messaging system with low latency message delivery and fault tolerance in the face of machine failures. Kafka also supports distributed read and write, making it a very scalable system. Kafka stores data in a distributed log called a Topic. Producer applications publish data to a Topic, and consumer applications read from that Topic. Each Topic is divided into several partitions that contain records in an unchangeable sequence, and each paper is assigned a unique offset within the log. Consumers advance the balance as they consume messages, and the number of destroyed records tracks the total consumption of a Topic. To provide high availability, a Kafka cluster includes multiple brokers. The number of brokers depends on your throughput and availability requirements. The group must have a minimum of two in-sync replicas for each Topic, and it is recommended to spread the brokers across different nodes and availability zones for resilience. We will deploy a three-broker Kafka cluster in a StatefulSet with a single PersistentVolumeClaim as the shared storage for demonstration purposes. We will also include a headless service called Kafka-svc, providing access to the Pod’s endpoints outside the cluster. In addition, the kafka-svc service will have clusterIP: None set, which tells Kubernetes that a Pod can be restarted without losing any data if its name is changed.
Kafka can handle massive data, making it an essential integrating component for your applications deployed in a Kubernetes cluster. However, the amount of data can quickly overwhelm the resources available on a single server. To handle this, you must implement scalability. Kafka combines publish-subscribe and queuing messaging technologies to enable a scalable, multi-subscriber, and real-time message delivery system. The key difference between Kafka and traditional queues is that Kafka allows multiple subscribers to consume a topic simultaneously, whereas a conventional queue is a one-to-one connection. Producers send data to a Kafka cluster through streams, which are collections of messages in chronological order. Producers also create topics, which categorize streams of messages into more manageable sets called partitions. Consumers subscribe to an issue and read from it using an offset, which identifies a particular record in the log. Kafka preserves the order of records in a topic, which gives it great reliability and scalability. A Kafka deployment’s scalability and high availability are achieved by having multiple brokers in the cluster. These brokers store copies of the data in various servers and locations to ensure that a node failure does not result in lost data. Additionally, Kafka can rebuild brokers if necessary at the cost of a lower I / O performance. Kubernetes provides automatic recovery of pods, and it can even restart Kafka brokers if they cannot process requests.