Kafka is a popular distributed messaging system. It can handle large volumes of data with high availability.
A Kafka cluster comprises brokers that store events in files organized by topic and partition. This data can be retrieved at any time.
Kafka brokers are vulnerable to failures, so they need to be replicated. If necessary, Kubernetes can reduce the number of brokers, which could increase latency in your real-time data applications.
Install Kafka on Kubernetes
Streaming data in huge volumes can be handled via the distributed publish-subscribe messaging system known as Kafka. It is used by businesses to create streaming data pipelines that securely transfer data between applications and systems.
Kafka on Kubernetes provides the framework for deploying, scaling, and managing containerized applications. This allows your IT teams to deploy and manage Kafka clusters without installing them on every host machine.
It also means you can use the same tools and processes your IT team already uses for other platforms. This helps speed up the adoption of the platform and minimize downtime.
Container management is made easier with the aid of the open-source platform Kubernetes. It joins physical and virtual host machines into a cluster, providing greater processing power, storage capacity, and network capabilities than any single machine could have. A cluster can host one or more “pods,” each containing a single instance of an application.
Configure Kafka
Kafka is a distributed publish-subscribe messaging system that handles high-volume, real-time data streams. It acts as a central nervous system for microservice architectures, connecting services so they can send and receive messages.
Kafka clusters are designed to be highly available with replication and partition tolerance. Each broker in a Kafka cluster is deployed to multiple hosts within the same or different fault domains to ensure availability and scalability.
In addition, Kafka has built-in security features, including authentication and access control for operations. Moreover, it supports data encryption in-flight between producers and consumers using SSL or TLS.
In this tutorial, we deploy a local Kafka setup as a StatefulSet for testing and development purposes. We’ll use a default config with three brokers and three replicated partitions. Each Kafka broker will store its data in files organized by topic and partition. You can check the storage identity of a specific broker by looking at its Pod. The Pod name will be listed as clusterIP: None, and the PersistentVolumeClaim and PersistentVolumeSize will be displayed in the Kubernetes console under Dashboards (Kafka Monitoring) or in Prometheus Overview (General).
Create a Topic
Apache Kafka is a high-performance, distributed event stream platform. It allows producer applications to publish events and consumers to subscribe to them. The events are stored in topics, which contain information about the data. For example, an e-commerce application would create a topic for its orders.
To create a topic in Kafka, log in to the Event Streams UI using a supported web browser (see how to determine your login URL). Click Home in the primary navigation, then select the Create a Topic tile. Enter a topic name, for example, my-topic. Select the number of partitions. Partitions are used for scalability and distributing data across brokers.
For high availability, Kafka servers make multiple copies of each topic. This ensures that if one server fails, the data will be available on another. You can also define a replication factor for each topic.. You can specify additional configuration options for the topic at creation time. These options include message retention, partitions, and replicas.
Create a Consumer
Kafka provides a way for services within your cloud to communicate with each other via messages. These messages are organized and stored in topics. A typical event might be payment transactions, geolocation updates from mobile phones, shipping orders, or sensor measurements from IoT devices.
To consume these events, we need to create a consumer. The steps to do this will vary by your chosen development language, but the concepts are the same. First, we must create a consumer and configure it with its appropriate configurations (group id, prior offset, etc.).
Next, we must write some events to a topic using the console producer client. You can do this by typing a few lines into the terminal and watching the events appear in your consumer output.
When you’re done, remember to close() the consumer. This will close all the network connections and sockets and trigger a rebalance immediately (rather than waiting for the group coordinator to discover that the consumer has stopped sending heartbeats and is likely dead). The shutdown() method provides an atomic gate that can only be opened or closed from inside runways ().
Create a Producer
Kafka is a distributed messaging system that allows you to write applications that put data into topics and consume messages from those topics. This allows you to scale your application to any number of servers or brokers in a distributed manner without losing data.
Producers send event records to a topic using an asynchronous API. Each record is assigned a unique producer id and a sequence number that increases monotonically. The producer sends an operation and a message to the leader broker. The leader broker assigns the message to a partition based on a key specified by the producer (key = null) or determined automatically based on a key hash.
Consumers read from the topics they have subscribed to. They can be configured to start reading from the beginning or from a specific position, known as an offset. To improve throughput, the consumers can be configured to group into a consumer group that reads from multiple partitions of a topic simultaneously, thus increasing message throughput.
