Let's Think Kafka Cluster Without Zookeeper With KIP-500. Managing a ZooKeeper cluster creates an additional burden on the infrastructure and the admins. With KIP-500, we are going to see a Kafka cluster without the ZooKeeper cluster where the metadata management will be done with Kafka itself.
Right now, Apache Kafka utilizes Apache ZooKeeper to store its metadata. Information such as the partitions, configuration of topics, access control lists, etc. metadata stored in a ZooKeeper cluster. Managing a ZooKeeper cluster creates an additional burden on the infrastructure and the admins. With KIP-500, we are going to see a Kafka cluster without the ZooKeeper cluster where the metadata management will be done with Kafka itself.
Before KIP-500, our Kafka setup looks like depicted below. Here we have a 3 node ZooKeeper cluster and a 4 node Kafka cluster. This setup is a minimum for sustaining 1 Kafka broker failure. The orange Kafka node is a controller node.
Let's see what issues we have with the above setup with the involvement of ZooKeeper:
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The objective of this blog is to build some more understanding of Apache Kafka concepts such as Topics, Partitions, Consumer, and Consumer Groups. Kafka's basic concepts have been covered in my previous article. Kafka Topic & Partitions As we know, messages in Kafka are categorized or stored inside Topics. In simple terms, Topic can be construed as a Database table.
This Apache Kafka Tutorial - Kafka Tutorial for Beginners will help you understand what is Apache Kafka & its features. It covers different components of Apache Kafka & it’s architecture. You'll learn: What is Kafka? Kafka Features, Kafka Components, Kafka architecture, Installing Kafka, Working with Single Node Single Broker Cluster
I have kept this blog as short as possible on two commonly used ways of how to use Kafka Producer-Consumer processes over single node…Prerequisite: Ubuntu 18.04 server and a non-root user with sudo privileges. At least 4GB of RAM is required on the server. Installation without this amount of RAM may cause the Kafka service to fail, with the Java Virtual Machine(JVM) throwing out an “Out Of Memory” exception during startup. Even using Docker services one need to make sure the host machine has more than 4GB of RAM (advisable 8 GB RAM) as it is an absolute requirement for Kafka will consume a big part of RAM.
Integration and transformation between XML Messages and Apache Kafka, including Kafka Connect Connector, Middleware, ESB, ETL, SOAP/WSDL.