Let’s think Kafka cluster without Zookeeper with KIP-500

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 us see what issues we have with the above setup with the involvement of Zookeeper.

  • Making the Zookeeper cluster highly available is an issue as without the Zookeeper cluster the Kafka cluster is DEAD.
  • Availability of the Kafka cluster if the controller dies. Electing another Kafka broker as a controller requires pulling the metadata from the Zookeeper which leads to the Kafka cluster unavailability. If the number of topics and the partitions is more per topic, the failover Kafka controller time increases.
  • Kafka supports intra-cluster replication to support higher availability and durability. There should be multiple replicas of a partition, each stored in a different broker. One of the replicas is designated as a leader and the rest of the replicas are followers. If a broker fails, partitions on that broker with a leader temporarily become inaccessible. To continue serving the client requests, Kafka will automatically transfer the leader of those inaccessible partitions to some other replicas. This process is done by the Kafka broker who is acting as a controller. The controller broker should get metadata from the Zookeeper for each of the affected partition. The communication between the controller broker and the Zookeeper happens in a serial manner which leads to unavailability of the partition if the leader broker dies.
  • When we delete or create a topic, the Kafka cluster needs to talk to Zookeeper to get the updated list of topics. To see the impact of topic deletion or creation with the Kafka cluster will take time.
  • The major issue we see is the SCALABILITY issue.

Let’s see how the Kafka cluster looks like post KIP-500. Below is the Kafka cluster setup.

If you look at the post KIP-500, the metadata is stored in the Kafka cluster itself. Consider that cluster as a controller cluster. The controller marked in orange color is an active controller and the other nodes are standby controllers. All the brokers in the cluster will be in sync. So, during the failure of the active controller node, electing the standby node as a controller is very quick as it doesn’t require syncing the metadata. The brokers in the Kafka cluster will periodically pull the metadata from the controller. This design means that when a new controller is elected, we never need to go through a lengthy metadata loading process.

Post KIP-500 will speed up the topic creation and deletion. Currently, the topic creation or deletion requires to get the full list of topics in the cluster from the Zookeeper metadata. Post KIP-500, just the entry needs to add to the metadata partition. This speeds up the topic creation and deletion. Post KIP-500, the metadata scalability increases which eventually improves the SCALABILITY of Kafka.

In the future, I want to see the elimination of the second Kafka cluster for controllers and eventually, we should be able to manage the metadata within the actual Kafka cluster. That reduces the burden on the infrastructure and the administrator’s job to the next level. We will meet with another topic. Until then, Happy Messaging!!


Siva Janapati is an Architect with experience in building Cloud Native Microservices architectures, Reactive Systems, Large scale distributed systems, and Serverless Systems. Siva has hands-on in architecture, design, and implementation of scalable systems using Cloud, Java, Go lang, Apache Kafka, Apache Solr, Spring, Spring Boot, Lightbend reactive tech stack, APIGEE edge & on-premise and other open-source, proprietary technologies. Expertise working with and building RESTful, GraphQL APIs. He has successfully delivered multiple applications in retail, telco, and financial services domains. He manages the GitHub(https://github.com/2013techsmarts) where he put the source code of his work related to his blog posts.

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Posted in Apache Kafka

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