Migrating from Zookeeper to Kraft Mode in Apache Kafka: Understanding KRaft and its Benefits

6 min readApr 4, 2024

What is Kafka?

Apache Kafka is a high-throughput, distributed streaming platform for handling real-time data pipelines. It ingests, stores, and processes streams of messages, enabling various applications like data analytics, log aggregation, and microservices communication.

What was Zookeeper’s Role in Kafka?

Traditionally, Kafka relied on Zookeeper for crucial metadata management tasks like:

Leader election: Selecting the leader broker in a multi-broker cluster.
Topic configuration: Storing information about topics, partitions, and replicas.
Consumer offsets: Tracking the message position consumers have already processed.

While Zookeeper served these purposes well initially, it presented some limitations:

Single point of failure: If Zookeeper goes down, the entire Kafka cluster becomes unavailable.
Scalability constraints: Zookeeper struggles to manage large deployments efficiently.
Increased complexity: Maintaining and operating Zookeeper adds another layer of overhead.

Introducing KRaft: Enhanced Metadata Management for Kafka

KRaft, also known as Kafka Raft Metadata mode, was introduced as a replacement for Zookeeper, addressing its limitations and improving Kafka’s overall architecture. KRaft leverages the Raft consensus algorithm, a distributed protocol ensuring:

High availability: No single point of failure, as the cluster can function even if some nodes are unavailable.
Enhanced scalability: KRaft scales better with larger deployments compared to Zookeeper.
Simplified architecture: KRaft eliminates the need for a separate Zookeeper service, streamlining the Kafka ecosystem.

Components of Kafka with KRaft And its Architecture and Workflow: Orchestrating Metadata Management

With KRaft mode, Kafka adopts a refined architecture for managing metadata, replacing Zookeeper with a more resilient and scalable approach. Here’s a breakdown of the key components and their interactions, accompanied by helpful reference images:

1. Kafka Brokers:

The workhorses of the cluster, are responsible for receiving, storing, and forwarding messages.
Each broker maintains a replica of the topic partitions it hosts.
Brokers communicate with controllers and each other for data exchange and cluster coordination.

2. Controller Ensemble:

A group of specialized Kafka servers forming the core of KRaft mode.
They collectively manage metadata like topics, partitions, replicas, and consumer offsets.
The ensemble operates using the Raft consensus algorithm to ensure consistency and high availability.

3. Raft Algorithm:

The distributed consensus protocol powering KRaft, guaranteeing agreement on the current state of the metadata across controllers.
Involves leader election, vote exchange, and replication mechanisms for consistency and fault tolerance

4. Leader Controller:

One member of the ensemble elected through the Raft algorithm to process metadata updates and replicate them to other controllers.
Acts as the central point for handling metadata changes and coordinates with brokers.

5. Follower Controllers:

Members of the ensemble that keep their local metadata state synchronized with the leader.
Ready to take over as leader if the current leader fails, ensuring high availability.

6. Event-Sourced Metadata:

KRaft stores metadata as a sequence of events, allowing for transparent tracking of changes and simplified debugging.
Enables efficient state reconstruction if needed.

7. Kafka Clients (Producers and Consumers):

Applications interacting with Kafka to produce or consume messages.
Connect to brokers to send/receive messages and interact with metadata as needed.

Additional Notes:

This diagram showcases the core components; a production environment might have additional tools for monitoring, security, and management.
The specific number of controller nodes in the ensemble depends on your desired level of fault tolerance and scalability.
Quorum Controllers: These specialized Kafka services form the heart of KRaft, responsible for replicating and storing metadata across the cluster.
Raft Algorithm: This distributed consensus protocol guarantees consistency and agreement among controllers on the current state of the metadata.
Event-Sourcing: KRaft stores metadata as a sequence of events, allowing for efficient state reconstruction and easier debugging.
Leadership and Replication: One controller acts as the leader, receiving metadata updates and replicating them to followers. Each controller maintains a complete copy of the metadata for redundancy.

Workflow of Topic Creation in Kraft

Imagine a new topic creation request:

The request arrives at a Kafka broker.
The broker forwards it to the leader controller.
The leader validates the request and proposes it to other controllers.
Controllers reach consensus using the Raft algorithm, ensuring consistency.
Upon agreement, the leader applies the change to its local state and replicates it to followers.
After successful replication, the leader informs the broker about the successful topic creation.

Workflow of Controller Leader Election in Kraft

The leader election process in KRaft, powering Kafka’s metadata management, plays a crucial role in ensuring the cluster’s smooth operation and fault tolerance. Here’s a breakdown of this intriguing workflow:

Triggering a Leader Election:

A leader election can be initiated in several scenarios:
Startup: When Kafka brokers start, they initiate an election to determine the initial leader.
Leader failure: If the current leader becomes unavailable, a new election is triggered to choose a successor.
Partition rebalance: During the rebalancing of partitions to maintain consistency, a leader election might occur for specific partitions.

Raft Algorithm in Action:

Nominating a Candidate: Each controller nominates itself or another eligible controller as the leader based on pre-defined rules like the current leader’s term and voting weight.
Vote Exchange: Controllers exchange votes for their nominated candidates. A candidate needs a majority (quorum) of votes to become the leader.
Term Advancement: If no candidate receives a majority in the current term, the term is incremented, and controllers repeat the nomination and vote exchange process.
Leadership Established: Once a candidate secures a majority of votes in a given term, it becomes the new leader. Other controllers transition to follower roles, replicating data from the leader and remaining ready to participate in future elections.

Additional Considerations:

Preventing Split Brain: The quorum requirement ensures consistency and avoids electing multiple leaders simultaneously.
Leader Responsibilities: The leader processes metadata updates, replicates them to followers, and coordinates with brokers to maintain cluster state.
Follower Responsibilities: Followers keep their local state synchronized with the leader and are prepared to take over as leader if necessary.
Election Timeouts: Timeouts are used to bound the election process and prevent indefinite waiting in case of network issues or other delays.

Benefits of KRaft Leader Election:

High Availability: Ensures a new leader is quickly elected after failures, minimizing downtime and maintaining cluster function.
Fault Tolerance: Distributes leadership across controllers, eliminating single points of failure.
Scalability: The election process adapts to changes in the cluster size and controller availability.

Why Migrate from Zookeeper to KRaft?

Migrating your Kafka cluster from Zookeeper to KRaft offers several benefits:

Improved Availability: Eliminate the single point of failure risk associated with Zookeeper, ensuring higher uptime and resilience.
Enhanced Scalability: Easily scale your Kafka cluster to handle growing data volumes and message throughput without performance bottlenecks.
Simplified Operations: Reduce operational complexity by removing the need to manage and maintain Zookeeper alongside Kafka.
Streamlined Architecture: Enjoy a more unified and cleaner Kafka setup with all metadata management handled within the Kafka framework.

Conclusion

While Zookeeper served its purpose in the early stages of Kafka’s development, KRaft represents a significant advancement in terms of availability, scalability, and operational efficiency. Migrating your Kafka cluster to KRaft mode is a recommended step for future-proofing your infrastructure and unlocking the full potential of this powerful streaming platform.