Understanding KRaft Mode in Kafka

Learn how Apache Kafka's KRaft mode eliminates ZooKeeper dependency through a native consensus protocol, simplifying operations and improving scalability for modern streaming architectures.

Understanding KRaft Mode in Kafka

Apache Kafka has undergone one of its most significant architectural changes since its inception: the introduction of KRaft mode. This transformation removes Kafka's long-standing dependency on Apache ZooKeeper, replacing it with a native consensus protocol built directly into Kafka. Understanding KRaft is essential for anyone working with modern Kafka deployments.

What is KRaft?

KRaft (Kafka Raft) is Apache Kafka's implementation of the Raft consensus algorithm for managing cluster metadata. Introduced through KIP-500 and declared production-ready in Kafka 3.3.1, KRaft fundamentally changes how Kafka handles critical cluster information like topic configurations, partition assignments, access control lists, and broker metadata.

In traditional Kafka deployments, this metadata lived in ZooKeeper, a separate distributed coordination service. With KRaft, Kafka brokers themselves manage this metadata using a quorum-based approach, eliminating the need for an external dependency.

The name "KRaft" combines "Kafka" with "Raft," the consensus algorithm developed by Diego Ongaro and John Ousterhout at Stanford University. Raft was designed to be more understandable than alternatives like Paxos while providing the same strong consistency guarantees.

The ZooKeeper Problem

For over a decade, Apache ZooKeeper served as Kafka's metadata store and coordination layer. While this architecture worked, it introduced several operational and technical challenges.

Operational Complexity: Running a production Kafka cluster meant operating two separate distributed systems—Kafka and ZooKeeper. Each had its own configuration, monitoring requirements, upgrade cycles, and failure modes. Teams needed expertise in both systems, and troubleshooting issues often required understanding complex interactions between them.

Scalability Limitations: ZooKeeper wasn't designed for Kafka's specific workload patterns. As Kafka clusters grew to support hundreds of thousands of partitions, ZooKeeper became a bottleneck. The metadata size grew, and operations like partition reassignments or leader elections could take minutes instead of seconds.

Metadata Propagation Delays: In the ZooKeeper model, metadata changes followed a complex path: written to ZooKeeper, then read by the controller, then propagated to other brokers. This multi-hop process introduced latency and created windows where different parts of the cluster had inconsistent views of metadata.

A concrete example: creating a topic with 1,000 partitions in a ZooKeeper-based cluster could take 10-15 seconds as metadata was written to ZooKeeper and then propagated to all brokers. During this window, producers and consumers couldn't reliably interact with the new topic.

How KRaft Works

KRaft replaces ZooKeeper with a Raft-based quorum controller running within Kafka itself. Instead of storing metadata in an external system, KRaft maintains it in a special internal Kafka topic called __cluster_metadata.

Traditional Kafka with ZooKeeper:
┌─────────────────────────────────────────────────────────────┐
│                      ZooKeeper Ensemble                      │
│              (External coordination service)                 │
│        Stores: Metadata, Config, ACLs, Partition Info       │
└──────────────────────┬──────────────────────────────────────┘
                       │ Metadata read/write
          ┌────────────┼────────────┐
          │            │            │
          ▼            ▼            ▼
    ┌─────────┐  ┌─────────┐  ┌─────────┐
    │ Broker  │  │ Broker  │  │ Broker  │
    │    1    │  │    2    │  │    3    │
    └─────────┘  └─────────┘  └─────────┘

KRaft Mode (No ZooKeeper):
┌─────────────────────────────────────────────────────────────┐
│                    Kafka Cluster                             │
│  ┌──────────────────────────────────────────────────────┐   │
│  │          Controller Quorum (KRaft)                   │   │
│  │  ┌──────────┐  ┌──────────┐  ┌──────────┐           │   │
│  │  │Controller│  │Controller│  │Controller│           │   │
│  │  │    1     │  │    2     │  │    3     │           │   │
│  │  │ (Leader) │  │(Follower)│  │(Follower)│           │   │
│  │  └────┬─────┘  └────┬─────┘  └────┬─────┘           │   │
│  │       │             │             │                  │   │
│  │       └─────────────┼─────────────┘                  │   │
│  │              __cluster_metadata topic                │   │
│  └──────────────────────┬───────────────────────────────┘   │
│                         │ Metadata consumption              │
│          ┌──────────────┼──────────────┐                    │
│          │              │              │                    │
│          ▼              ▼              ▼                    │
│    ┌─────────┐    ┌─────────┐    ┌─────────┐              │
│    │ Broker  │    │ Broker  │    │ Broker  │              │
│    │    1    │    │    2    │    │    3    │              │
│    └─────────┘    └─────────┘    └─────────┘              │
└─────────────────────────────────────────────────────────────┘

The Quorum Controller: In a KRaft cluster, a subset of brokers are designated as controllers. These controllers form a Raft quorum, electing one controller as the active leader. The leader handles all metadata changes, appending them to the metadata log. Follower controllers replicate this log to maintain redundancy.

Event-Driven Metadata: Unlike ZooKeeper's tree-based data structure, KRaft treats metadata as an event log. Every metadata change—creating a topic, changing a configuration, reassigning partitions—becomes an event in the metadata log. Brokers consume this log to build their view of cluster state, similar to how they handle regular Kafka topics.

Leader Election: When the active controller fails, the remaining controllers use Raft's leader election protocol to choose a new leader automatically. This process typically completes in milliseconds, minimizing the window where metadata changes are unavailable.

Consider the same topic creation example: with KRaft, creating 1,000 partitions takes 1-2 seconds. The metadata change is written once to the metadata log and immediately available to all brokers consuming that log.

Key Benefits of KRaft

The shift to KRaft delivers several tangible improvements for Kafka operations.

Simplified Architecture: A KRaft cluster is just Kafka. No separate ZooKeeper ensemble to deploy, configure, monitor, or upgrade. This reduces infrastructure footprint and operational overhead. For development environments, you can run a single-node Kafka cluster without any external dependencies.

Improved Scalability: KRaft supports clusters with millions of partitions—far beyond what was practical with ZooKeeper. The metadata log scales horizontally, and metadata propagation is faster and more efficient.

Faster Metadata Operations: Controller failover times drop from seconds to milliseconds. Metadata changes propagate faster because brokers consume the metadata log directly rather than polling ZooKeeper.

Better Observability: Since metadata is stored in a Kafka topic, you can use standard Kafka tools to inspect, monitor, and even replay metadata changes. Streaming management platforms can visualize the metadata topic, making it easier to understand cluster state and troubleshoot issues.

Migration Considerations

While new Kafka deployments should use KRaft by default, existing ZooKeeper-based clusters require careful migration planning.

Migration Approaches: Kafka 3.4 and later support a bridge mode that allows migration from ZooKeeper to KRaft without downtime. This involves running both systems temporarily while metadata is transferred, then switching over to KRaft-only mode.

Testing Requirements: Before migrating production clusters, thoroughly test in non-production environments. Validate that client applications, monitoring tools, and operational procedures work correctly with KRaft. Some older management tools may not fully support KRaft clusters.

Timing: The Apache Kafka community has announced that ZooKeeper support will be removed in Kafka 4.0. Organizations should plan their migrations accordingly, balancing the urgency of moving to KRaft with the need for careful validation.

Monitoring During Migration: During the transition, monitor both systems closely. Streaming management tools can help track metadata consistency and identify any discrepancies between ZooKeeper and KRaft views of cluster state.

KRaft in Data Streaming Ecosystems

KRaft's impact extends beyond Kafka itself, affecting the broader data streaming landscape.

Stream Processing Frameworks: Tools like Apache Flink, Kafka Streams, and Spark Structured Streaming rely on Kafka for reliable, scalable event streaming. KRaft's improved scalability and faster metadata operations enable larger streaming deployments with more topics, partitions, and consumer groups.

Infrastructure as Code: KRaft's simplified architecture makes Kafka easier to deploy via infrastructure-as-code tools like Terraform, Kubernetes operators, or Ansible. Fewer components mean simpler deployment templates and less configuration drift.

Cloud-Native Deployments: In containerized environments, KRaft reduces resource requirements and simplifies orchestration. Kubernetes-based Kafka deployments benefit from not needing to manage separate ZooKeeper StatefulSets.

Ecosystem Tools: The Kafka ecosystem—schema registries, connectors, monitoring tools—continues to work with KRaft clusters. However, administrators should verify that their specific tools support KRaft mode, especially for older or less-maintained projects.

The shift to KRaft represents Kafka's maturation as a platform, making it more aligned with modern cloud-native and streaming-first architectures.

Summary

KRaft mode represents a foundational architectural shift in Apache Kafka, replacing ZooKeeper with a native Raft-based consensus protocol. This change simplifies operations by eliminating an external dependency, improves scalability to support millions of partitions, and accelerates metadata operations through event-driven architecture.

For new Kafka deployments, KRaft is the clear choice. For existing ZooKeeper-based clusters, the upcoming removal of ZooKeeper in Kafka 4.0 makes migration planning essential. Understanding KRaft's architecture, benefits, and migration paths prepares you to operate Kafka effectively in modern streaming environments.

As data streaming becomes increasingly central to enterprise architectures, KRaft positions Kafka for the next decade of growth, enabling simpler operations and larger-scale deployments.