Why Apache Kafka got rid of Zookeeper?
Autonomous Distributed Systems are better than Convoluted ones :)
Apache Kafka, the distributed event streaming platform, has undergone a significant architectural change in recent years. One of the most notable shifts has been the removal of Apache Zookeeper, a long-standing component in Kafka's ecosystem. This article delves into the reasons behind this decision, its implementation, and the implications for Kafka users and the broader tech community.
What is Apache Kafka and Zookeeper?
Kafka is a distributed event streaming platform capable of handling trillions of events a day. Initially developed by LinkedIn, Kafka is now an open-source project used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
Apache Zookeeper, on the other hand, is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services.
Zookeeper's Role in Kafka's Architecture
In the original Kafka architecture, Zookeeper played several crucial roles:
1. Broker Management: It maintained a list of all brokers in the cluster.
2. Topic Configuration: It stored configurations for all topics, including the number of partitions for each.
3. Access Control Lists (ACLs): It stored ACLs for all topics.
4. Leader Election: It facilitated the process of electing partition leaders among the brokers.
5. Cluster Membership: It detected broker failures and helped in the process of adding or removing brokers from the cluster.
Despite its usefulness, the dependency on Zookeeper introduced complexity and potential performance bottlenecks, which ultimately led to the decision to remove it from Kafka's architecture.
Reasons for Removing Zookeeper
Several factors contributed to the decision to remove Zookeeper from Kafka:
1. Simplification of Architecture: Running and maintaining two distributed systems (Kafka and Zookeeper) added operational complexity.
2. Performance Improvements: Zookeeper could become a bottleneck, especially in large clusters with thousands of brokers.
3. Scalability: Zookeeper's limitations in terms of the number of concurrent connections it could handle were becoming apparent as Kafka clusters grew larger.
4. Consistency Model: Kafka's consistency model differed from Zookeeper's, leading to potential issues in certain edge cases.
5. Security: Having two systems increased the attack surface and complicated security setups.
6. Expertise Requirements: Operators needed to be proficient in both Kafka and Zookeeper, increasing the learning curve.
The KIP that Initiated the Change
The process of removing Zookeeper from Kafka was formally proposed in Kafka Improvement Proposal (KIP) 500, titled "Replace ZooKeeper with a Self-Managed Metadata Quorum." This KIP was created by Jason Gustafson and posted on September 30, 2019.
The proposal outlined a plan to replace Zookeeper with a metadata quorum built into Kafka itself, using a consensus protocol similar to Raft. This would allow Kafka to manage its own cluster metadata internally, eliminating the need for an external system like Zookeeper.
The transition has been gradual, allowing users to migrate at their own pace while ensuring stability and backwards compatibility.
Benefits of the New Kafka Architecture
The removal of Zookeeper and the introduction of KRaft have brought several benefits:
1. Simplified Operations: With only one system to manage, operations have become more straightforward.
2. Improved Performance: Early benchmarks have shown improvements in broker start-up times and metadata operations.
3. Enhanced Scalability: The new architecture allows for larger clusters, potentially supporting hundreds of thousands of partitions.
4. Better Security: A single security model for the entire system has simplified security configurations.
6. Unified Consistency Model: The new architecture ensures a consistent model throughout the system.
Performance So Far
Early adopters and benchmarks have reported positive results:
- Startup Time: Broker startup times have significantly decreased, especially in large clusters.
- Metadata Operations: Operations like creating topics and altering partitions have become faster.
- Scalability: Users have successfully run clusters with more partitions than were practical with Zookeeper.
- Stability: Despite being a major architectural change, the KRaft mode has proven to be stable in production environments.
However, it's important to note that performance can vary depending on specific use cases and configurations. The Kafka community continues to optimize and improve the KRaft implementation.
What This Means for Kafka Users and the Future
For Kafka users, the removal of Zookeeper represents a significant but positive change:
1. Migration Planning: Users should start planning their migration to KRaft, although Zookeeper support will be maintained for some time.
2. Simplified Deployments: New Kafka deployments can be set up more easily without the need to configure and maintain Zookeeper.
3. Potential for Larger Clusters: Users can explore scaling their clusters to sizes that were previously impractical.
4. Continued Improvements: As the community gains more experience with KRaft, further optimizations and features are likely to be developed.
Looking to the future, the removal of Zookeeper paves the way for Kafka to continue evolving. It opens up possibilities for new features that were difficult or impossible to implement in the old architecture. The Kafka community is likely to focus on further improving scalability, performance, and ease of use in upcoming releases.
Conclusion
The decision to remove Zookeeper from Apache Kafka's architecture marks a significant milestone in the project's evolution. While it presented challenges, the benefits in terms of simplification, performance, and scalability make it a worthwhile transition. As Kafka continues to adapt to the growing demands of modern data infrastructures, this change positions it well for future innovations in the world of distributed systems and event streaming.