How did Kafka achieve horizontal scalability?

5 months ago

2 minutes

Several aspects are involved in Kafka’s implementation of horizontal scalability.

Partitioning mechanism: Kafka stores data in multiple partitions, each partition can be replicated on different brokers, achieving horizontal scalability. By partitioning the data, it can be distributed across multiple nodes, increasing system throughput and capacity.
Multiple Replication Mechanism: Kafka supports a multiple replication mechanism where data can be replicated and stored across multiple brokers to enhance data availability and fault tolerance. By replicating data across multiple copies, it helps to prevent single point of failures and achieve high availability.
Horizontal scaling: Kafka supports horizontal scaling, allowing for increased system capacity and performance by adding new broker nodes. These new broker nodes can join the cluster, automatically rebalancing and replicating data to achieve system scalability.
Distributed architecture: Kafka is a distributed system that can scale its capacity and performance by adding new broker nodes. Kafka’s distributed architecture allows for distributed storage and processing of data, enabling horizontal scalability of the system.

Overall, Kafka achieves horizontal scalability through its partition mechanism, multiple replicas, horizontal scaling, and distributed architecture, enabling distributed storage and processing of data to enhance system scalability and performance.