How do streaming systems handle high availability?

Streaming systems achieve high availability through data replication, fault-tolerant architectures, and automatic recovery mechanisms. These systems distribute data across multiple nodes and maintain redundant copies to ensure continuous operation during hardware failures or network issues. By design, they minimize single points of failure and enable seamless failover between components.

A core strategy is partitioning and replicating data streams. For example, Apache Kafka divides topics into partitions, each hosted on a server (broker). Each partition has a leader handling read/write operations and followers that replicate the data. If the leader fails, Kafka automatically promotes a follower to leader, ensuring uninterrupted access. Similarly, Amazon Kinesis uses shards (similar to partitions) with replication across availability zones. This approach guarantees that even if an entire data center faces issues, the system continues processing streams using redundant copies.

Durability features like write-ahead logs and checkpoints further enhance availability. Systems such as Apache Flink use periodic checkpoints to persist processing state to durable storage (e.g., HDFS or S3). If a worker node fails, Flink restarts tasks on healthy nodes and reloads the latest checkpoint. Kafka stores messages in commit logs on disk and replicates them across brokers, preventing data loss. Consumers track their position using offsets, allowing them to resume processing from the last committed offset after failures. These mechanisms ensure data integrity and continuity even during partial outages.

Finally, streaming systems employ cluster managers and health monitoring for automatic recovery. Tools like Kubernetes or Apache ZooKeeper detect node failures and trigger rebalancing of partitions or tasks. For instance, if a Kafka broker goes offline, ZooKeeper coordinates leader election and updates metadata to redirect clients. Cloud-native services like Google Pub/Sub automate scaling and redundancy without manual intervention. Combined with load balancing and real-time health checks, these features enable streaming systems to self-heal, maintaining availability with minimal downtime or performance degradation.