How do distributed databases ensure fault tolerance?

Distributed databases ensure fault tolerance by replicating data across multiple nodes and implementing mechanisms to handle failures without losing data or availability. The core idea is that no single point of failure can disrupt the entire system. For example, if a server crashes or a network partition occurs, the database continues operating by relying on redundant copies of data stored on other nodes. This redundancy is often combined with consensus protocols to maintain consistency, ensuring all nodes agree on the state of the data even during disruptions.

One common approach is using replication strategies like leader-follower or multi-leader architectures. In a leader-follower setup, writes are sent to a primary node (leader) and propagated to secondary nodes (followers). If the leader fails, a follower can be promoted to take its place. Systems like Apache Cassandra use a peer-to-peer model where all nodes are equal, allowing writes to any node and propagating changes asynchronously. For stricter consistency, databases like Google Spanner use synchronous replication with the Paxos or Raft consensus algorithms. These protocols require a majority of nodes to acknowledge writes before committing them, ensuring data survives individual node failures. For instance, Raft ensures that a write is only considered successful once replicated to a quorum of nodes, preventing data loss if one node goes offline.

Another layer of fault tolerance comes from automatic failure detection and recovery. Distributed databases use health checks (e.g., heartbeats) to monitor node status. If a node becomes unresponsive, the system redirects traffic to healthy replicas. For example, Amazon DynamoDB automatically partitions data and replicates each partition across multiple availability zones. If a zone fails, traffic shifts to replicas in other zones. Some systems also employ self-healing mechanisms, like Apache Kafka’s ability to rebalance partitions or CockroachDB’s re-replication of data from failed nodes to new ones. Additionally, techniques like checksums and versioning help detect and correct data corruption. By combining redundancy, consensus, and automated recovery, distributed databases minimize downtime and data loss even in unpredictable environments.