How do distributed databases support high availability?

Distributed databases support high availability by replicating data across multiple nodes and ensuring the system remains operational even during hardware failures, network issues, or maintenance. This is achieved through techniques like data replication, partitioning, and consensus protocols. By spreading data across geographically dispersed servers, these systems minimize single points of failure and allow uninterrupted access even if some nodes go offline. For example, a distributed database might store copies of the same data in three different data centers, ensuring that if one fails, requests can be routed to the others.

One key method is data replication, where multiple copies of data are stored on different nodes. Systems like Apache Cassandra use a peer-to-peer architecture where every node can accept read and write requests, eliminating reliance on a single master node. If a node fails, other replicas continue serving requests. Replication strategies like synchronous or asynchronous writes balance consistency and availability. For instance, in a three-node cluster configured for synchronous replication, a write operation is confirmed only after all three nodes acknowledge it, ensuring strong consistency. Asynchronous replication, used in systems like Amazon DynamoDB, prioritizes availability by confirming writes once they reach a primary node, with replicas updated later, trading some consistency for lower latency.

Another approach is automatic failover and partitioning. Distributed databases often split data into shards (partitions) spread across nodes. If a shard becomes unavailable, the system redirects traffic to other replicas. Tools like MongoDB’s replica sets automatically promote a secondary node to primary if the original leader fails. Consensus algorithms like Raft or Paxos ensure nodes agree on data states during failures. For example, etcd uses Raft to elect a new leader if the current one becomes unresponsive, maintaining availability. Load balancing and health checks further enhance reliability by distributing traffic evenly and rerouting it from unhealthy nodes. This combination of replication, partitioning, and automated recovery ensures high availability even under adverse conditions.