How do distributed databases handle failures?

Distributed databases handle failures through redundancy, consensus protocols, and automated recovery mechanisms. Since data is spread across multiple nodes or data centers, the system is designed to continue operating even if individual components fail. Key strategies include replicating data, ensuring agreement among nodes during outages, and restoring consistency after failures. These approaches prioritize availability and durability while minimizing downtime or data loss.

One core method is data replication. Distributed databases like Apache Cassandra or Amazon DynamoDB store copies of data across multiple nodes. If a node fails, requests are redirected to replicas, ensuring uninterrupted access. For example, Cassandra uses a tunable consistency model: a write operation can succeed once a majority of replicas acknowledge it, allowing the system to tolerate temporary node outages. Similarly, leader-follower architectures (used in MongoDB replica sets) automatically promote a healthy follower to leader if the primary node fails. This redundancy ensures that even during hardware failures or network partitions, the database remains available for reads and writes.

Consensus protocols like Raft or Paxos help nodes agree on the state of the system during failures. For instance, etcd uses the Raft protocol to elect a new leader if the current one becomes unresponsive, ensuring continuous write operations. These protocols also prevent split-brain scenarios (where two nodes act as leaders) by requiring a quorum of nodes to validate decisions. Additionally, distributed databases employ heartbeat mechanisms to detect node failures quickly. If a node stops responding, the system marks it as offline and reroutes traffic. Some databases, like Google Spanner, combine these techniques with synchronized clocks to maintain global consistency even during regional outages.

After a failure is resolved, the database must recover lost data and synchronize nodes. Write-ahead logging (WAL) is commonly used: every change is logged before being applied, so failed nodes can replay logs to catch up. For example, PostgreSQL uses WAL to restore data after crashes. In eventual consistency models, version vectors or conflict-free replicated data types (CRDTs) resolve discrepancies. Amazon Dynamo employs vector clocks to track data versions, letting applications merge conflicting updates logically. Automated repair processes, such as Cassandra’s anti-entropy repair, compare data across replicas and fix inconsistencies. By combining these methods, distributed databases ensure data integrity and minimize manual intervention during failures.