Distributed databases ensure data durability by replicating data across multiple nodes and employing mechanisms to confirm writes even when failures occur. Durability means data survives hardware crashes, network issues, or other disruptions. This is achieved through a combination of replication strategies, consensus protocols, and failure recovery processes. By spreading data across geographically dispersed nodes, these systems reduce the risk of data loss and ensure that committed transactions persist despite partial system failures.
One common approach is synchronous replication with a quorum. For example, Apache Cassandra allows users to configure a replication factor (e.g., three copies of data) and a consistency level (e.g., requiring acknowledgments from two nodes for writes). This ensures data is stored on multiple nodes before confirming success. Similarly, Amazon DynamoDB uses synchronous replication across multiple Availability Zones (AZs), writing updates to a majority of replicas within a region. Another key mechanism is the write-ahead log (WAL), used in systems like CockroachDB. Every write is first recorded to a durable log on disk, even before applying it to the database. This log is replicated across nodes, ensuring that if a node crashes, the data can be recovered from the log on surviving nodes.
To handle failures, distributed databases use automatic repair and consistency checks. For instance, Cassandra employs “hinted handoffs” to temporarily store writes intended for unreachable nodes, then forwards them once the node recovers. Systems like Riak use “anti-entropy” protocols with Merkle trees to efficiently detect and reconcile data inconsistencies between replicas. Additionally, many databases implement graceful degradation during network partitions. For example, a quorum-based system might allow reads and writes to continue with available nodes, then resolve conflicts once connectivity is restored. These layers of redundancy and recovery ensure that even during outages, data remains durable and eventually consistent across the system.