Distributed databases ensure data durability primarily through a combination of data replication, consensus algorithms, and reliable storage mechanisms. Data durability means that once a transaction is committed, it will survive any subsequent failures, like server crashes or network issues. By replicating data across multiple nodes within the database cluster, distributed systems can withstand the loss of individual nodes. If one node goes down, other nodes can still provide the necessary data, ensuring continuous availability and durability.
One common method for achieving data durability is the use of consensus algorithms, such as Paxos or Raft. These algorithms help ensure that all replicas reach agreement before considering a transaction committed. For example, when a client writes data, the consensus algorithm requires multiple nodes to confirm the write. Only after a majority of nodes acknowledge the transaction does it become durable. This way, even if some nodes fail after the transaction is acknowledged, the data remains safe on other nodes that have persisted it.
In addition to replication and consensus, distributed databases often employ robust storage systems to further enhance durability. Various storage technologies, like write-ahead logging (WAL) and durable file systems, are used to ensure data is not lost even if a crash occurs just after a write operation. Write-ahead logging, for instance, records changes to a log before the actual data is written to the database. If a failure happens, the system can recover by replaying the log entries. By combining these strategies, distributed databases maintain a high level of data durability, ensuring that applications can rely on the integrity of their data over time.