Distributed databases ensure data availability during system failures through several strategies, including data replication, partitioning, and employing consensus algorithms. Each of these techniques contributes to maintaining access to data even when some parts of the system go down. When a failure occurs, the system can still operate because it has copies of the data stored across multiple locations or nodes.
One common approach is data replication, where the same data is stored in multiple nodes across the network. For instance, if a user requests access to a particular piece of data, the system can retrieve it from any of the available replicas rather than relying on a single source. This redundancy means that if one node fails, others can still serve data requests, ensuring high availability. Many distributed databases, like Apache Cassandra or MongoDB, use this replication mechanism to provide fault tolerance and maintain performance.
Another technique is partitioning or sharding, where the database is divided into smaller, more manageable pieces that can be distributed across different nodes. This way, if one shard becomes unavailable due to a failure, the rest of the system can still function normally. Additionally, consensus algorithms like Raft or Paxos help ensure that updates to the database are correctly recorded and that nodes reach agreement on the current state of the database, even if some nodes are unreachable. By combining these methods, distributed databases can effectively manage system failures, providing a reliable experience for users and applications that depend on them.