Distributed databases handle schema changes through strategies designed to maintain availability and consistency across multiple nodes. These systems typically use versioning, phased rollouts, and backward compatibility to minimize disruption. Unlike single-node databases, where schema updates can be applied atomically, distributed databases must coordinate changes across nodes that might be in different states or geographically dispersed. The goal is to ensure that applications continue functioning even as some nodes operate on older schema versions temporarily.
One common approach is schema versioning combined with phased rollouts. For example, when adding a column to a table, the database might first update the schema metadata in a backward-compatible way, allowing nodes running older software to ignore the new column. Systems like Apache Cassandra propagate schema changes asynchronously using a gossip protocol: nodes exchange schema versions and apply updates incrementally. During this process, writes and reads continue using the latest schema version each node understands. Another technique is using online schema migration tools, such as those in Google Spanner, which apply changes without downtime by leveraging atomic schema updates coordinated through a globally consistent timestamp. These methods ensure that nodes can operate independently during the transition while avoiding locks or downtime.
Challenges include handling partial failures and ensuring cross-node consistency. For instance, if a node crashes during a schema update, the system must detect and retry the change once the node recovers. Some databases use two-phase commit protocols or consensus algorithms like Raft to enforce agreement on schema changes. Additionally, backward compatibility is critical: applications must tolerate mixed schema versions during transitions. For example, a new optional field in a document database like MongoDB can be added without breaking existing queries. Tools like automatic rollback mechanisms or version checks help mitigate errors. By combining these strategies, distributed databases balance flexibility and reliability, enabling seamless schema evolution at scale.