Distributed databases maintain data integrity through mechanisms that ensure data remains accurate, consistent, and reliable across multiple nodes. These systems face challenges like network partitions, node failures, and concurrent updates, so they rely on protocols and algorithms to coordinate operations and resolve conflicts. Key strategies include ACID transactions, consensus algorithms, and conflict resolution techniques, all working together to enforce rules that prevent data corruption or inconsistencies.
One foundational approach is the use of ACID (Atomicity, Consistency, Isolation, Durability) transactions adapted for distributed environments. For example, two-phase commit (2PC) ensures atomicity by requiring all participating nodes to agree on committing or aborting a transaction. In 2PC, a coordinator node first asks all nodes to prepare for a transaction. If all agree, the coordinator instructs them to commit; otherwise, it aborts. While 2PC guarantees atomicity, it can suffer from blocking if the coordinator fails. To address this, some systems use optimizations like three-phase commit or rely on consensus algorithms such as Paxos or Raft. Google Spanner, for instance, combines 2PC with a globally synchronized clock (TrueTime) to manage distributed transactions efficiently while maintaining strong consistency.
Consensus algorithms and quorum-based systems are another critical layer. Algorithms like Raft or Paxos ensure nodes agree on the order of operations, preventing split-brain scenarios. For example, Raft elects a leader to coordinate writes, ensuring all replicas apply changes in the same order. Quorum systems, like those in Apache Cassandra, require a majority of nodes (e.g., a quorum of 3 out of 5 replicas) to acknowledge a write before it’s confirmed. This ensures consistency even if some nodes are unavailable. DynamoDB uses a similar approach, allowing developers to tune consistency levels based on read/write quorums. These mechanisms balance availability and consistency, adhering to the CAP theorem’s trade-offs while minimizing data divergence.
Finally, conflict resolution and replication strategies handle scenarios where concurrent updates create inconsistencies. Version vectors or logical clocks track data versions across nodes, allowing systems to detect conflicts. For example, Amazon DynamoDB uses vector clocks to identify conflicting writes, leaving resolution to application logic. Conflict-free Replicated Data Types (CRDTs) take this further by enabling automatic merging of updates, such as counters that can be safely incremented across nodes. CouchDB employs a “multi-version concurrency control” (MVCC) approach, storing document revisions and letting clients resolve conflicts during synchronization. These methods ensure eventual consistency while providing tools to reconcile differences without manual intervention.