Distributed databases manage concurrent reads and writes through a combination of consistency models, coordination protocols, and conflict resolution strategies. These systems aim to balance data availability, consistency, and partition tolerance while handling multiple operations across nodes. Key approaches include using timestamps, versioning, and consensus algorithms to order operations and resolve conflicts. For example, a write might be assigned a timestamp to determine its priority, while reads may check multiple nodes to ensure they return the most recent data.
One common method is the use of quorum-based systems, where a minimum number of nodes (a “quorum”) must acknowledge a read or write operation for it to succeed. For instance, in a system with a replication factor of 3, a write might require confirmation from 2 nodes (a write quorum), and a read might check 2 nodes (a read quorum) to ensure consistency. This prevents stale data from being returned if some nodes are outdated. Databases like Apache Cassandra use tunable consistency levels, allowing developers to adjust quorum requirements based on their needs for speed versus accuracy. Another approach is multi-version concurrency control (MVCC), where data is stored with version numbers or timestamps. Reads access a snapshot of the database at a specific time, avoiding conflicts with ongoing writes. PostgreSQL’s distributed extensions like Citus use MVCC to isolate transactions and maintain consistency across shards.
For write conflicts, distributed databases often rely on conflict-free replicated data types (CRDTs) or application-defined resolution logic. CRDTs ensure that concurrent updates to the same data (e.g., incrementing a counter) can be merged automatically without data loss. Platforms like Redis Enterprise use CRDTs for active-active replication. When automatic resolution isn’t possible, systems like Google Spanner use synchronized clocks (via TrueTime API) and two-phase commit protocols to serialize transactions globally. Developers can also implement application-level conflict handlers, such as “last write wins” (based on timestamps) or custom merge logic for complex data structures. These techniques collectively enable distributed databases to handle concurrency while maintaining performance and reliability.