What is the impact of latency on distributed database performance?

Latency directly impacts distributed database performance by increasing the time required for data operations and coordination between nodes. In a distributed system, nodes often communicate over networks to replicate data, maintain consistency, or execute transactions. High latency between nodes slows these interactions, leading to longer response times for queries and updates. For example, a write operation requiring acknowledgment from multiple replicas will take longer if network delays prevent nodes from confirming receipt quickly. This effect becomes more pronounced in globally distributed databases, where nodes in different regions may experience latency of 100ms or more due to physical distance.

Specific scenarios highlight latency’s impact. Consider a distributed database using synchronous replication for strong consistency: every write must be confirmed by all replicas before the operation completes. If one replica is in a high-latency region, the entire write operation stalls until its acknowledgment arrives. Similarly, distributed transactions relying on two-phase commit protocols suffer when coordination messages between nodes take longer to traverse the network. Even read operations can be affected—for instance, a query requiring data from multiple partitions might wait for slower nodes to respond, creating a bottleneck. In geo-partitioned systems, latency can also create “stale read” issues if local replicas aren’t updated quickly enough due to delayed synchronization.

To mitigate latency, developers often optimize data placement, reduce round trips, and relax consistency requirements. Placing frequently accessed data closer to users (via geo-sharding or edge caching) reduces cross-region communication. Techniques like batching operations or using asynchronous replication minimize the number of network calls. Choosing eventual consistency instead of strong consistency can also help—for example, allowing reads to return slightly stale data from a local replica instead of waiting for a globally consistent state. Additionally, protocols like QUIC or optimized TCP stacks can reduce network-layer overhead. Monitoring tools like latency heatmaps or tracing systems (e.g., OpenTelemetry) help identify bottlenecks to prioritize fixes, such as relocating a problematic node or adjusting timeout thresholds.