Distributed databases handle time synchronization through a combination of logical clocks and physical clock coordination to maintain consistency across nodes. Since distributed systems can’t rely on a single global clock due to network latency and hardware differences, they use mechanisms like Lamport timestamps, vector clocks, or protocols like Network Time Protocol (NTP) to approximate event ordering. Logical clocks track causality by incrementing counters when events occur, while physical clocks attempt to align real-world time across servers. For example, Google Spanner uses atomic clocks and GPS receivers (TrueTime) to synchronize time with tight bounds, enabling consistent global transactions.
Logical clocks are often used to establish causal relationships without requiring exact real-time coordination. Lamport timestamps assign a unique counter value to each event, ensuring that if event A happens before event B, A’s timestamp is smaller. Vector clocks extend this by maintaining a vector of counters per node, allowing the system to detect concurrent updates. Apache Cassandra, for instance, uses timestamps to resolve write conflicts by favoring the latest write. These methods avoid reliance on physical time but require additional metadata and can’t guarantee absolute ordering across all nodes. They’re effective for systems where eventual consistency is acceptable or where conflicts are resolved asynchronously.
Physical clock synchronization is critical for systems needing strict consistency. NTP is commonly used to align server clocks within milliseconds, but its accuracy can vary due to network conditions. Distributed databases like CockroachDB use hybrid logical clocks (HLCs), combining NTP-synchronized physical time with logical counters to handle edge cases where physical clocks drift. Google’s TrueTime API exemplifies a highly accurate approach, using specialized hardware to bound clock uncertainty to 7ms, enabling Spanner to assign globally meaningful commit timestamps. While effective, these methods add complexity and cost, as they may require infrastructure upgrades (e.g., GPS hardware) or frequent clock adjustments. Developers must balance precision requirements with system overhead when choosing a synchronization strategy.