Understand replication and consistency
See how replicas improve availability while creating coordination choices.
- Compare synchronous and asynchronous replication and explain eventual consistency.
Replication keeps copies of data on multiple nodes. Synchronous replication waits for acknowledgements before confirming a write, which can improve durability or read-after-write behavior at the cost of latency and availability during failures. Asynchronous replication responds sooner, but replicas can lag. Eventual consistency means replicas converge if updates stop and communication resumes; it does not promise when a particular read will see an update.
1primary_value = "v2"
2replica_value = "v1"
3print(f"primary={primary_value}, replica={replica_value}")
4replica_value = primary_value
5print(f"after replication: {replica_value}")primary=v2, replica=v1 after replication: v2
This Python example illustrates replica lag. A product can reduce confusion with read-your-writes routing, version indicators, or by waiting for a chosen replication acknowledgement before reporting success.
Key takeaways
Replication improves resilience but copies can disagree temporarily.
Synchronous acknowledgement trades latency and availability for stronger write guarantees.
Eventual convergence does not imply a freshness deadline.
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