| concept | desp |
|---|---|
| Consistency | Every read receives the most recent write or an error. |
| Availability | Every request receives a (non-error) response, without the guarantee that it contains the most recent write. |
| Partition Tolerance | The system continues to operate despite an arbitrary number of messages being dropped (or delayed) by the network between nodes. |
All distributed systems must make trade-offs between guaranteeing consistency, availability, and partition tolerance (CAP Theorem). Our goal was to support replication in a Kafka cluster within a single datacenter, where network partitioning is rare, so our design focuses on maintaining highly available and strongly consistent replicas. Strong consistency means that all replicas are byte-to-byte identical, which simplifies the job of an application developer.
| Article | Note |
|---|---|
| Medium article with good-look Image | - |
| Greate Discussion about why kafka is not P in CAP Theorem | - |
| Wiki of CAP Theorem | - |
| Chinese Introduction of CAP | (chinese) |