要点：
需要更多细节可以阅读文末链接中的论文，下面是论文内容的一些摘录。
Coordination-free Actors
In multi-master replication, a key is replicated on multiple actors, each of which can read and update its own local copy.
In a coordination-free approach, on the other hand, each actor can process a request locally without introducing any inter-actor communication on the critical path. Updates are periodically communicated to other actors when a timer is triggered or when the actor experiences a reduction in request load.
Unlike synchronous multi-master and single-master replication, a coordination-free multi-master scheme could lead to inconsistencies between replicas, because replicas may observe and process messages in different orders.
Rader: Key通过一致性哈希分不到不同的Server和Actors
Anna perform updates against their local state in parallel without synchronizing, and periodically exchange state via multicast.
Anna employs simple eventual consistency , and threads are set to multicast every 100 milliseconds.
On single machine, Actors update their local states, then write the updates to a shared buffer and multicast the address of updates in buffer to other actors.
On different machines, updates needs to be serialized (e.g. through protobuf) and then broadcast through tcp.
Rader:Anna是 最终一致性 的，意味着会有一个时间窗口各个Actor本地的状态是不同步的
Anna indeed achieves wait-free execution: the vast majority of CPU time (90%) is spent processing requests without many cache misses, while overheads of lattice merge and multicast are small. In short, Anna’s Coordination-free actor model addresses the heart of the scalability limitations of multi-core KVS systems.
TBB and Masstree spend 92% - 95% of the CPU time on atomic instructions under high contention, and only 4% - 7% of the CPU time is devoted to request handling. As a result, the TBB hash map and Masstree perform 50× slower than Anna (rep= 1) and 700× slower than Anna (full replication).
Rader：更新冲突较多的情况下，共享内存模型花费了绝大多数的CPU在原子操作上，不管是有锁还是无锁的实现方式维护“ 缓存一致性 ”都是瓶颈。 Redis因为是单线程的，没有这方面的问题。
for systems that support multi-master replication, having a high replication factor under low contention workloads can hurt performance. Instead, we want to dynamically monitor the data’s contention level and
selectively replicate the highly contented keys across threads
Rader：冲突较少的情况下，谨慎选择副本数量，过多的副本会伤害性能
Anna can significantly outperform Redis Cluster by replicating hot keys under high contention, and can
match the performance of Redis Cluster under low contention.
Rader:低冲突的情况下跟Redis性能差不多，但是高冲突的时候可以通过Hot Key副本提高性能