Causal-Pdh: Causal Consistency Model for NoSQL Distributed Data Storage Using HashGraph

The causal consistency of data in a distributed environment means that when data with causal dependence is updated, the dependency metadata in other distributed copies must be updated simultaneously, while meeting higher availability and performance requirements. To solve the problem of users put latency and updating visible latency in existing results, based on the data center stable vectors, combined with the principle of hybrid logical clocks and the HashGraph, we propose the Causal-Pdh model. To reduce the communication overhead caused by exchanging data between replicates, partial stabel vectors required by synchronizing data and Hash value as the message signatures are used instead of the whole data center stable vectors. The principle of virtual voting in HashGraph is used to improve the process of synchronizing the latest entries in each data center. Just like Gossip about Gossip: each parent node also randomly exchanges the latest status, and updates the clock regularly. This progress reduces the time of virtual voting between the replicates. Finally, it is verified by experiments that the Causal-Pdh model not only doesnt affect the throughput of the client query, but also reduces the wait latency of users put operation by 20.85% when the clock skew is severe. When the query is amplified in the system, the response time of request is reduced by 23.37%.