脑裂问题

2024-04-27 03:18

文章标签 问题脑裂

本文主要是介绍脑裂问题，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

问题描述

由于网络分区，产生了至少两个以上集合，不包含主节点的集合、会出现检测不到主节点的情况，从而产生新的主节点。这样，整个集群就有了两个以上的主节点，从而产生不一致。

解决方案

Davidson et al.,[2] after surveying several approaches to handle the problem, classify them as either optimistic or pessimistic.

The optimistic approaches simply let the partitioned nodes work as usual; this provides a greater level of availability, at the cost of sacrificing correctness. Once the problem has ended, automatic or manual reconciliation might be required in order to have the cluster in a consistent state. One current implementation for this approach is Hazelcast, which does automatic reconciliation of its key-value store.[3]

The pessimistic approaches sacrifice availability in exchange for consistency. Once a network partitioning has been detected, access to the sub-partitions is limited in order to guarantee consistency. A typical approach, as described by Coulouris et al.,[4] is to use a quorum-consensus approach. This allows the sub-partition with a majority of the votes to remain available, while the remaining sub-partitions should fall down to an auto-fencing mode. One current implementation for this approach is the one used by MongoDB replica sets.[5] And another such implementation is Galera replication for MariaDB and MySQL. .[6]

Modern commercial general-purpose HA clusters typically use a combination of heartbeat network connections between cluster hosts, and quorum witness storage. The challenge with two-node clusters is that adding a witness device adds cost and complexity (even if implemented in the cloud), but without it, if heartbeat fails, cluster members cannot determine which should be active. In such clusters (without quorum), if a member fails, even if the members normally assign primary and secondary statuses to the hosts, there is at least a 50% probability that a 2-node HA cluster will totally fail until human intervention is provided, to prevent multiple members becoming active independently and either directly conflicting or corrupting data.