## 18.1 Group Replication Background

[18.1.1 Replication Technologies](group-replication-replication-technologies.html)
[18.1.2 Group Replication Use Cases](group-replication-use-cases.html)
[18.1.3 Multi-Primary and Single-Primary Modes](group-replication-deploying-in-multi-primary-or-single-primary-mode.html)
[18.1.4 Group Replication Services](group-replication-details.html)
[18.1.5 Group Replication Plugin Architecture](group-replication-plugin-architecture.html)

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 This section provides background information on MySQL Group Replication.

 The most common way to create a fault-tolerant system is to resort to making components redundant, in other words the component can be removed and the system should continue to operate as expected. This creates a set of challenges that raise complexity of such systems to a whole different level. Specifically, replicated databases have to deal with the fact that they require maintenance and administration of several servers instead of just one. Moreover, as servers are cooperating together to create the group several other classic distributed systems problems have to be dealt with, such as network partitioning or split brain scenarios.

 Therefore, the ultimate challenge is to fuse the logic of the database and data replication with the logic of having several servers coordinated in a consistent and simple way. In other words, to have multiple servers agreeing on the state of the system and the data on each and every change that the system goes through. This can be summarized as having servers reaching agreement on each database state transition, so that they all progress as one single database or alternatively that they eventually converge to the same state. Meaning that they need to operate as a (distributed) state machine.

 MySQL Group Replication provides distributed state machine replication with strong coordination between servers. Servers coordinate themselves automatically when they are part of the same group. The group can operate in a single-primary mode with automatic primary election, where only one server accepts updates at a time. Alternatively, for more advanced users the group can be deployed in multi-primary mode, where all servers can accept updates, even if they are issued concurrently. This power comes at the expense of applications having to work around the limitations imposed by such deployments.

 There is a built-in group membership service that keeps the view of the group consistent and available for all servers at any given point in time. Servers can leave and join the group and the view is updated accordingly. Sometimes servers can leave the group unexpectedly, in which case the failure detection mechanism detects this and notifies the group that the view has changed. This is all automatic.

 For a transaction to commit, the majority of the group have to agree on the order of a given transaction in the global sequence of transactions. Deciding to commit or abort a transaction is done by each server individually, but all servers make the same decision. If there is a network partition, resulting in a split where members are unable to reach agreement, then the system does not progress until this issue is resolved. Hence there is also a built-in, automatic, split-brain protection mechanism.

 All of this is powered by the provided Group Communication System (GCS) protocols. These provide a failure detection mechanism, a group membership service, and safe and completely ordered message delivery. All these properties are key to creating a system which ensures that data is consistently replicated across the group of servers. At the very core of this technology lies an implementation of the Paxos algorithm. It acts as the group communication engine.

