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Regardless of complexity, what I find interesting in the deployment of Redis for this use case is the following: if for the use case the consistency provided by Redis is sufficient, or even more, when a relaxed level of consistency and durability are enough for the business requirements, being Redis an in memory system and because of the specific design of the Stream inside Redis (a radix tree of blobs), you get a very high number of operations per second per process, with a memory usage which is very compact compared to the other data structures. This allows to scale certain use cases using a small percentage of the resources needed with other systems.

About the use cases where strong guarantees are needed: for instance Disque (http://github.com/antirez/disque) provides strong guarantees of delivery in face of failures, being a pure AP system with synchronous replication of messages. For Redis 4.2 I'm moving Disque as a Redis module. To do this, Redis modules are getting a fully featured Cluster API. This means that it will be possible, for instance, to write a Redis module that orchestrates N Redis masters, using Raft or any other consensus algorithm, as a single distributed system. This will allow to also model strong guarantees easily.

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