Given a modern distributed system composed of multiple microservices, each possessing a sub-set of a domain’s aggregate data, that system will almost assuredly have some data duplication. Given this duplication, how do we maintain data consistency? In this two-part post, we will explore one possible solution to this challenge — Apache Kafka and the model of eventual consistency.
Apache Kafka is an open-source distributed event streaming platform capable of handling trillions of messages. According to Confluent, initially conceived as a messaging queue, Kafka is based on an abstraction of a distributed commit log. Since being created and open-sourced by LinkedIn in 2011, Kafka has quickly evolved from a messaging queue to a full-fledged event streaming platform.
Eventual consistency, according to Wikipedia, is a consistency model used in distributed computing to achieve high availability that informally guarantees that if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. I previously covered the topic of eventual consistency in a distributed system using RabbitMQ in the May 2017 post, Eventual Consistency: Decoupling Microservices with Spring AMQP and RabbitMQ. The post was featured on Pivotal’s RabbitMQ website.
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