An Introduction to Redis Data Structures

Since its launch over a decade ago, Redis has become one of the popular databases in the world. When you access an ecommerce or gaming platform, for instance, it’s entirely possible that these applications are using Redis behind the scenes — but do you really know Redis? In this white paper, we try to dispel some common misconceptions about Redis, clarify exactly why it is so popular and explain how it has evolved into an all-purpose preferred database.

Redis is an open source, in-memory data structure store, and each of its data structures is optimized for specific operations. Thanks to these data structures, Redis is able to support a wide range of use cases, enabling customers to use Redis as a database, cache or message broker depending on their needs. Download this white paper to learn more about what each of these terms means within a Redis context.

Redis often starts off as a cache, with many customers using it to store high velocity data that is not stored anywhere else. However, Redis distinguishes itself from the common cache because it can persist its data to disk and is highly available due to in-memory replication and auto-failover. In this way, Redis has gradually assumed a database role.

That isn’t all Redis can do, either. Following the publish-subscribe paradigm, Redis can also be used as a message broker. It is not a single-purpose message broker like Apache Kafka or RabbitMQ, but Redis’ List data structure and notification setup make it well-suited to this particular use case. Redis’ high performance as a message broker also makes it an ideal solution for job management systems like Sidekiq, Resque and Celery.

At the heart of Redis’ versatility are its numerous data structures. Each of Redis’ data types are like Lego blocks, designed to work together so that developers can perform complex operations with minimal latency, coding and network overhead. Redis’ data structures — ranging from Strings and Lists to Hashes and HyperLogLogs — set Redis apart from its competitors, and they are optimized for specific application scenarios and functionalities. For example, customers can use Redis for everything from real-time transaction analytics to time-series data analysis.

Moving forward, two factors will ensure that Redis will only continue to grow: the increasing need for a versatile, in-memory database, and the increasing availability of cheap memory options. We are also always working to enhance Redis’ data structures, although Redis is already unparalleled when it comes to processing data at incredibly high speeds. Please download our white paper to learn more about Redis’ structures and functions.