{"id":20636,"date":"2023-04-11T10:21:54","date_gmt":"2023-04-11T08:21:54","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=20636"},"modified":"2023-04-11T10:50:19","modified_gmt":"2023-04-11T08:50:19","slug":"distributed-cache","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/backend\/distributed-cache\/","title":{"rendered":"Distributed Cache: How to Boost System Responsiveness"},"content":{"rendered":"\n

Distributed cache can improve the responsiveness of today’s high-performance web and mobile apps<\/strong>. At heart it’s a simple concept, but, when used well, it can bring improved capacity and flexibility to networked systems. As well as classic caching scenarios, distributed cache systems like Redis offer an alternative to standard databases when handling working data.<\/p>\n\n\n\n

In this article we’ll look more closely at distributed cache and how it can be used. And we’ll share some insights from Insoore<\/a> to help you optimise your own apps and services.<\/p>\n\n\n\n

The ABC of distributed cache<\/strong><\/h2>\n\n\n\n

The use of caching<\/a> is well-known to preserve the stability and performance of web apps and websites. Resources that are served regularly are stored in pre-generated forms to reduce database hits and loads on internal networks. Whether served from the filesystem or memory, such mechanisms can significantly improve the capacity and speed of data-intensive applications. Distributed caching is basically an extension of this principle.<\/p>\n\n\n\n

Where standard caching stores resources locally, a distributed cache pools the RAM of multiple connected devices. This overcomes the inherent limitations of single-machine-based caching strategies<\/strong>. Distributed cache is typically used for data-driven apps where performance and scale are significant factors.<\/p>\n\n\n\n

Distributed cache vs. database<\/strong><\/h2>\n\n\n\n

Without caching, data access typically requires a structured database of some form. Databases allow data to be stored securely and reliably, using the ACID<\/a> principles to ensure data consistency<\/strong>. However, database reads and writes require disk operations, with associated network and hardware overheads. Distributed cache allows quick reads of commonly used data directly from memory, avoiding this I\/O latency.<\/p>\n\n\n\n

Distributed cache also achieves efficiency savings in other ways. It doesn’t require query parsing and syntax validation. Checks on database authorisation and permissions can be bypassed. What’s more, distributed cache data can be optimised by data access plans developed to meet predicted usage patterns. Caching also allows greater concurrency for read-only operations as locks or integrity checks are not needed.<\/p>\n\n\n\n

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Distributed cache offers benefits compared to traditional database.<\/figcaption><\/figure>\n\n\n\n

What is Redis?<\/strong><\/h2>\n\n\n\n

Redis is an in-memory data store that can serve as the basis for distributed cache operations. For applications where fast performance, scalability and network optimisation are key, Redis is ideal<\/strong>. The in-memory nature of its data operations makes it much faster than conventional databases’ read-write processes and by making use of distributed architectures, it can cut out disk-based persistence for real-time functionality. However, that doesn’t mean your data is precarious. <\/strong>Redis features a number of persistence options, including a Redis database that performs periodic snapshots in case of system failure.<\/p>\n\n\n\n

Redis is also highly versatile and is used for functionality like streaming and message brokers. It allows the use of native datatypes without the need for conversion. Strings, hashes, lists, sets, streams and much more are all supported. Redis has its own DSL, Lua<\/a>, for server-side scripting, making it highly programmable and flexible. It is modular and extensive, and you can write your own modules in C, or with C++<\/a> and any other language that supports C binding functionalities.<\/p>\n\n\n\n

Applying distributed cache: use cases and examples<\/strong><\/h2>\n\n\n\n

A few example use cases give a window into just how powerful Redis can be:<\/p>\n\n\n\n