The Comprehensive Guide to Caching — Part One

Have you ever wondered how the biggest services in the world, like AWS and Netflix, achieve single-digit millisecond response times? Despite consisting of hundreds or even thousands of microservices, their architecture still delivers extremely fast response times. In contrast, we sometimes encounter scenarios where executing multiple complicated database queries slows our responses significantly — so what’s the secret? The answer is caching. Some engineers argue this strategy alone can dramatically scale your service, enabling it to handle thousands of requests per second from a single machine.

Building scalable systems often involves running complex database queries, interacting with external services and APIs, and handling CPU-intensive computations. Repeating these operations for every request can lead to significant slowdowns, scalability issues, and bottlenecks. Imagine being a software engineer for a large news website with millions of users. If each request directly hit the database, the system would quickly become overwhelmed, resulting in slow response times or potential outages. Caching provides a powerful solution by allowing us to store and reuse responses, reducing the need to query the database repeatedly and alleviating the load on the system.

Caching is a broad and vital topic, so we’ve split it into two articles to ensure we explore each concept in depth. In this first part of the series, we’ll cover foundational topics such as what caching is, how cache keys and stores work, and the significance of the cache hit ratio. In the second article, we’ll dive deeper into advanced concepts like caching strategies, maintaining cache consistency with your database using invalidation techniques, and exploring cache eviction policies. I highly recommend reading both articles in sequence to gain a complete understanding of caching. Let’s start by diving into the fundamentals of caching.

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