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TechnicalPig🐷: Finding the Right Cache for your Application
Part 26: Quick and Easy Reads for the Busy Engineer
Caching is a critical component for enhancing the performance and scalability of applications. By storing frequently accessed data in a temporary storage area, caching reduces the need to repeatedly fetch data from slower backend systems, thereby decreasing response times and relieving pressure on databases. This results in a smoother, faster user experience and can significantly reduce resource consumption and cost, particularly under high load.
Moreover, caching helps in maintaining system availability and performance during peak traffic periods, making it an essential strategy for optimising both web and backend application efficiency.
Whether dealing with user interface speed improvements or backend data processing, implementing appropriate caching mechanisms can lead to a more robust and responsive application.
1. In-Process Cache
What is it? In-process caching stores data within the same process as your application. This means the data is held in the same memory space as the application itself, allowing direct access without any network overhead.
Example Use Case: Consider using in-process caching if you are working with AWS Lambda functions. The cache would reside on the same Lambda instance and remain there for the duration of that instance's life. It is important to note that this cache is not shared across different Lambda instances, even if they access the same data store.
When to Use: In-process caching is ideal when:
Fast data access is crucial.
Data does not need to be shared with other processes or instances.
You expect high-frequency access in short bursts, such as multiple invocations to a Lambda function in quick succession.
2. Distributed Cache
What is it? A distributed cache stores data outside of your application's process. Accessing this cache typically requires network calls, as it is not housed within the same memory space as your application.
Example Use Case: Distributed caching is useful when you need persistence beyond the lifespan of a single process or when multiple applications need to access the cache. For instance, different Lambda instances can access a shared distributed cache, which helps maintain data consistency and availability across services.
When to Use: Distributed caching is most beneficial when:
There is a need for shared data access among various parts of your application.
You are dealing with a distributed system where data consistency and availability across different nodes are required.
Summary
Choose in-process caching for scenarios requiring quick, isolated access to data without the overhead of network latency. Opt for distributed caching when your application's architecture demands shared access to data across multiple processes or services.
Each caching strategy offers unique benefits, so selecting the right one depends on your application's specific needs and architecture.