In-memory caching is a technique used to store data in the memory (RAM) of the application's server to improve its performance. It is typically used when the following conditions are met:
Repeatable Queries: If your application constantly makes the same queries for data, it's more efficient to cache this data in memory rather than repeatedly reading from disk.
Read-Heavy Workloads: In-memory caching is ideal for applications that have a high read-to-write ratio, as these types of applications can significantly benefit from reduced latencies and improved throughput.
Data Volatility: If the data doesn't change often, it's a good candidate for in-memory caching, as the overhead of invalidating and refreshing the cache would be minimized.
Low Latency Requirement: For applications or services where low latency is critical, in-memory caches provide rapid access to data by holding frequently accessed information to serve up requests quickly.
Here's an example of how you might use in-memory caching in a Python application with Redis:
In this example,
expensive_query could be a function that makes a slow database query, or perhaps makes a request to a remote API. By caching the results of these calls, you can greatly reduce the latency and load on your system.
Remember, while in-memory caching boosts performance, it should be used judiciously considering factors like cost since RAM is relatively more expensive than disk storage, and volatility, as data stored in-memory is volatile and can be lost in case of failures unless backed by persistent storage.