How to cache data? What exactly to cache and why?

Data caching is the process of storing the results of expensive operations (such as database queries, computations, or network loads) for quick reuse.

What to cache:

• Results of frequently repeated queries or calculations.
• Static or rarely changing data.
• Data that takes significant time to retrieve.

Why cache:

• Reduce application response time.
• Decrease load on external services and databases.
• Improve system scalability.

In Python, you can use the built-in decorator functools.lru_cache for caching functions with repeated calls:

from functools import lru_cache

@lru_cache(maxsize=128)
def expensive_function(x):
    # simulate an expensive operation
    return x * x

print(expensive_function(4))  # computed and cached
print(expensive_function(4))  # retrieved from cache

For more complex scenarios, external caches like Redis or Memcached are used, especially in distributed systems.