An introduction to caching
As we create more computationally intensive Streamlit apps and begin to use and upload larger datasets, we should start thinking about the runtime of these apps and work to increase our efficiency whenever possible. The easiest way to make a Streamlit app more efficient is through caching, which is storing some results in memory so that the app does not repeat the same work whenever possible.
A good analogy for an app’s cache is a human’s short-term memory, where we keep bits of information close at hand that we think might be useful. When something is in our short-term memory, we don’t have to think very hard to get access to that piece of information. In the same way, when we cache a piece of information in Streamlit, we are making a bet that we’ll use that information often.
The way Streamlit caching works more specifically is by storing the results of a function in our app, and if that function is called with the same...
