2 2 Caching Data Explained

Key Concepts

• Caching: Storing the results of expensive operations to avoid redundant computations.
• st.cache: A Streamlit decorator to cache function outputs.
• Cache Expiry: Mechanisms to invalidate cached data after a certain period or condition.
• Performance Optimization: How caching improves the performance of Streamlit apps.

Explanation

1. Caching

Caching is a technique used to store the results of expensive operations so that they can be reused without recomputing them. This is particularly useful in Streamlit apps where certain operations, such as data loading or complex calculations, can be time-consuming.

2. st.cache

st.cache is a Streamlit decorator that allows you to cache the output of a function. When the function is called with the same arguments, Streamlit will return the cached result instead of re-executing the function.

3. Cache Expiry

Cache expiry refers to mechanisms that invalidate cached data after a certain period or condition. This ensures that the cached data remains fresh and relevant. Streamlit provides options to set cache expiry based on time or specific conditions.

4. Performance Optimization

Caching significantly improves the performance of Streamlit apps by reducing the need for redundant computations. This leads to faster response times and a smoother user experience.

Examples

Example 1: Basic Caching with st.cache

import streamlit as st
import time

@st.cache
def expensive_computation(a, b):
    time.sleep(2)  # Simulate a time-consuming computation
    return a * b

a = 2
b = 21
st.write("Result:", expensive_computation(a, b))
    

Example 2: Caching with Cache Expiry

import streamlit as st
import time

@st.cache(ttl=3600)  # Cache expires after 1 hour (3600 seconds)
def load_data():
    time.sleep(2)  # Simulate a time-consuming data loading process
    return "Data loaded"

st.write(load_data())
    

Analogies

Think of caching as a refrigerator where you store prepared meals. Instead of cooking a meal from scratch every time you're hungry, you can quickly grab a pre-cooked meal from the fridge. Similarly, caching in Streamlit allows you to quickly retrieve precomputed results, saving time and resources.

By mastering caching in Streamlit, you can create high-performance applications that efficiently handle expensive computations and data loading tasks.