When building Streamlit apps, performance is key to providing a smooth user experience. In this blog post, we'll explore various techniques to optimize your Streamlit applications and make them lightning-fast.

1. Caching: Your Secret Weapon

Caching is one of the most powerful tools in your Streamlit optimization arsenal. It allows you to store the results of expensive computations and reuse them when needed.

Using @st.cache

The @st.cache decorator is your go-to for basic caching:

import streamlit as st
import time

@st.cache
def expensive_computation(x):
    time.sleep(2)

# Simulating a time-consuming operation
    return x * 2

result = expensive_computation(21)
st.write(f"The result is: {result}")

This function will only run once, and subsequent calls will return the cached result.

Leveraging @st.experimental_memo

For more fine-grained control, use @st.experimental_memo:

import streamlit as st

@st.experimental_memo(ttl=3600)
def fetch_data_from_api():

# Your API call here
    pass

data = fetch_data_from_api()
st.dataframe(data)

This decorator allows you to set a time-to-live (TTL) for your cached data, ensuring it's refreshed periodically.

2. State Management: Keep It Local

Efficient state management can significantly improve your app's performance. Use Streamlit's session state to store and manage local data:

import streamlit as st

if 'counter' not in st.session_state:
    st.session_state.counter = 0

if st.button('Increment'):
    st.session_state.counter += 1

st.write(f"Counter value: {st.session_state.counter}")

This approach avoids unnecessary recomputation and keeps your app responsive.

3. Efficient Data Handling

When working with large datasets, consider these strategies:

Lazy Loading

Load data only when necessary:

import streamlit as st
import pandas as pd

@st.cache
def load_data():
    return pd.read_csv("large_dataset.csv")

if st.checkbox("Show data"):
    data = load_data()
    st.dataframe(data)

Data Aggregation

Aggregate data before displaying:

import streamlit as st
import pandas as pd

@st.cache
def load_and_aggregate_data():
    df = pd.read_csv("large_dataset.csv")
    return df.groupby('category').mean()

aggregated_data = load_and_aggregate_data()
st.dataframe(aggregated_data)

4. Optimize Rendering

Streamlit offers various ways to display data. Choose the most efficient one for your use case:

• Use st.dataframe() for interactive tables with small to medium-sized datasets.
• Opt for st.table() for static, non-interactive tables.
• Consider st.write() for simple data display.

Example:

import streamlit as st
import pandas as pd

data = pd.DataFrame({
    'A': range(1000),
    'B': range(1000, 2000)
})

st.dataframe(data)

# Interactive, but might be slower for large datasets
st.table(data.head())

# Static, faster for displaying a subset
st.write(data.describe())

# Simple and fast for summary statistics

5. Leverage Asynchronous Operations

For long-running tasks, use Streamlit's experimental async support:

import streamlit as st
import asyncio

@st.experimental_async
async def long_running_task():
    await asyncio.sleep(5)
    return "Task completed!"

result = await long_running_task()
st.write(result)

This keeps your app responsive while performing time-consuming operations in the background.

By implementing these optimization techniques, you'll create Streamlit apps that are not only functional but also fast and efficient. Remember to profile your app and focus on optimizing the most resource-intensive parts for the best results.