What are the Limitations of Streamlit?

Streamlit is a powerful open-source Python library for quickly building interactive data applications, but like any framework, it comes with certain limitations that developers should consider, especially for complex or production-grade deployments. These limitations primarily revolve around UI customization, performance for large-scale data, scalability, and specific platform integrations.

Core Limitations of Streamlit

While Streamlit excels in rapid prototyping and building internal tools, it has several constraints that might make it less suitable for every use case.

1. Limited UI/UX Customization
Streamlit prioritizes simplicity and speed over granular control of the user interface. While it offers basic components and layout options (like columns and expanders), achieving highly customized or branded designs akin to what's possible with a full-stack web framework (e.g., React, Angular, or even Flask/Django with custom CSS) can be challenging or impossible.

• Impact: Developers have less control over CSS styling, intricate layouts, and advanced interactive elements, which might restrict the application's aesthetic appeal or specific user experience requirements.
• Workaround: Use st.markdown for custom HTML/CSS injections (with caution), or consider embedding custom components using st.components.v1.html, though these often add complexity.

2. Performance with Large Datasets and Complex Logic
Streamlit's execution model involves re-running the entire script from top to bottom every time a user interacts with a widget. While this simplifies development, it can lead to performance bottlenecks if your application deals with large datasets, complex computations, or frequent user interactions without proper optimization.

• Impact: Slower response times, especially for apps performing heavy data processing or machine learning model inferences on every interaction.
• Workaround: Effectively utilize Streamlit's caching mechanisms:
  • @st.cache_data: For caching data retrieval and transformations.
  • @st.cache_resource: For caching global resources like ML models or database connections.
  • Structure your code to minimize re-executions of expensive operations.

3. Scalability for Production Applications
Streamlit is fantastic for internal dashboards, proofs-of-concept, and small-to-medium scale applications. However, scaling a Streamlit application for high-traffic, public-facing, or enterprise-level deployments can be more complex than with traditional web frameworks. Managing concurrent users and ensuring robust performance under heavy load requires careful consideration and potentially additional infrastructure.

• Impact: May not be ideal for applications requiring high availability, complex load balancing, or a large number of simultaneous users without significant architectural overhead.
• Workaround: Consider deploying with solutions like Streamlit Server, containerization (Docker), or Kubernetes for better resource management and scalability.

4. Lack of Native User Authentication and Authorization
Streamlit does not provide built-in features for user management, such as login systems, user registration, or role-based access control. Developers must integrate external libraries or custom solutions to implement secure authentication and authorization.

• Impact: Securing applications and managing user access requires additional development effort and reliance on third-party components.
• Workaround: Integrate with community-driven libraries like streamlit-auth-ui, st-login-form, or connect to external identity providers (e.g., Auth0, OAuth).

5. Building Complex Multi-Page Applications
While Streamlit has improved multi-page application support with st.pages, building highly intricate web applications with deeply nested routes, complex navigation patterns, or inter-page communication can still feel less streamlined compared to full-fledged web development frameworks.

• Impact: Can lead to less organized codebases or more challenging state management across multiple pages for very complex applications.
• Workaround: Leverage st.pages effectively, utilize st.session_state for inter-page communication, and structure your project modularly.

Specific Limitations in Snowflake

When using Streamlit within the Snowflake environment, there are additional, specific limitations to be aware of:

• Data Retrieval Limit: Streamlit applications running in Snowflake have a specific limit on the amount of data they can retrieve. This means that applications designed to query and display extremely large datasets might encounter restrictions.
• Server-Side Encryption for Stages: The Streamlit in Snowflake editor does not support stages configured with server-side encryption. This can be a significant limitation for organizations with strict data security and encryption policies when working with external stages.

Summary of Streamlit Limitations

The table below provides a quick overview of Streamlit's common limitations and considerations:

Understanding these limitations helps in choosing the right tool for your specific project needs. While Streamlit excels in rapid development and data exploration, for applications requiring extensive customization, high scalability, or robust user management, a full-stack web framework might be a more appropriate choice.