This post provides an in-depth comparison and benchmark of two popular Python frameworks for building real-time dashboards: Django Channels and Flask SocketIO. It covers their ease of use, architecture, performance, scalability, and overall development experience to help developers choose the right framework for their next real-time application.
Real-time dashboards that update instantly as new data arrives are becoming increasingly popular. They allow you to monitor metrics and key performance indicators in real-time to make faster data-driven decisions. In this post, we will benchmark two Python frameworks for building real-time dashboards - Django Channels and Flask SocketIO. We will look at their architecture, ease of use, performance, and scalability to help you pick the right framework for your next real-time application.
Before we dive into the benchmarks, let's briefly explain what Django Channels and Flask SocketIO are and how they enable real-time capabilities:
Django Channels extends Django and enables building real-time applications efficiently. It provides an ASGI server and a channel layer that handles complex routing, messaging, and concurrency out of the box.
Key capabilities:
Flask SocketIO wraps the SocketIO library to enable real-time capabilities in Flask apps. It establishes a WebSocket connection to constantly stream data between the client and server.
Key capabilities:
With this background, let's now benchmark Django Channels and Flask SocketIO across a few key criteria.
Both Flask SocketIO and Django Channels are easy to get started with real-time capabilities.
Flask SocketIO takes minutes to add real-time events to an existing Flask app. You import SocketIO, initialize it, and use decorators to define event handlers. The simplicity and lightweight nature of Flask carries over to Flask SocketIO as well.
On the other hand, Django Channels has a slightly steeper learning curve. You need to grasp Channels, routing, consumers, ASGI, and channel layers. However, there are plenty of resources to ramp up quickly. And you get the benefit of Django's batteries-included framework after the initial ramp up.
Overall, Flask SocketIO is simpler to get started with while Django Channels has a more feature-rich ecosystem.
Winner: Flask SocketIO
Django Channels follows a protocol-first architecture with ASGI being the standard protocol. This means that your application code is decoupled from the actual sockets and transports layer. To scale the transport layer horizontally, you can plug in a channel layer like Redis without changing application code.
In contrast, Flask SocketIO directly builds on top of the SocketIO python library and TCP sockets. So your application code stays coupled to the transport layer. To scale horizontally, you will need to deploy copies of the entire Flask SocketIO application behind a load balancer.
Overall, Channels provides a cleaner separation of concerns and more flexibility in scaling. But SocketIO apps are simpler to reason about initially.
Winner: Django Channels
To benchmark performance, we built a simple real-time dashboard with 100 concurrent users updating metrics every second. This translates to about 3600 events every minute being handled by the server.
We used the AsyncIO transport for Flask SocketIO and Redis channel layer for Django Channels - both optimal choices for horizontal scaling.
Here is how Django Channels and Flask SocketIO compare on key performance metrics:
1 CPU Core - 100 concurrent connections