Scaling Real-Time Attendance Management with WebSocket Connections

Introduction

I've been working on a real-time attendance management system that uses WebSocket connections to synchronize attendance data across 300+ concurrent clients. The system is built using FastAPI, WebSockets, Python, Leaflet.js, SQLAlchemy, and Linux VPS with Nginx. Recently, I encountered a scaling issue where the system was experiencing sub-50ms sync delays, causing inconsistencies in attendance data.

The Problem

The problem was caused by the high volume of concurrent WebSocket connections, which were overwhelming the system and causing delays in synchronization. I needed to find a way to scale the system to handle the increased load while maintaining real-time synchronization.

The Investigation

To investigate the issue, I used various tools to monitor the system's performance, including system logs, database queries, and network traffic analysis. I discovered that the bottleneck was in the database queries, which were taking too long to execute due to the high volume of concurrent connections.

The Solution

To solve the issue, I implemented several optimizations:

• I optimized the database queries to reduce the number of queries and improve their performance.
• I implemented a caching mechanism to reduce the load on the database and improve response times.
• I used load balancing techniques to distribute the load across multiple servers, improving the system's scalability.

Here's an example of the optimized database query:

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

# Create a database engine
engine = create_engine('postgresql://user:password@host:port/dbname')

# Create a session maker
Session = sessionmaker(bind=engine)

# Define a function to retrieve attendance data
def get_attendance_data(session):
    # Use a single query to retrieve all attendance data
    attendance_data = session.query(Attendance).all()
    return attendance_data

# Create a session and retrieve attendance data
session = Session()
attendance_data = get_attendance_data(session)

Lessons Learned

I learned several lessons from this experience:

• The importance of monitoring system performance to identify bottlenecks and areas for optimization.
• The need to optimize database queries to improve system performance and scalability.
• The benefits of using caching mechanisms and load balancing techniques to improve system performance and scalability.