Ensuring Single Worker Execution in FastAPI with Uvicorn

What will you learn?

In this comprehensive guide, you will learn how to guarantee that specific code within a FastAPI application executes only once across all Uvicorn workers. This is crucial for tasks such as database initialization that should not be duplicated. By implementing a mechanism for worker coordination, you can ensure seamless operation even with multiple workers running concurrently.

Introduction to the Problem and Solution

When deploying a FastAPI application using Uvicorn with multiple workers, each worker functions as an independent process. This setup can lead to scenarios where essential initialization tasks, like configuring a database or sending notifications, are performed redundantly by each worker instead of just once overall. To address this challenge and ensure that certain code runs exclusively in one worker, we need to establish a system for coordinating the workers effectively.

The proposed solution involves leveraging the environment variable WORKER_ID, which is unique for every Uvicorn worker. By identifying the initial worker (typically assigned an ID of 1), we can selectively execute our initialization code, guaranteeing its execution only once irrespective of the number of workers spawned by Uvicorn.


import os
from fastapi import FastAPI

app = FastAPI()

if os.environ.get("WORKER_ID", "1") == "1":
    # Place your one-time execution code here
    print("This is executed by only one worker.")

async def read_root():
    return {"Hello": "World"}

# Copyright PHD


In the provided solution: – We first validate the presence of an environment variable WORKER_ID, which must be configured when launching your Uvicorn server with multiple workers. – The comparison os.environ.get(“WORKER_ID”, “1”) == “1” ensures that our designated block of code executes when either no WORKER_ID is specified (assuming a single-worker scenario) or if it’s explicitly set to “1”. – Any critical initialization or setup task enclosed within this conditional block will execute solely within one worker process.

It’s imperative to manage environment variables meticulously and synchronize their values across diverse deployment environments effectively. Additionally, relying on environment variables necessitates precise control over your application’s startup procedures, which may vary based on the hosting services or container orchestration tools utilized.

  1. How do I set environment variables for Uvicorn workers?

  2. To define an environment variable like WORKER_ID, utilize export in Linux/MacOS (export WORKER_ID=1) or SET in Windows (SET WORKER_ID=1) before executing your server command.

  3. Can I use this technique with any number of workers?

  4. Yes, but you must manually handle setting distinct WORKER_ID values for each worker if configuring them outside a Docker or Kubernetes setup where automation is feasible.

  5. Does this approach work in Docker containers?

  6. If each container operates as a single instance of the application (effectively acting as its own ‘worker’), then yes�although managing singleton processes differs in orchestrated environments like Kubernetes.

  7. What happens if my ‘one-worker’ task fails? Will another attempt it?

  8. No. The provided logic does not automatically retry failed tasks on other workers. Addressing failures would necessitate more intricate coordination mechanisms or external tools/services tailored for distributed task management.

  9. Is there performance impact using this method?

  10. The overhead is minimal since the condition check occurs solely at startup time and exerts negligible influence on runtime performance.

  11. Can I still scale my FastAPI app using this method?

  12. Absolutely! This methodology facilitates scalability while enabling control over initializing resources or executing startup operations within your application.

  13. Is there an alternative way without using environmental variables?

  14. Yes! Alternatives include employing centralized flags within databases/memcache systems where all instances can verify if initialization has been completed elsewhere�or leveraging features from orchestration tools directly.

  15. How secure is this approach?

  16. As secure as handling any environmental configuration; ensure sensitive information remains protected through these variables with appropriate safeguards.

  17. Can I apply similar logic inside background tasks initiated by API requests?

  18. Technically feasible�yet consider whether managing background tasks through dedicated queueing systems offering inherent mechanisms to restrict job duplication across workers might be more advantageous.


Ensuring specific segments of your FastAPI application run precisely once even when distributed across numerous processes mandates effective coordination among those processes. Utilizing environmental variables offers a direct albeit manual approach to achieve singular execution amidst multiple instances�an indispensable feature for applications requiring meticulous control over resource-intensive operations during various lifecycle stages.

Leave a Comment