Python Async Workers on Fly Machines

Last year, while working in what was my day job at the time (before I joined Fly.io!), we had just developed a new internal tool to help an adjacent team with their work. This adjacent team wrote technical content, and they had a lot of issues stemmi

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Darla Magdalene Shockley
•10 min read•beginner•
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Overview

This article discusses how to implement lightweight background jobs for a Python web application using Fly Machines, which are fast-starting virtual machines based on Firecracker. It highlights the challenges faced with traditional methods and presents a streamlined approach utilizing Redis for job management.

What You'll Learn

1

How to implement simple background jobs using Fly Machines

2

Why using Redis for job management can improve efficiency

3

When to use Fly Machines for cost-effective resource management

Prerequisites & Requirements

  • Fly.io account and installation of the flyctl command-line tool

Key Questions Answered

How do Fly Machines help in implementing background jobs?
Fly Machines are lightweight VMs that start quickly and allow you to run background jobs without the overhead of traditional server management. They utilize a simple API for interaction, making it easy to manage job execution efficiently.
What are the steps to set up Redis for job management?
To set up Redis, you need to create an Upstash Redis instance using the command 'fly redis create'. This instance will facilitate communication between your web application and the background job workers, ensuring smooth operation.
What is the code structure for the worker in Fly Machines?
The worker code involves defining a function that retrieves job parameters from Redis, executes the specified task, and writes the results back to Redis. This modular approach allows for easy task management and scalability.

Technologies & Tools

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Backend
Fly Machines
Used for running lightweight background jobs in a Python web application.
Database
Redis
Used for storing job parameters and results to facilitate communication between the web application and background jobs.
Backend
Flask
Framework used to build the demo web application that interacts with Fly Machines.

Key Actionable Insights

1
Utilize Fly Machines for infrequent background jobs to save costs.
Since you only pay for resources when the workers are actively processing jobs, this approach can significantly reduce costs for applications with sporadic workloads.
2
Implement Redis for job parameter storage to streamline task execution.
Using Redis allows for quick access to job parameters and results, enhancing the responsiveness of your application and improving user experience.
3
Consider building a reusable library for background job management.
Creating a generic library can facilitate the implementation of background jobs across multiple applications, promoting code reuse and consistency in your projects.

Common Pitfalls

1
Over-provisioning resources for background jobs can lead to unnecessary costs.
This happens when workers remain idle most of the time, leading to wasted resources. Using Fly Machines allows for scaling down to zero when not in use, which mitigates this issue.

Related Concepts

Background Job Processing
Asynchronous Programming
Microservices Architecture