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Python Microservices Development

You're reading from   Python Microservices Development Build, test, deploy, and scale microservices in Python

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785881114
Length 340 pages
Edition 1st Edition
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Author (1):
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Tarek Ziadé Tarek Ziadé
Author Profile Icon Tarek Ziadé
Tarek Ziadé
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Table of Contents (13) Chapters Close

Preface 1. Understanding Microservices FREE CHAPTER 2. Discovering Flask 3. Coding, Testing, and Documenting - the Virtuous Cycle 4. Designing Runnerly 5. Interacting with Other Services 6. Monitoring Your Services 7. Securing Your Services 8. Bringing It All Together 9. Packaging and Running Runnerly 10. Containerized Services 11. Deploying on AWS 12. What Next?

Splitting the monolith


Let's project into the world where Runnerly, as implemented previously, starts to be used by a lot of people. Features are added, bugs are fixed, and the database is steadily growing.

The first problem that we're facing is the background process that creates reports and calls Strava. Since we're having thousands of users, these tasks take most of the server resources, and users are experiencing slowdowns on the frontend.

It's getting obvious that we need to have them running on separate servers. With the monolithic application using Celery and Redis, it's not an issue. We can dedicate a couple of new servers for the background jobs.

But the biggest concern if we do this is that the Celery worker code needs to import the Flask application code to operate. So the deployment dedicated to the background workers needs to include the whole Flask app. That also means that every time something changes in the app, we'll need to update the Celery workers as well to avoid regression...

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