Distributed Tasks Demystified with Celery, SQS & Python
This course teaches beginners to industry professionals the fundamental concepts of Distributed Programming in the context of python & Django. We look at how to build applications that increase throughput and reduce latency. In this course, we will take a dive intially in the irst part of the course and build a strong foundation of asynchronous parallel tasks using python-celery a distributed task queue framework. We will explore AWS SQS for scaling our parallel tasks on the cloud.
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These fundamentals will aid you in building scalable Python solutions for virtually any python project. By the end of this course, you will have learnt how to use popular distributed programming frameworks for python and Django. Through concepts learnt, you will discover the world of distributed computing with Python and how easy it is to build distributed components into your python or Django projects.
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| # | Title | Duration |
|---|---|---|
| 1 | Introduction | 05:13 |
| 2 | Prepping up your environment | 07:24 |
| 3 | Blocking vs non blocking (part 1) | 06:11 |
| 4 | Blocking vs non blocking (part 2) | 05:01 |
| 5 | Concurrency Consumer & Producer problem a deep dive | 06:38 |
| 6 | Solving Consumer producers problem with Mutual Exlusion | 06:19 |
| 7 | Controlling threads with conditions (Part 1) | 02:24 |
| 8 | Controlling threads with conditions (Part 2) | 08:17 |
| 9 | Controlling threads with conditions (Part 3) | 03:51 |
| 10 | Daemon threads by example (Part 4) | 02:06 |
| 11 | Consumer producer a thread safe FIFO queue | 05:57 |
| 12 | Getting started with Celery | 05:53 |
| 13 | Celery backends & Asyncresult by example | 08:45 |
| 14 | Python exception handling back to the basics | 13:43 |
| 15 | Exception handling in Celery Explained | 09:24 |
| 16 | Celery scheduled periodic tasks (Part 1) | 04:45 |
| 17 | Celery scheduled periodic tasks (Part 2) | 04:43 |
| 18 | Celery scheduled periodic tasks How to apply Mutex (Part 3) | 10:39 |
| 19 | Celery scheduled periodic tasks solar schedules | 01:21 |
| 20 | Introduction to distributed tasks with AWS SQS | 14:00 |
| 21 | Creating your first AWS SQS Queue with your AWS Console | 05:21 |
| 22 | How to create a AWS SQS background worker in python (Part 1) | 08:04 |
| 23 | How to create a AWS SQS background worker in python (Part 2) | 09:43 |
| 24 | Dead-letter Queues the theory | 07:12 |
| 25 | Dead-letter Queues illustrated | 10:17 |
| 26 | How to bypass AWS SQS (Simple Queue Service) 256kb payload limit | 10:33 |
| 27 | Introduction Project #1 | 01:05 |
| 28 | Real world examples of data ingestors | 04:00 |
| 29 | Creating a twitter developer application and Authentication Token | 06:17 |
| 30 | Building your first social ingestor twitter (Part 1) | 01:23 |
| 31 | Building your first social ingestor twitter (Part 2) | 03:35 |
| 32 | Building your first social ingestor twitter Rate Limits (Part 3) | 08:53 |
| 33 | Building your first social ingestor twitter Handle (Part 4) | 12:11 |
| 34 | Building your first social ingestor twitter Handle (Part 5) | 07:53 |
| 35 | Basic fundamentals of SMTP and transactional email Services | 04:21 |
| 36 | Creating your first background email worker (Part 1) | 11:49 |
| 37 | Creating your first background email worker (Part 2) | 11:23 |
| 38 | Creating your first background email worker (Part 3) | 03:21 |
| 39 | Quick start guide: Getting started with PyCharm IDE (Mac) | 07:55 |
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