Designing Distributed Systems - Labs

Labs for Designing Distributed Systems

The samples in this lab are written with the reader of this book in mind: https://azure.microsoft.com/en-us/resources/designing-distributed-systems/en-us/ and will guide you through the steps in designing and deploying distributed systems in Microsoft Azure.

1.1. Single Node Pattern: Ambassador

In this lab we'll guide you through the steps to implement the Ambassador pattern with NGINX in Kubernetes by deploying a request splitting service that will split 10% of the incoming HTTP requests to an experimental server. This request splitting service can then be used in a scenario where you want to test a new version of a back-end service with only a subset of the requests.

Go to lab: 1.1. Request Splitter

1.2. Single Node Pattern: Circuit Breaker Pattern

In this lab we'll guide you through the steps to implement the Ambassador pattern as a Circuit Breaker with NGINX Plus and Kubernetes. The Circuit Breaker patterns is extremely useful in scenarios where you want to help failing back-end servers to recover from failure, re-route traffic and perform rate limiting.

Go to lab: 1.2. Single Node Pattern

2.1. Serving Pattern: Load Balancing Server

In this lab we'll guide you through the steps to deploy a replicated load balancing service that will process requests for the definition of English words. The requests will be processed by a few small replicated NodeJS servers that you will deploy in Kubernetes using a pre-existing Docker image.

Go to lab: 2.1. Replicated Load Balanced Services

2.2. Serving Pattern: Decorator Function

In this lab you will apply the Decorator Pattern to implement a function in Kubeless that adds default values and performs transformations to the input of an HTTP RESTful API.

Go to lab: 2.2. Decorator Function

3. Batch Computation Pattern

In this lab you will apply the Copier, Filter, Splitter and Join patterns to implement a fully functional containerized and batch-processing thumbnail generator in Kubernetes that uses a pre-generated Docker image of the popular FFMPEG media conversion tool.

Go to lab: 3. Batch Computational Pattern

4. Contributors

Roles	Author(s)
Project Lead / Architect / Lab Manuals	Manfred Wittenbols (Canviz) @mwittenbols
Sponsor / Support	Phil Evans (Microsoft)
Sponsor / Support	Anand Chandramohan (Microsoft)

5. Version history

Version	Date	Comments
1.0	April 23, 2018	Initial release

Disclaimer

THIS CODE IS PROVIDED AS IS WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING ANY IMPLIED WARRANTIES OF FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR NON-INFRINGEMENT.

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