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https://github.com/mspnp/microservices-reference-implementation

A reference implementation demonstrating microservices architecture and best practices for Microsoft Azure
https://github.com/mspnp/microservices-reference-implementation

aks azure azure-pipelines cicd kubernetes microservice microservices-architecture microservices-reference

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A reference implementation demonstrating microservices architecture and best practices for Microsoft Azure

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# Microservices Reference Implementation

Microsoft patterns & practices



This reference implementation shows a set of best practices for building and running a microservices architecture on Microsoft Azure, using Kubernetes.



> :heavy_exclamation_mark: The previous advanced Microservices Reference Implementation is now known as the [AKS Fabrikam Drone Delivery reference implementation](https://github.com/mspnp/aks-fabrikam-dronedelivery). The AKS Frabrikam Drone Delivery reference implementation is built on top the guidance forming the [AKS Baseline Cluster](https://github.com/mspnp/aks-secure-baseline). This basic Microservices Reference Implementation will remain here for your reference but we recommend you to consider basing your work on the [AKS Fabrikam Drone Delivery reference implementation](https://github.com/mspnp/aks-fabrikam-dronedelivery).



## Guidance



This project has a companion set of articles that describe challenges, design patterns, and best practices for building microservices architecture. You can find these articles on the Azure Architecture Center:



- [Designing, building, and operating microservices on Azure with Kubernetes](https://docs.microsoft.com/azure/architecture/microservices)



## Scenario



​Fabrikam, Inc. (a fictional company) is starting a drone delivery service. The company manages a fleet of drone aircraft. Businesses register with the service, and users can request a drone to pick up goods for delivery. When a customer schedules a pickup, a backend system assigns a drone and notifies the user with an estimated delivery time. While the delivery is in progress, the customer can track the location of the drone, with a continuously updated ETA.



## The Drone Delivery app



The Drone Delivery application is a sample application that consists of several microservices. Because it's a sample, the functionality is simulated, but the APIs and microservices interactions are intended to reflect real-world design patterns.



- Ingestion service. Receives client requests and buffers them.

- Scheduler service. Dispatches client requests and manages the delivery workflow.

- Supervisor service. Monitors the workflow for failures and applies compensating transactions.

- Account service. Manages user accounts.

- Third-party Transportation service. Manages third-party transportation options.

- Drone service. Schedules drones and monitors drones in flight.

- Package service. Manages packages.

- Delivery service. Manages deliveries that are scheduled or in-transit.

- Delivery History service. Stores the history of completed deliveries.



![](./architecture.png)



## Test results and metrics

The Drone Delivery application has been tested up to 2000 messages/sec:



|                                          | Replicas | ~Max CPU (mc) | ~Max Mem (MB) | Avg. Throughput*        | Max. Throughput*        | Avg (ms) | 50th (ms) | 95th (ms) | 99th (ms) |

|------------------------------------------|----------|---------------|---------------|-------------------------|-------------------------|----------|-----------|-----------|-----------|

| Nginx                                    | 1        | N/A           | N/A           | serve: 1595 reqs/sec    | serve: 1923 reqs/sec    | N/A      | N/A       | N/A       | N/A       |

| Ingestion                                | 10       | 474           | 488           | ingest: 1275 msgs/sec   | ingest: 1710 msgs/sec   | 251      | 50.1      | 1560      | 2540      |

| Workflow (receive messages)              | 35       | 1445          | 79            | egress: 1275 msgs/sec   | egress: 1710 msgs/sec   | 81.5     | 0         | 25.7      | 121       |

| Workflow (call backend services + mark message as complete) | 35       | 1445          | 79            | complete: 1100 msgs/sec | complete: 1322 msgs/sec | 561.8    | 447       | 1350      | 2540      |

| Package                                  | 50       | 213           | 78            | N/A                     | N/A                     | 67.5     | 53.9      | 165       | 306       |

| Delivery                                 | 50       | 328           | 334           | N/A                     | N/A                     | 93.8     | 82.4      | 200       | 304       |

| Dronescheduler                           | 50       | 402           | 301           | N/A                     | N/A                     | 85.9     | 72.6      | 203       | 308       |



*sources:

1. Serve: Visual Studio Load Test Throughout Request/Sec

2. Ingest: Azure Service Bus metrics Incoming Messages/Sec

3. Egress: Azure Service Bus metrics Outgoing Messages/Sec

4. Complete: AI Service Bus Complete dependencies  

5. Avg/50th/95th/99th: AI dependencies

6. CPU/Mem: Azure Monitor for Containers



## Deployment



To deploy the solution, follow the steps listed [here](./deployment.md).



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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [[email protected]](mailto:[email protected]) with any additional questions or comments.