Event Driven - Microservices Reference Application

The primary goal of this sample is to explain following software-architecture concepts and container-technologies like:

• Microservices
• CQRS
• Event Sourcing (Using Kafka)
• Domain Driven Design (DDD)
• Eventual Consistency
• Docker
• Docker-Compose

and methods and tools to make load testing and monitoring memory and cpu consumption like :

• Bombardier
• dotnet-counters

Architecture:

Description:

This repo contains a sample application simulates a food delivery journey between ordering and notification operations after successfull transaction. The system consists of the following parts.

• Producer/Order API - An API which accepts post request to make transactional operation of an order (Assuming a new order and/or payment operation has been successfully executed) Then it sends an OrderPaid event message to the event bus.
• Consumer/Email and Notification Service - A Hosted Service (.Net Core Worker Service) which subscribes the Kafka Topic (orderPaid) and sends notification/email about the operation.

How to set up and run the project

You can run the bellow command from the /source/Producer/Order.Api/ directory to build docker images for Producer/Order API

docker build -f "Dockerfile" -t orderproducer_image ..

and the below command from the /source/Consumer/EmailAndNotificationService/ for Consumer/Email and Notification Service

docker build -f "Dockerfile" -t orderconsumer_image ..

after building docker images of two application you can run the below command from the /setup/ directory to run both 2 services and service bus enviroment. (Kafka and Zookeeper)

docker-compose up

Load testing and performance monitoring

To test the application i use bombardier which is written in Go programming language to simulate many HTTP(S) request concurently sent from different clients.

You can run the below command from the /diagnostics/bombardier/ directory to build docker images for bombardier

docker build -t alpine/bombardier .

To monitor our applications memory consumption and cpu usage i use dot-net-counters which was introduced with .NET CORE 3.0

You can run the below commands using command prompt to monitor diagnostics of Producer/Order API and Consumer/Email and Notification Service

PLEASE change your command prompt font-size to 12 for better readibilty.

docker exec -it orderproducer dotnet counters monitor -p 1 System.Runtime Microsoft.AspNetCore.Hosting

docker exec -it orderconsumer dotnet counters monitor -p 1 System.Runtime

In the image below, you can see the CPU utilization and memory consuptions of our applications on waiting mode. (No request and load)

Let's make load testing using bombardier

You can run the below command using powershell or command prompt to make concurent http call to Producer/Order API. It simulates 50 http calls per second from 50 different clients during 100 seconds.

Running and result

Run the below command using powershell or command prompt.

docker run -ti --rm alpine/bombardier -c 50 -d 100s --rate 50 -m POST "http://host.docker.internal:5000/api/Order" -H "Content-Type: application/json" -f "orderEventData.json"
Bombarding http://host.docker.internal:5000/api/Order for 1m40S using 50 connection(s)
[=======================================================================================================================================================================================================================================] 1m40sDone!
Done!
Statistics        Avg      Stdev        Max
  Reqs/sec       48.32     453.54     26595.33
  Latency      1.13s     147.09ms      1.81s
  Latency Distribution
     50%   613.75ms
     75%   639.65ms
     90%   707.76ms
     99%      1.20s
  HTTP codes:
    1xx - 0, 2xx - 5001, 3xx - 0, 4xx - 0, 5xx - 0
    others - 0
  Throughput:   36.01KB/s

Please see the content of HTTP POST request.

{
	"customerId":322332, 
	"customerName": "Emre",
	"customerLastName":"Alper",
	"customerEmail":"[email protected]",
	"deliveryAddress":"55 Water Street 28th Floor New York, NY 10041 USA",
	"restaurantId":66789, 
	"restaurantName": "Quick China",
	"orderNote": "please don't ring the doorbell baby is sleeping",
	"paymentMethodType":2,
	"orderProducts":[

		{
			"productId":784567,
			"productName":"Philadelphia Roll Menu (16 Pieces)",
			"quantity":1,
			"unitCost":17.5,
			"currencyType":1
		},
		{
			"productId":784589,
			"productName":"California Roll Half Menu (8 Pieces)",
			"quantity":1,
			"unitCost":8.25,
			"currencyType":1
		}
	]
}

In the image below, you can monitor real-time diagnostics to detect memory leakage or cpu usage of the producer and consumer applications.

Read and Watch further and references

• Confluent Schema Registry
• A Deep Dive into Apache Kafka
• Using Akka between rest and Kafka
• Performance Testing Techniques