/train-ticket-test

An experimental version of Train Ticket Benchmark Microservice System

Primary LanguageC

Train Ticket：A Benchmark Microservice System

The project is a train ticket booking system based on microservice architecture which contains 41 microservices. The programming languages and frameworks it used are as below.

  • Java - Spring Boot, Spring Cloud
  • Node.js - Express
  • Python - Django
  • Go - Webgo
  • DB - Mongo、MySQL

You can get more details at Wiki Pages.

Service Architecture Graph

architecture

Quick Start

We provide two options to quickly deploy our application: Using Docker Compose and Using Kubernetes.

Using Docker Compose

The easiest way to get start with the Train Ticket application is by using Docker and Docker Compose.

If you don't have Docker and Docker Compose installed, you can refer to the Docker website to install them.

Presequisite

  • Docker
  • Docker Compose

1. Clone the Repository

git clone https://github.com/FudanSELab/train-ticket.git
cd train-ticket/

2. Start the Application

docker-compose -f deployment/quickstart-docker-compose/quickstart-docker-compose.yml up

Once the application starts, you can visit the Train Ticket web page at http://localhost:8080.

Jaeger

we provide a Tracing System based on Jaeger. You can visit the Jaeger Webpage at http://localhost:16686 to view traces in the system.

Using Kubernetes

Here is the steps to deploy the Train Ticket onto any existing Kubernetes cluster.

Presequisite

  • An existing Kubernetes cluster

1. Clone the Repository

git clone https://github.com/FudanSELab/train-ticket.git
cd train-ticket/

2. Deploy the application

cd deployment/quickstart-k8s-deployment/

# Deploy the databases
kubectl apply -f quickstart-ts-deployment-part1.yml
# Deploy the services
kubectl apply -f quickstart-ts-deployment-part2.yml
# Deploy the UI Dashboard
kubectl apply -f quickstart-ts-deployment-part3.yml

3. Run kubectl get pods to see pods are in a ready state

4. Visit the Train Ticket web page at http://[Node-IP]:32677.

Jaeger

we provide a Tracing System based on Jaeger. You can visit the Jaeger Webpage at http://[Node-IP]:32688 to view traces in the system.

Build From Source

In the above, We use pre-built images to quickly deploy the application.

If you want to build the application from source, you can refer to the Installation Guide.

Screenshot

screenshot In order to know how to use the application, you can refer to the User Guide.

Communication

Information

Paper Reference

Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chao Ji, Dewei Liu, Qilin Xiang, and Chuan He.
Latent Error Prediction and Fault Localization for Microservice Applications by Learning from System Trace Logs.
In Proceedings of the 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2019) , Tallinn, Estonia, August 2019.
Download: [PDF] [BibTeX]


Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chao Ji, Wenhai Li, and Dan Ding.
Fault Analysis and Debugging of Microservice Systems: Industrial Survey, Benchmark System, and Empirical Study.
IEEE Transactions on Software Engineering , To appear.
Download: [PDF]


Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Wenhai Li, Chao Ji, and Dan Ding.
Delta Debugging Microservice Systems.
In Proceedings of 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018) , Short Paper, Montpellier, France, September 2018.
Download: [PDF] [BibTeX]
An extended version to appear in IEEE Transactions on Services Computing.


Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chenjie Xu, Chao Ji, and Wenyun Zhao.
Poster: Benchmarking Microservice Systems for Software Engineering Research.
In Proceedings of the 40th International Conference on Software Engineering (ICSE 2018) , Posters, Gothenburg, Sweden, May 2018.
Download: [PDF] [BibTeX]