This repository contains a forked version of the TrainTicket microservice benchmark system. TrainTicket was originally developed at the University of Fudan, and is available on GitHub as well. This fork contains various faulty instances of TrainTicket that were developed as part of a student research project. The faulty instances are contained in several branches of the repository.
Please find the original README of TrainTicket below:
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.
We provide two options to quickly deploy our application: Using Docker Compose and Using Kubernetes.
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.
- Docker
- Docker Compose
git clone --depth=1 https://github.com/FudanSELab/train-ticket.git
cd train-ticket/docker-compose -f deployment/docker-compose-manifests/quickstart-docker-compose.yml upOnce the application starts, you can visit the Train Ticket web page at http://localhost:8080.
Here is the steps to deploy the Train Ticket onto any existing Kubernetes cluster.
- An existing Kubernetes cluster
git clone --depth=1 https://github.com/FudanSELab/train-ticket.git
cd train-ticket/cd deployment/kubernetes-manifests/quickstart-k8s
# 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.yml4. Visit the Train Ticket web page at http://[Node-IP]:32677.
There are many other quick deployment ways in deployment folder. For example, you can deploy this system with Jaeger, and then visit the Jaeger Webpage to view traces.
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.
In order to know how to use the application, you can refer to the User Guide.
We have released a serverless version of Train Ticket.
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]