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

Quick Start

We provide k8s deployment to quickly deploy our application: Using Kubernetes.

Using Kubernetes

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

Presequisite

• An existing Kubernetes cluster
• Helm supported, you can see https://helm.sh/docs/helm/helm_install/ for helm install
• PVC supported, you can see https://openebs.io/docs/2.12.x/user-guides/installation for localPV support.

1. Clone the Repository

git clone --depth=1 https://github.com/FudanSELab/train-ticket.git 
cd train-ticket/

2. Deploy the application

For Quick Start

make deploy

Note: if you want specify namespace, set Namespace paramter:

make deploy Namespace=yournamespace

Deploy Mysql Clusters For Each Services

make deploy DeployArgs="--independent-db"

With Moinitorig

make deploy DeployArgs="--with-monitoring"

With Distributed Tracing

make deploy DeployArgs="--with-tracing"

Deploy All

make deploy DeployArgs="--all"

Customise Deployment

You can freely combine parameters for custom deployment， for example, deploy with monitoring and tracing:

make deploy DeployArgs="--with-tracing --with-monitoring"

Reset Deployment

make reset-deploy
# if you specify namespace when deploy, set namespace as well when reset
# make reset-deploy Namespace=yournamespace

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.

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.

Test scripts

Use scripts to test train-ticket: https://github.com/FudanSELab/train-ticket-auto-query

Screenshot

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

Communication

• FAQ
• Submit an issue
• Open a pull request

Information

• Release Note

Serverless Train Ticket

We have released a serverless version of Train Ticket.

• serverless-trainticket

Paper Reference

Bowen Li, Xin Peng, Qilin Xiang, Hanzhang Wang, Tao Xie, Jun Sun, Xuanzhe Liu.
Enjoy your observability: an industrial survey of microservice tracing and analysis
Empirical Software Engineering, Volume 27, 25, 2022.
Download:[PDF]

Chenxi Zhang, Xin Peng, Chaofeng Sha, Ke Zhang, Zhenqing Fu, Xiya Wu, Qingwei Lin, Dongmei Zhang
DeepTraLog: Trace-Log Combined Microservice Anomaly Detection through Graph-based Deep Learning
In Proceedings of the 44th International Conference on Software Engineering (ICSE 2022) , Pittsburgh, USA, May, 2022.
Download:[PDF]

Dewei Liu, Chuan He, Xin Peng, Fan Lin, Chenxi Zhang, Shengfang Gong, Ziang Li, Jiayu Ou, Zheshun Wu
MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systems
In Proceedings of the 43rd IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP 2021) , Madrid, Spain, May, 2021.
Download:[PDF]

Qilin Xiang, Xin Peng, Chuan He, Hanzhang Wang, Tao Xie, Dewei Liu, Gang Zhang, Yuanfang Cai
No Free Lunch: Microservice Practices Reconsidered in Industry
arXiv preprint arXiv:2106.07321, 2021.
Download:[PDF]

Xiaofeng Guo, Xin Peng, Hanzhang Wang, Wanxue Li, Huai Jiang, Dan Ding, Tao Xie, Liangfei Su
Graph-based trace analysis for microservice architecture understanding and problem diagnosis
In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2020) , Virtual Event, USA, November, 2020.
Download:[PDF]

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]