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https://github.com/FudanSELab/train-ticket

Train Ticket - A Benchmark Microservice System
https://github.com/FudanSELab/train-ticket

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Train Ticket - A Benchmark Microservice System

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README 

        

# 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](https://github.com/FudanSELab/train-ticket/wiki).



## Service Architecture Graph

![architecture](./image/2.png)



## Quick Start

We provide k8s deployment to quickly deploy our application:  [Using Kubernetes](#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

```bash

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

cd train-ticket/

```



#### 2. Deploy the application

### For Quick Start

```bash

make deploy



```



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



```bash

make deploy Namespace=yournamespace

```



### Deploy Mysql Clusters For Each Services



```bash

make deploy DeployArgs="--independent-db"

```



### With Moinitorig

```bash

make deploy DeployArgs="--with-monitoring"

```



### With Distributed Tracing

```bash

make deploy DeployArgs="--with-tracing"

```



### Deploy All 

```bash

make deploy DeployArgs="--all"

```



### Customise Deployment

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



```bash

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](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](https://github.com/FudanSELab/train-ticket/wiki/Installation-Guide).



## Test scripts

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



## Screenshot

![screenshot](./image/main_interface.png)

In order to know how to use the application, you can refer to [the User Guide](https://github.com/FudanSELab/train-ticket/wiki/User-Guide).



## Communication



* [FAQ](https://github.com/FudanSELab/train-ticket/wiki/FAQ)

* [Submit an issue](https://github.com/FudanSELab/train-ticket/issues)

* [Open a pull request](https://github.com/FudanSELab/train-ticket/pulls)



## Information



* [Release Note](https://github.com/FudanSELab/train-ticket/wiki/Release-Note)



## Serverless Train Ticket



We have released a serverless version of Train Ticket.



* [serverless-trainticket](https://github.com/FudanSELab/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](https://www.springer.com/journal/10664/), Volume 27, 25, 2022.


Download:[[PDF](https://link.springer.com/content/pdf/10.1007/s10664-021-10063-9.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](https://dblp.uni-trier.de/db/conf/icse/icse2022.html)) , Pittsburgh, USA, May, 2022.


Download:[[PDF](https://dl.acm.org/doi/pdf/10.1145/3510003.3510180)]






	

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](https://dblp.uni-trier.de/db/conf/icse/seip2021.html#LiuH0LZGLOW21)) , Madrid, Spain, May, 2021.


Download:[[PDF](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9402058)]







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](https://arxiv.org/pdf/2106.07321.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](https://dblp.uni-trier.de/db/conf/sigsoft/fse2020.html)) , Virtual Event, USA, November, 2020.


Download:[[PDF](https://dl.acm.org/doi/pdf/10.1145/3368089.3417066)]







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](https://dblp.uni-trier.de/db/conf/sigsoft/fse2019.html)) , Tallinn, Estonia, August 2019. 


Download: [[PDF](https://cspengxin.github.io/publications/fse19-zhou-microservice.pdf)] [[BibTeX](https://dblp.uni-trier.de/rec/bibtex/conf/sigsoft/Zhou0X0JLXH19)] 







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](https://www.computer.org/web/tse) , To appear.   
 

Download: [[PDF](https://cspengxin.github.io/publications/tse19-msdebugging.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](http://ase2018.com/)) , Short Paper, Montpellier, France, September 2018. 


Download: [[PDF](https://cspengxin.github.io/publications/ase18-debugmicroservice.pdf)] [[BibTeX](https://dblp.uni-trier.de/rec/bibtex/conf/kbse/ZhouPX0LJD18)] 


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](https://www.icse2018.org/)) , Posters, Gothenburg, Sweden, May 2018. 


Download: [[PDF](https://cspengxin.github.io/publications/icse18poster-microservices.pdf)] [[BibTeX](https://dblp.uni-trier.de/rec/bibtex/conf/icse/ZhouPX0XJZ18)]