The TeaStore is a micro-service reference and test application to be used in benchmarks and tests. The TeaStore emulates a basic web store for automatically generated, tea and tea supplies. As it is primarily a test application, it features UI elements for database generation and service resetting in addition to the store itself.

The TeaStore is a distributed micro-service application featuring five distinct services plus a registry. Each service may be replicated without limit and deployed on separate devices as desired. Services communicate using REST and using the Netflix Ribbon client side load balancer. Each service also comes in a pre-instrumented variant that uses Kieker to provide detailed information about the TeaStore's actions and behavior.

Check out our Getting Started Guide for information on how to use the TeaStore:

1. Deploying the TeaStore
   1. Run as Multiple Single Service Containers
   2. Run the TeaStore using Docker Compose
   3. Run the TeaStore on a Kubernetes Cluster
   4. Run the TeaStore with helm templates
2. Using the TeaStore for Testing and Benchmarking
   1. Generating Load
      1. LIMBO HTTP Load Generator
      2. JMeter™
   2. Instrumenting the TeaStore
      1. Docker containers with Kieker
      2. OpenTracing with Kubernetes and Istio
3. Building and Customizing the TeaStore

The TeaStore was first published in Proceedings of the 26th IEEE International Symposium on the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS2018). If you use the TeaStore please cite the following publication:

@inproceedings{KiEiScBaGrKo2018-MASCOTS-TeaStore,
  author = {J{\'o}akim von Kistowski and Simon Eismann and Norbert Schmitt and Andr{\'e} Bauer and Johannes Grohmann and Samuel Kounev},
  title = {{TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research}},
  booktitle = {Proceedings of the 26th IEEE International Symposium on the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems},
  series = {MASCOTS '18},
  year = {2018},
  month = {September},
  location = {Milwaukee, WI, USA},
}

For an example of a large-scale TeaStore setup we refer to Microservices: A Performance Tester’s Dream or Nightmare? and the corresponding replication package.

@inproceedings{10.1145/3358960.3379124,
  author = {Eismann, Simon and Bezemer, Cor-Paul and Shang, Weiyi and Okanovi\'{c}, Du\v{s}an and van Hoorn, Andr\'{e}},
  title = {Microservices: A Performance Tester's Dream or Nightmare?},
  year = {2020},
  booktitle = {Proceedings of the ACM/SPEC International Conference on Performance Engineering},
  pages = {138–149},
  series = {ICPE '20},
}

The TeaStore is used as the demo application in the Cisco Full Stack Observability Workshop and as a case study in a number of scientific publications:

• A. Horn, H.M. Fard, F. Wolf. Multi-objective Hybrid Autoscaling of Microservices in Kubernetes Clusters. In Euro-Par 2022: Parallel Processing, pp 233–250. 2022. https://doi.org/10.1007/978-3-031-12597-3_15
• M. Elsaadawy, A. Lohner, R. Wang, J. Wang, and B. Kemme. DyMonD: dynamic application monitoring and service detection framework. In Proceedings of the 22nd International Middleware Conference: Demos and Posters, pp- 8-9. 2021. https://doi.org/10.1145/3491086.3492471
• D. Sokolowski, P. Weisenburger, and G. Salvaneschi. Automating serverless deployments for DevOps organizations. In ESEC/FSE 2021: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 57-69. 2021. https://doi.org/10.1145/3468264.3468575
• L. Liao, J. Chen, H. Li, Y. Zeng, W. Shang, C. Sporea, A. Toma, and S. Sajedi. Locating Performance Regression Root Causes in the Field Operations of Web-based Systems: An Experience Report. In IEEE Transactions on Software Engineering. 2021. https://doi.org/10.1109/TSE.2021.3131529
• J. Flora, P. Gonçalves, M. Teixeira, and N. Antunes. My Services Got Old! Can Kubernetes Handle the Aging of Microservices? In IEEE International Symposium on Software Reliability Engineering Workshops. 2021. https://doi.org/10.1109/ISSREW53611.2021.00042
• M. Torquato, P. Maciel, and M. Vieira. PyMTDEvaluator: A Tool for Time-Based Moving Target Defense Evaluation: Tool description paper. In IEEE 32nd International Symposium on Software Reliability Engineering. 2021. https://doi.org/10.1109/ISSRE52982.2021.00045
• J. Keim, S. Schulz, D. Fuchß, C. Kocher, J. Speit, and A. Koziolek. Trace Link Recovery for Software Architecture Documentation. In European Conference on Software Architecture, pp. 101-116. 2021. https://doi.org/10.1007/978-3-030-86044-8_7
• J. Grohmann, M. Straesser, A. Chalbani, S. Eismann, Y. Arian, N. Herbst, N Peretz, and S. Kounev. 2021. SuanMing: Explainable Prediction of Performance Degradations in Microservice Applications. In Proceedings of the ACM/SPEC International Conference on Performance Engineering, pp. 165-176. 2021. https://doi.org/10.1145/3427921.3450248
• V. Rao, V. Singh, K. S. Goutham, B. U. Kempaiah, R. J. Mampilli, S. Kalambur, and D. Sitaram. 2021. *Scheduling Microservice Containers on Large Core Machines through Placement and Coalescing.*https://jsspp.org/papers21/vishal-rao.pdf
• D. Monschein, M. Mazkatli, R. Heinrich, and A. Koziolek. 2021. *nabling Consistency between Software Artefacts for Software Adaption and Evolution.*In 2021 IEEE 18th International Conference on Software Architecture (ICSA) (pp. 1-12). https://sdqweb.ipd.kit.edu/publications/pdfs/monschein2021a.pdf
• S. Eismann, C. Bezemer, W. Shang, D. Okanović, and A. van Hoorn. 2020. *icroservices: A Performance Tester's Dream or Nightmare?*In Proceedings of the ACM/SPEC International Conference on Performance Engineering (ICPE '20). Association for Computing Machinery, New York, NY, USA, 138–149. https://doi.org/10.1145/3358960.3379124
• J. Grohmann, P. Nicholson, J. Iglesias, S. Kounev, and D. Lugones. 2019. *onitorless: Predicting Performance Degradation in Cloud Applications with Machine Learning.*In Proceedings of the 20th International Middleware Conference (Middleware '19). Association for Computing Machinery, New York, NY, USA, 149–162. https://doi.org/10.1145/3361525.3361543
• M. Mazkatli, D. Monschein, J. Grohmann and A. Koziolek, Incremental Calibration of Architectural Performance Models with Parametric Dependencies 020 IEEE International Conference on Software Architecture (ICSA '2020), Salvador, Brazil, 2020, pp. 23-34, https://doi.org/10.1109/ICSA47634.2020.00011.
• J. Grohmann, S. Eismann, S. Elflein, J. V. Kistowski, S. Kounev and M. Mazkatli, Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS '19), Rennes, France, 2019, pp. 309-322, https://doi.org/10.1109/MASCOTS.2019.00042
• S. Gholami, A. Goli, C. Bezemer, and H. Khazaei. 2020. A Framework for Satisfying the Performance Requirements of Containerized Software Systems Through Multi-Versioning. In Proceedings of the ACM/SPEC International Conference on Performance Engineering (ICPE '20). Association for Computing Machinery, New York, NY, USA, 150–160. https://doi.org/10.1145/3358960.3379125
• N. Schmitt, L. Iffländer, A. Bauer and S. Kounev, Online Power Consumption Estimation for Functions in Cloud Applications. 2019 IEEE International Conference on Autonomic Computing (ICAC '19), Umea, Sweden, 2019, pp. 63-72, https://10.1109/ICAC.2019.00018
• S. Athlur, N. Sondhi, S. Batra, S. Kalambur and D. Sitaram, Cache Characterization of Workloads in a Microservice Environment 2019 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM '19), Bengaluru, India, 2019, pp. 45-50, https://10.1109/CCEM48484.2019.00010
• S. Caculo, K. Lahiri and S. Kalambur, Characterizing the Scale-Up Performance of Microservices using TeaStore. 2020 IEEE International Symposium on Workload Characterization (IISWC '2020), Beijing, China, 2020, pp. 48-59, https://10.1109/IISWC50251.2020.00014
• A. Goli, N. Mahmoudi, H. Khazaei, and O. Ardakanian. A Holistic Machine Learning-Based Autoscaling Approach for Microservice Applications. preprint
• W. Viktorsson, C. Klein and J. Tordsson, Security-Performance Trade-offs of Kubernetes Container Runtimes. 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS '2020), Nice, France, 2020, pp. 1-4, doi: https://10.1109/10.1109/MASCOTS50786.2020.9285946
• J. Martin, A. Kandasamy, and K. Chandrasekaran. CREW: Cost and Reliability aware Eagle‐Whale optimiser for service placement in Fog. Software: Practice and Experience 50.12 (2020): 2337-2360. https://doi.org/10.1002/spe.2896
• M. Tamiru, J. Tordsson, E. Elmroth, and G. Pierre. An Experimental Evaluation of the Kubernetes Cluster Autoscaler in the Cloud. In CloudCom 2020-12th IEEE International Conference on Cloud Computing Technology and Science. 2020. https://hal.inria.fr/hal-02958916
• E. Boza, C. Abad, S. Narayanan, B. Balasubramanian, and M. Jang. 2019. A Case for Performance-Aware Deployment of Containers. In Proceedings of the 5th International Workshop on Container Technologies and Container Clouds (WOC '19). Association for Computing Machinery, New York, NY, USA, 25–30. https://doi.org/10.1145/3366615.3368355

If your paper is missing from this list, open up an issue and we'll add it :)