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.
It 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.
The TeaStore supports three different deployment models, traditional deployment in an application server, individual docker containers or in a Kubernetes cluster. All three deployment models are described in detail on our GitHub page.
- Docker 16.0
- Source code repository
- Docker images
- Jóakim von Kistowski
- Simon Eismann
- Norbert Schmitt
- André Bauer
- Johannes Grohmann
- Long Bui
- Samuel Kounev
Simon Eismann, simon.eismann(at)uni-wuerzburg.de
Sanderring 2, 97070 Würzburg, Germany
Apache License 2.0
Related publications and projects
- von Kistowski, Jóakim, et al. "TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research." 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS). IEEE, 2018.
- Grohmann, Johannes, et al. "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). IEEE, 2019.
- Schmitt, Norbert, et al. "Online Power Consumption Estimation for Functions in Cloud Applications." 2019 IEEE International Conference on Autonomic Computing (ICAC). IEEE, 2019.
- Grohmann, Johannes, et al. "Monitorless: Predicting Performance Degradation in Cloud Applications with Machine Learning." MIDDLEWARE. 2019.