Bexhoma Python Package
Benchmark Experiment Host Manager for Orchestration of DBMS Benchmarking Experiments in Clouds
Benchmark Experiment Host Manager
This Python tools helps managing benchmark experiments of Database Management Systems (DBMS) in a Kubernetes-based High-Performance-Computing (HPC) cluster environment. It enables users to configure hardware / software setups for easily repeating tests over varying configurations.
It serves as the orchestrator  for distributed parallel benchmarking experiments in a Kubernetes Cloud. This has been tested at Amazon Web Services, Google Cloud, Microsoft Azure, IBM Cloud und Oracle Cloud and at Minikube installations.
The basic workflow is [1,2]: start a virtual machine, install monitoring software and a database management system, import data, run benchmarks (external tool) and shut down everything with a single command. A more advanced workflow is: Plan a sequence of such experiments, run plan as a batch and join results for comparison.
Download the repository: https://github.com/Beuth-Erdelt/Benchmark-Experiment-Host-Manager
Install the package
pip install bexhoma
Make sure you have a working
(Also make sure to have access to a running Kubernetes cluster - for example Minikube)
Adjust configuration [tbd in detail]
Set name of context, namespace and name of cluster in that file
Install data [tbd in detail]
Example for TPC-H SF=1:
kubectl create -f k8s/job-data-tpch-1.yml
When job is done, clean up with
kubectl delete job -l app=bexhoma -l component=data-sourceand
kubectl delete deployment -l app=bexhoma -l component=data-source.
Install result folder
kubectl create -f k8s/pvc-bexhoma-results.yml
The repository contains a tool for running TPC-H (reading) queries at MonetDB and PostgreSQL.
tpch run -sf 1 -t 30.
You can watch status using
bexperiments statuswhile running.
This is equivalent to
python cluster.py status.
After benchmarking has finished, run
bexperiments dashboardto connect to a dashboard. You can open dashboard in browser at
This is equivalent to
python cluster.py dashboard
Alternatively you can open a Jupyter notebook at
For full power, use this tool as an orchestrator as in . It also starts a monitoring container using Prometheus and a metrics collector container using cAdvisor. It also uses the Python package dbmsbenchmarker as query executor  and evaluator . See the images folder for more details.
This module has been tested with Brytlyt, Citus, Clickhouse, DB2, Exasol, Kinetica, MariaDB, MariaDB Columnstore, MemSQL, Mariadb, MonetDB, MySQL, OmniSci, Oracle DB, PostgreSQL, SingleStore, SQL Server and SAP HANA.
Erdelt P.K. (2021) A Framework for Supporting Repetition and Evaluation in the Process of Cloud-Based DBMS Performance Benchmarking. In: Nambiar R., Poess M. (eds) Performance Evaluation and Benchmarking. TPCTC 2020. Lecture Notes in Computer Science, vol 12752. Springer, Cham. https://doi.org/10.1007/978-3-030-84924-5_6
Erdelt P.K. (2022) Orchestrating DBMS Benchmarking in the Cloud with Kubernetes. In: Nambiar R., Poess M. (eds) Performance Evaluation and Benchmarking. TPCTC 2021. Lecture Notes in Computer Science, vol 13169. Springer, Cham. https://doi.org/10.1007/978-3-030-94437-7_6
(old, slightly outdated docs)