Package: bexhoma
Benchmark Experiment Host Manager (Bexhoma)This Python tools helpsmanaging 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 theorchestrator[2] for distributed parallel benchmarking experiments in a Kubernetes Cloud.
This has been tested at Amazon Web Services, Google Cloud, Microsoft Azure, IBM Cloud, Oracle Cloud, and at Minikube installations,
running with Citus Data (Hyperscale), Clickhouse, CockroachDB, Exasol, IBM DB2, MariaDB, MariaDB Columnstore, MemSQL (SingleStore), MonetDB, MySQL, OmniSci (HEAVY.AI), Oracle DB, PostgreSQL, SQL Server, SAP HANA, TimescaleDB, Vertica and YugabyteDB.Benchmarks included are YCSB, TPC-H and TPC-C (HammerDB and Benchbase version).The basic workflow is [1,2]: start a containerized version of the DBMS, install monitoring software, import data, run benchmarks 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.It is also possible to scale-out drivers for generating and loading data and for benchmarking to simulate cloud-native environments as in [4].
Seeexampleresults as presented inA Cloud-Native Adoption of Classical DBMS Performance Benchmarks and Toolsand how they are generated.See thehomepageand thedocumentation.If you encounter any issues, please report them to ourGithub issue tracker.InstallationDownload the repository:https://github.com/Beuth-Erdelt/Benchmark-Experiment-Host-ManagerInstall the packagepip install bexhomaMake sure you have a workingkubectlinstalled.(Also make sure to have access to a running Kubernetes cluster - for exampleMinikube)(Also make sure, you can create PV via PVC and dynamic provisioning)AdjustconfigurationCopyk8s-cluster.configtocluster.configSet name of context, namespace and name of cluster in that fileMake sure theresultfolderis set to a folder that exists on your local filesystemOther components like the shared data and result directories, the message queue and the evaluator are installed automatically when you start an experiment. Before that, you might want to adjustResult directory:https://github.com/Beuth-Erdelt/Benchmark-Experiment-Host-Manager/blob/master/k8s/pvc-bexhoma-results.ymlstorageClassName: must be an available storage class of typeReadWriteManyin your clusterstorage: size of the directoryData directory:https://github.com/Beuth-Erdelt/Benchmark-Experiment-Host-Manager/blob/master/k8s/pvc-bexhoma-data.ymlstorageClassName: must be an available storage class of typeReadWriteManyin your clusterstorage: size of the directoryQuickstartYCSBRunpython ycsb.py -ms 1 -dbms PostgreSQL -workload a run.This installs PostgreSQL and runs YCSB workload A with varying target. The driver is monolithic with 64 threads. The experiments runs a second time with the driver scaled out to 8 instances each having 8 threads.You can watch status usingbexperiments statuswhile running. This is equivalent topython cluster.py status.After benchmarking has finished, you will see a summary.For further inspections, runbexperiments dashboardto connect to a dashboard. You can open dashboard in browser athttp://localhost:8050. This is equivalent topython cluster.py dashboard. Alternatively you can open a Jupyter notebook athttp://localhost:8888.See more details athttps://bexhoma.readthedocs.io/en/latest/Example-YCSB.htmlTPC-HRunpython tpch.py -ms 1 -dbms PostgreSQL run.This installs PostgreSQL and runs TPC-H at scale factor 1. The driver is monolithic.You can watch status usingbexperiments statuswhile running. This is equivalent topython cluster.py status.After benchmarking has finished, you will see a summary.For further inspections, runbexperiments dashboardto connect to a dashboard. This is equivalent topython cluster.py dashboard.  You can open a Jupyter notebook athttp://localhost:8888.See more details athttps://bexhoma.readthedocs.io/en/latest/Example-TPC-H.htmlMore InformationsFor full power, use this tool as an orchestrator as in [2]. It also starts a monitoring container usingPrometheusand a metrics collector container usingcAdvisor. For analytical use cases, the Python packagedbmsbenchmarker, [3], is used as query executor and evaluator as in [1,2].
For transactional use cases, HammerDB's TPC-C, Benchbase's TPC-C and YCSB are used as drivers for generating and loading data and for running the workload as in [4].See theimagesfolder for more details.Contributing, Bug ReportsIf you have any question or found a bug, please report them to ourGithub issue tracker.
In any bug report, please let us know:Which operating system and hardware (32 bit or 64 bit) you are usingPython versionBexhoma version (or git commit/date)Traceback that occurs (the full error message)We are always looking for people interested in helping with code development, documentation writing, technical administration, and whatever else comes up.
If you wish to contribute, please first read thecontribution sectionor visit thedocumentation.ReferencesIf you use Bexhoma in work contributing to a scientific publication, we kindly ask that you cite our application note [2] or [1]:[1]A Framework for Supporting Repetition and Evaluation in the Process of Cloud-Based DBMS Performance BenchmarkingErdelt 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[2]Orchestrating DBMS Benchmarking in the Cloud with KubernetesErdelt 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[3]DBMS-Benchmarker: Benchmark and Evaluate DBMS in PythonErdelt P.K., Jestel J. (2022).
DBMS-Benchmarker: Benchmark and Evaluate DBMS in Python.
Journal of Open Source Software, 7(79), 4628https://doi.org/10.21105/joss.04628[4]A Cloud-Native Adoption of Classical DBMS Performance Benchmarks and ToolsErdelt P.K. (2023)http://dx.doi.org/10.13140/RG.2.2.29866.18880