Bexhoma Python Package

Benchmark Experiment Host Manager for Orchestration of DBMS Benchmarking Experiments in Clouds

Maintenance GitHub release

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 [2] 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.

See the homepage and the documentation.

Installation

  1. Download the repository: https://github.com/Beuth-Erdelt/Benchmark-Experiment-Host-Manager

  2. Install the package pip install bexhoma

  3. Make sure you have a working kubectl installed
    (Also make sure to have access to a running Kubernetes cluster - for example Minikube)

  4. Adjust configuration [tbd in detail]

    1. Rename k8s-cluster.config to cluster.config

    2. Set name of context, namespace and name of cluster in that file

  5. Install data [tbd in detail]
    Example for TPC-H SF=1:

    • Run 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-source and
      kubectl delete deployment -l app=bexhoma -l component=data-source.

  6. Install result folder
    Run kubectl create -f k8s/pvc-bexhoma-results.yml

Quickstart

The repository contains a tool for running TPC-H (reading) queries at MonetDB and PostgreSQL.

  1. Run tpch run -sf 1 -t 30.

  2. You can watch status using bexperiments status while running.
    This is equivalent to python cluster.py status.

  3. After benchmarking has finished, run bexperiments dashboard to connect to a dashboard. You can open dashboard in browser at http://localhost:8050.
    This is equivalent to python cluster.py dashboard
    Alternatively you can open a Jupyter notebook at http://localhost:8888.

More Informations

For full power, use this tool as an orchestrator as in [2]. 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 [2] and evaluator [1]. 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.

References

[1] A Framework for Supporting Repetition and Evaluation in the Process of Cloud-Based DBMS Performance Benchmarking

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

[2] Orchestrating DBMS Benchmarking in the Cloud with Kubernetes

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)