bexhoma 0.9.11

This python tools helps managing DBMS benchmarking experiments in a Kubernetes-based HPC cluster environment. It enables users to configure hardware / software setups for easily repeating tests over varying configurations.

Benchmark Experiment Host Manager (Bexhoma)

Orchestrating cloud-native DBMS benchmarking experiments on Kubernetes.

Bexhoma manages benchmark experiments of Database Management Systems (DBMS) in a Kubernetes-based cluster environment. It lets you configure hardware and software setups and repeat tests across varying configurations — all with a single command.

Benchmarks: YCSB · TPC-H · TPC-DS · TPC-C (HammerDB and Benchbase)

Tested DBMSs: Citus (Hyperscale), Clickhouse, CockroachDB, Dragonfly, Exasol, IBM DB2, MariaDB, MariaDB Columnstore, MemSQL (SingleStore), MonetDB, MySQL, OmniSci (HEAVY.AI), Oracle DB, PostgreSQL, SQL Server, SAP HANA, TiDB, TimescaleDB, Vertica, YugabyteDB

Tested cloud platforms: Amazon Web Services · Google Cloud · Microsoft Azure · IBM Cloud · Oracle Cloud · Minikube

Bexhoma acts as the orchestrator [2] for distributed parallel benchmarking experiments. The basic workflow is: start a containerised DBMS, deploy monitoring, import data, run benchmarks, and shut everything down — with a single command. A more advanced workflow plans a sequence of such experiments, runs them as a batch, and joins results for comparison.

Bexhoma also supports scaling out data-loading and benchmarking drivers to simulate cloud-native environments, as described in [4]. See example results from A Cloud-Native Adoption of Classical DBMS Performance Benchmarks and Tools.

Documentation · Issue tracker

Installation

1. Install the package:

   pip install bexhoma
   

   This requires Java and build tools.

2. Ensure kubectl is installed and configured for a running Kubernetes cluster (e.g. Minikube) with dynamic PVC provisioning.

3. Copy and adjust the cluster configuration:

   cp k8s-cluster.config cluster.config
   

   Set the context name, namespace, and cluster name. Ensure resultfolder points to an existing directory on your local filesystem. See the full configuration guide.

4. Optionally adjust the shared volume manifests before the first run:

   • k8s/pvc-bexhoma-results.yml — set storageClassName (ReadWriteMany) and storage size
   • k8s/pvc-bexhoma-data.yml — same

   All other components (monitoring, message queue, evaluator) are deployed automatically on first use.

Optional: Install a conda environment: conda create -n "bexhoma" python=3.14.2 ipython

Optional: Install Jupyter for notebooks: conda install -c conda-forge jupyter

Quickstart

Each command below installs PostgreSQL, loads data, runs the benchmark, and prints a summary. Monitor progress with bexperiments status while running.

YCSB

bexhoma ycsb run -ms 1 -tr -sf 1 --workload a -dbms PostgreSQL \
  -tb 16384 -nlp 1 -nlt 64 -nlf 4 -nbp 1,8 -nbt 64 -nbf 2,3

Loads 1 million rows, then runs workload A (50% reads / 50% updates, 1 million operations) targeting 32,768 ops/s (16,384 × 2) and 49,152 ops/s (16,384 × 3). Each target is tested with 64 driver threads as a single pod and again split across 8 pods.

See more details at https://bexhoma.readthedocs.io/en/latest/Example-YCSB.html

TPC-C (HammerDB)

bexhoma hammerdb run -ms 1 -tr -sf 16 -sd 5 -dbms PostgreSQL \
  -nlt 16 -nbp 1,2 -nbt 16

Loads a TPC-C schema with 16 warehouses using 16 loader threads, then benchmarks for 5 minutes with 16 virtual users — first in a single pod, then split across 2 pods.

See more details at https://bexhoma.readthedocs.io/en/latest/Example-HammerDB.html

TPC-C (Benchbase)

bexhoma benchbase run -ms 1 -tr -sf 16 -sd 5 -dbms PostgreSQL \
  -nbp 1,2 -nbt 16 -nbf 16 -tb 1024

Loads a TPC-C schema with 16 warehouses, then benchmarks for 5 minutes targeting 16,384 ops/s (1,024 × 16) with 16 threads — first in a single pod, then split across 2 pods.

See more details at https://bexhoma.readthedocs.io/en/latest/Example-Benchbase.html

TPC-H

bexhoma tpch run -ms 1 -dbms PostgreSQL

Loads 1 GB of TPC-H data (scale factor 1) and runs all 22 decision-support queries with a single client.

See more details at https://bexhoma.readthedocs.io/en/latest/Example-TPC-H.html

TPC-DS

bexhoma tpcds run -ms 1 -dbms PostgreSQL

Loads 1 GB of TPC-DS data (scale factor 1) and runs all 99 decision-support queries with a single client.

See more details at https://bexhoma.readthedocs.io/en/latest/Example-TPC-DS.html

Architecture

Bexhoma deploys Prometheus for monitoring and cAdvisor for resource metrics alongside every DBMS container. For analytical benchmarks the dbmsbenchmarker package [3] is used as query executor and evaluator [1,2]. For transactional workloads, HammerDB, Benchbase, and YCSB act as drivers [4].

See the images/ folder for more details.

Contributing

We welcome contributions of all kinds — code, documentation, bug reports, and more. Please first read the contribution guide.

If you find a bug, please open an issue on the issue tracker and include:

• Operating system and architecture
• Python version
• Bexhoma version (or git commit / date)
• Full traceback

References

If you use Bexhoma in work contributing to a scientific publication, please cite [2] or [1]:

[1] Erdelt P.K. (2021). A Framework for Supporting Repetition and Evaluation in the Process of Cloud-Based DBMS Performance Benchmarking. TPCTC 2020, LNCS 12752, Springer.
https://doi.org/10.1007/978-3-030-84924-5_6

[2] Erdelt P.K. (2022). Orchestrating DBMS Benchmarking in the Cloud with Kubernetes. TPCTC 2021, LNCS 13169, Springer.
https://doi.org/10.1007/978-3-030-94437-7_6

[3] Erdelt P.K., Jestel J. (2022). DBMS-Benchmarker: Benchmark and Evaluate DBMS in Python. JOSS 7(79), 4628.
https://doi.org/10.21105/joss.04628

[4] Erdelt P.K. (2024). A Cloud-Native Adoption of Classical DBMS Performance Benchmarks and Tools. TPCTC 2023, LNCS 14247, Springer.
https://doi.org/10.1007/978-3-031-68031-1_9

[5] Erdelt P.K., Rabl T. (2026). Benchmarking Multi-Tenant Architectures in PostgreSQL. EDBT 2026.
https://doi.org/10.48786/edbt.2026.46