DBMS Benchmarker Python Package

Maintenance GitHub release PyPI version .github/workflows/draft-pdf.yml DOI JOSS


DBMS-Benchmarker is a Python-based application-level blackbox benchmark tool for Database Management Systems (DBMS). It aims at reproducible measuring and easy evaluation of the performance the user receives even in complex benchmark situations. It connects to a given list of DBMS (via JDBC) and runs a given list of (SQL) benchmark queries. Queries can be parametrized and randomized. Results and evaluations are available via a Python interface and can be inspected with standard Python tools like pandas DataFrames. An interactive visual dashboard assists in multi-dimensional analysis of the results.

See the homepage and the documentation.

Key Features


  • is Python3-based

  • helps to benchmark DBMS

    • connects to all DBMS having a JDBC interface - including GPU-enhanced DBMS

    • requires only JDBC - no vendor specific supplements are used

    • benchmarks arbitrary SQL queries - in all dialects

    • allows planning of complex test scenarios - to simulate realistic or revealing use cases

    • allows easy repetition of benchmarks in varying settings - different hardware, DBMS, DBMS configurations, DB settings etc

    • investigates a number of timing aspects - connection, execution, data transfer, in total, per session etc

    • investigates a number of other aspects - received result sets, precision, number of clients

    • collects hardware metrics from a Prometheus server - hardware utilization, energy consumption etc

  • helps to evaluate results - by providing

    • metrics that can be analyzed by aggregation in multi-dimensions, like maximum throughput per DBMS, average CPU utilization per query or geometric mean of run latency per workload

    • predefined evaluations like statistics

    • in standard Python data structures

    • in Jupyter notebooks see rendered example

    • in an interactive dashboard

For more informations, see a basic example or take a look in the documentation for a full list of options.

The code uses several Python modules, in particular jaydebeapi for handling DBMS. This module has been tested with Clickhouse, DB2, Exasol, Hyperscale (Citus), Kinetica, MariaDB, MariaDB Columnstore, MemSQL, Mariadb, MonetDB, MySQL, OmniSci, Oracle DB, PostgreSQL, SingleStore, SQL Server, SAP HANA, TimescaleDB and Vertica.


Run pip install dbmsbenchmarker

Basic Usage

The following very simple use case runs the query SELECT COUNT(*) FROM test 10 times against one local MySQL installation. As a result we obtain an interactive dashboard to inspect timing aspects.


We need to provide

  • a DBMS configuration file, e.g. in ./config/connections.config

      'name': "MySQL",
      'active': True,
      'JDBC': {
        'driver': "com.mysql.cj.jdbc.Driver",
        'url': "jdbc:mysql://localhost:3306/database",
        'auth': ["username", "password"],
        'jar': "mysql-connector-java-8.0.13.jar"
  • the required JDBC driver, e.g. mysql-connector-java-8.0.13.jar

  • a Queries configuration file, e.g. in ./config/queries.config

    'name': 'Some simple queries',
    'connectionmanagement': {
          'timeout': 5 # in seconds
        'title': "Count all rows in test",
        'query': "SELECT COUNT(*) FROM test",
        'numRun': 10

Perform Benchmark

Run the CLI command: dbmsbenchmarker run -e yes -b -f ./config

  • -e yes: This will precompile some evaluations and generate the timer cube.

  • -b: This will suppress some output

  • -f: This points to a folder having the configuration files.

This is equivalent to python benchmark.py run -e yes -b -f ./config

After benchmarking has been finished we will see a message like

Experiment <code> has been finished

The script has created a result folder in the current directory containing the results. <code> is the name of the folder.

Evaluate Results in Dashboard

Run the command: dbmsdashboard

This will start the evaluation dashboard at localhost:8050. Visit the address in a browser and select the experiment <code>.

Alternatively you may use a Jupyter notebook, see a rendered example.

Benchmarking in a Kubernetes Cloud

This module can serve as the query executor [2] and evaluator [1] for distributed parallel benchmarking experiments in a Kubernetes Cloud, see the orchestrator for more details.


Limitations are:

  • strict black box perspective - may not use all tricks available for a DBMS

  • strict JDBC perspective - depends on a JVM and provided drivers

  • strict user perspective - client system, network connection and other host workloads may affect performance

  • not officially applicable for well known benchmark standards - partially, but not fully complying with TPC-H and TPC-DS

  • hardware metrics are collected from a monitoring system - not as precise as profiling

  • no GUI for configuration

  • strictly Python - a very good and widely used language, but maybe not your choice

Other comparable products you might like

  • Apache JMeter - Java-based performance measure tool, including a configuration GUI and reporting to HTML

  • HammerDB - industry accepted benchmark tool, but limited to some DBMS

  • Sysbench - a scriptable multi-threaded benchmark tool based on LuaJIT

  • OLTPBench -Java-based performance measure tool, using JDBC and including a lot of predefined benchmarks


[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