Database Benchmarking Tools Compared

Tags concept comparison olap oltp

A comprehensive comparison of open-source database benchmarking tools—their strengths, trade-offs, and when to use each.

Overview

No single benchmarking tool covers all use cases. OLTP vs OLAP, Java vs Python, TPC-only vs custom workloads—each tool makes different trade-offs. Understanding these trade-offs matters because picking the wrong tool wastes time, produces irrelevant results, or locks you into a narrow platform subset.

This page compares four major open-source tools: HammerDB, BenchBase, LakeBench, and BenchBox. All are actively maintained, all are free, and each dominates a specific niche.

The Four Contenders

HammerDB

Attribute

Value

Language

Tcl (93.9%), GPL v3.0

Version

5.0 (April 2025)—Tcl 9.0 rewrite

Focus

OLTP (TPROC-C) + limited OLAP (TPROC-H)

Databases

Oracle, SQL Server, PostgreSQL, MySQL/MariaDB, IBM Db2

Key metric

NOPM (New Orders Per Minute)

Unique strength

Decades of enterprise credibility, TPC Council sponsorship

BenchBase

Attribute

Value

Language

Java (96.8%), successor to OLTPBench

Version

CalVer releases (2023+)

Focus

OLTP + academic research workloads

Databases

PostgreSQL, MySQL, MariaDB, SQLite, CockroachDB, Spanner

Benchmarks

18+ (TPC-C, TPC-H, Twitter, YCSB, SEATS, etc.)

Unique strength

Extensibility, academic rigor, diverse workload mix

LakeBench

Attribute

Value

Language

Python (100%), pip-installable

Focus

Lakehouse ELT pipelines on Delta Lake

Platforms

Spark variants (Fabric, Synapse, HDInsight), DuckDB, Polars

Benchmarks

ELTBench, TPC-H, TPC-DS, ClickBench

Unique strength

End-to-end ELT lifecycle, Microsoft ecosystem integration

BenchBox

Attribute

Value

Language

Python (100%), uv/pip-installable

Focus

Broad OLAP analytics across platform spectrum

Platforms

26+ (DuckDB, Snowflake, BigQuery, Databricks, Polars, etc.)

Benchmarks

18 (TPC-H, TPC-DS, TPC-DI, SSB, ClickBench, plus originals)

Unique strength

Platform breadth, embedded data generation, DataFrame benchmarking

OLTP vs OLAP: The Fundamental Split

The biggest decision is workload type, not tool features.

Characteristic

OLTP

OLAP

Transaction size

Small, frequent

Large, infrequent

Query complexity

Simple CRUD

Complex joins/aggregations

Concurrency model

Many concurrent users

Few concurrent queries

Key metric

Transactions/minute

Query latency, throughput

TPC standard

TPC-C

TPC-H, TPC-DS

OLTP tools: HammerDB, BenchBase OLAP tools: BenchBox, LakeBench (partial HammerDB via TPROC-H)

Warning

Running TPC-H on a tool optimized for TPC-C (or vice versa) produces misleading results. The tool’s architecture assumes certain workload patterns.

Head-to-Head Comparison

Dimension

HammerDB

BenchBase

LakeBench

BenchBox

Primary workload

OLTP

OLTP

OLAP + ELT

OLAP

Language

Tcl

Java

Python

Python

Install complexity

Medium (binaries)

Medium (Maven/Java)

Low (pip)

Low (uv/pip)

Database breadth

6 enterprise DBs

7 SQL DBs

5 Spark/DF engines

26+ platforms

Benchmark count

2 (TPROC-C/H)

18+

4

18

Cloud DW support

Limited (Redshift)

Spanner only

Fabric/Synapse

Snowflake, BigQuery, Databricks, Redshift

DataFrame support

No

No

Partial (Polars, Daft)

Full (8 libraries)

TPC compliance

Derived (TPROC-*)

Derived

No

No

Active development

Yes (v5.0 Apr 2025)

Yes (CalVer 2023+)

Yes

Yes

License

GPL v3

Apache 2.0

MIT

MIT

When to Use Each Tool

Use HammerDB When…

  • Benchmarking enterprise OLTP (Oracle, SQL Server, Db2)

  • You need TPC-C derived metrics for hardware/config comparisons

  • Your organization requires TPC Council credibility

  • Running transactional throughput tests at scale

  • You have a Windows-heavy environment (native support)

Avoid when: Testing cloud DWs, analytical queries, or DataFrame libraries.

Use BenchBase When…

  • Conducting academic database research

  • You need OLTP workload variety beyond TPC-C (Twitter, YCSB, SEATS)

  • Testing CockroachDB or Spanner (first-class support)

  • Your team prefers Java/Maven toolchains

  • You want fine-grained workload control (rates, mixtures, distributions)

Avoid when: Testing cloud data warehouses or OLAP workloads beyond TPC-H.

Use LakeBench When…

  • Evaluating Spark-based lakehouse engines (Fabric, Synapse, HDInsight)

  • Testing end-to-end ELT pipelines (not just queries)

  • Your data is on Delta Lake (required format)

  • Working in Microsoft Azure ecosystem

  • You need ELTBench (unique to LakeBench)

Avoid when: Testing non-Spark platforms or pure SQL analytics.

Use BenchBox When…

  • Comparing cloud data warehouses (Snowflake vs BigQuery vs Databricks)

  • Benchmarking embedded analytics (DuckDB, DataFusion, SQLite)

  • Benchmarking DataFrame libraries — BenchBox is the only tool with full DataFrame support:

    • Polars, Pandas, PySpark DataFrame, DataFusion, Modin, Dask, cuDF (GPU)

    • Native DataFrame API translations (not SQL-over-DataFrame)

    • Side-by-side SQL vs DataFrame comparisons on the same data

  • You need benchmark variety (18 benchmarks, TPC standards + industry)

  • Your team prefers Python tooling

  • Evaluating the full OLAP platform spectrum in one framework

Avoid when: Running OLTP transactional benchmarks.

Note

DataFrame support is unique to BenchBox. HammerDB and BenchBase are SQL-only. LakeBench has partial support (Polars, Daft) but is Spark-focused. If you need to benchmark Polars vs Pandas vs DuckDB, BenchBox is the only option.

Combining Tools

The best evaluation strategy often uses multiple tools.

Common Combinations

  1. HammerDB + BenchBox: Test both OLTP and OLAP on PostgreSQL

  2. BenchBase + BenchBox: Academic OLTP research + cloud DW comparison

  3. LakeBench + BenchBox: Spark ELT pipelines + cross-platform OLAP

Example Workflow

# OLTP baseline with HammerDB
hammerdbcli <<< "dbset db pg; buildschema; vuset vu 16; vucreate; vustatus; vurun"

# OLAP comparison with BenchBox
benchbox run --platform postgresql --benchmark tpch --scale 10
benchbox compare -p duckdb -p postgresql --scale 10

Integration Opportunities

  • Export BenchBox results → feed into HammerDB comparisons

  • Use LakeBench ELT metrics → BenchBox query benchmarks

  • Combine NOPM (HammerDB) + geometric mean (BenchBox) in reports

What No Tool Does Well

Gap

Description

Streaming benchmarks

Kafka, Flink, Spark Streaming—none of the four has mature support

Graph databases

Neo4j, Neptune—BenchBase has theoretical extensibility but no implementations

Vector search

Emerging AI/ML workloads—all tools lag behind

Real-time mixed workloads

HTAP (hybrid transactional/analytical) benchmarks are nascent

Cost modeling

Only BenchBox and LakeBench attempt cost estimation; both are incomplete

Decision Tree

Is your primary workload OLTP (transactional)?
├── Yes → Is it academic research?
│         ├── Yes → BenchBase
│         └── No  → HammerDB
└── No (OLAP/analytics) → Do you need DataFrame benchmarking?
                          ├── Yes → BenchBox (only option with full DataFrame support)
                          └── No  → Is it Spark lakehouse ELT?
                                    ├── Yes → LakeBench
                                    └── No  → BenchBox

Key Takeaways

  1. Workload type is the primary discriminator—don’t force an OLTP tool on OLAP work

  2. DataFrame support is unique to BenchBox—if you need to benchmark Polars, Pandas, or other DataFrame libraries, BenchBox is the only choice

  3. Platform coverage matters—check if your target database is supported

  4. Language preference is secondary but affects integration and maintenance

  5. Combining tools is often the right answer for comprehensive evaluation

Get Started with BenchBox

uv add benchbox
benchbox run --platform duckdb --benchmark tpch --scale 0.1

References

See Also