Everyone says you should. BenchBox makes it easy to do. Use as CLI, Python library, or MCP for AI assistants. No Docker. No compilers. Just pip install.
# CLI - Quick benchmarking
benchbox run --platform duckdb --benchmark tpch
# MCP - AI assistant integration
"Run TPC-H on DuckDB" # Claude executes via MCP
# Library - Deep Python integration
from benchbox import TPCH
Minimal requirements. Python-only core with optional extras for cloud platforms. `pip install` and go.
TPC standards, academic, industry, real-world, time-series, SQL primitives, AI/ML, and experimental workloads.
SQL databases (DuckDB, ClickHouse, cloud platforms) and DataFrame libraries (Polars, Pandas).
Automatic SQL translation. Same benchmark queries run on local and cloud databases.
10MB to 100TB! No need to download pre-generated files.
MCP server for Claude Code and AI assistants. Run benchmarks with natural language.
From measurements to answers
The Results Explorer turns BenchBox output into a browsable view of performance. Use the public corpus to understand how platforms behave on the same workload, or open your own result file in the browser before you share it.
Open the Results Explorer →TPC-H · Scale factor 10
| Version | Geomean | Power@Size |
|---|---|---|
| DuckDB v2.0.0… | 128 ms | 281,041 |
| DuckDB v1.5.5 | 152 ms | 236,191 |
| DuckDB v1.4.4 | 168 ms | 213,021 |
| DuckDB v1.3.2 | 189 ms | 198,175 |
Browse by benchmark or platform, then narrow the public corpus to results that match the workload and scale you care about.
Compare compatible runs, inspect query-level timings, and see where one platform gains or loses time instead of relying on a single headline number.
Open a BenchBox result JSON file locally in the browser. Review its charts and run receipt without uploading it or adding it to the public rankings.
Official industry standards for comparing databases
Standard data warehouse benchmark with 22 business intelligence queries.
Research benchmarks from academia
Real-world benchmarks from practitioners
Benchmarks built on public real-world datasets
Workloads for time-series databases and columnar monitoring engines
Fundamental database operation testing
Vector similarity and other AI-shaped analytical workloads
Experimental benchmarks for specialized testing
In-process and local columnar databases
Traditional relational databases
Enterprise cloud data warehouses and lakehouses
Open source MPP and federated SQL engines
Cloud-managed Spark for lakehouse and data lake analytics
Optimized for time-stamped data
Native DataFrame APIs instead of SQL
Convert benchmark data to modern columnar formats for optimized storage, ACID transactions, and time travel capabilities.
Columnar storage for fast analytics
ACID transactions, time travel, and schema evolution
# Convert TPC-H data to different formats
benchbox convert --input ./tpch_sf1 --format parquet
benchbox convert --input ./tpch_sf1 --format delta
benchbox convert --input ./tpch_sf1 --format iceberg
benchbox convert --input ./tpch_sf1 --format ducklakeBenchBox includes an MCP (Model Context Protocol) server for Claude Code and other AI assistants. Run benchmarks with natural language instead of memorizing CLI flags.
Pick a surface, platform, benchmark, and scale. Copy the prompt into your coding agent. Defaults to a safe local DuckDB + TPC-H quickstart.
claude mcp add benchbox -- uv run python -m benchbox.mcpNatural language commands for benchmarking workflows
"What benchmarks are available?"
"Which platforms support TPC-DS?"
Explore 22 benchmarks and 50 platforms without reading documentation.
"Run TPC-H on DuckDB at scale 0.1"
"Compare Polars and Pandas on SSB"
Run benchmarks without memorizing CLI syntax or options.
"Which queries were slowest?"
"Compare results from my last two runs"
Get AI-powered analysis of performance patterns and regressions.
uv add benchbox# Quick TPC-H benchmark on DuckDB
benchbox run --platform duckdb --benchmark tpch --scale 0.1
# Preview on cloud before spending credits
benchbox run --platform databricks --benchmark tpch --dry-run ./previewfrom benchbox import TPCH
# Initialize and generate benchmark data
tpch = TPCH(scale_factor=0.1)
data_files = tpch.generate_data()
# Get schema and queries
create_sql = tpch.get_create_tables_sql()
query = tpch.get_query(1) # Q1: Pricing Summary Report