Comparing Platforms

Run the same benchmark on multiple databases to compare performance.

Overview

BenchBox makes it easy to compare platforms because:

Same benchmark runs identically on all platforms
Same data (shared generation)
Same validation criteria

Shared Generated Data

BenchBox generates benchmark data once per benchmark and scale factor (compressed .tbl files), then every platform loads from the same dataset. This gives you an apples-to-apples comparison: differences in results reflect engine performance, not data differences. A second run at the same scale factor reuses the generated data instead of regenerating it.

Choosing Table Mode

Use --table-mode to control how data is registered before query execution:

benchbox run --platform duckdb --benchmark tpch --scale 0.1 --table-mode native

benchbox run --platform duckdb --benchmark tpch --scale 0.1 --table-mode external

The first command uses the default mode, which materializes native tables. The second registers the files externally (views or external tables).

--table-mode external is useful when you want direct file-based comparisons across engines.

Quick Comparison: DuckDB vs SQLite

benchbox run --platform duckdb --benchmark tpch --scale 0.1 --output ./benchmark_runs

benchbox run --platform sqlite --benchmark tpch --scale 0.1 --output ./benchmark_runs

benchbox results --limit 2

--output takes a directory for generated data; result files land in the runs root (benchmark_runs/ next to your checkout, or BENCHBOX_OUTPUT_DIR). Copy the two result paths from benchbox results and compare them:

benchbox compare ./benchmark_runs/results/<duckdb-result>.json ./benchmark_runs/results/<sqlite-result>.json

The SQLite run reuses the same generated data as the DuckDB run.

Example output (actual results vary based on hardware, configuration, and workload):

BenchBox Platform Comparison

...
Speedup Ratio: 3.7x

Adding Cloud Platforms

Compare local and cloud performance:

benchbox run --platform duckdb --benchmark tpch --scale 1 --output ./benchmark_runs

benchbox run --platform snowflake --benchmark tpch --scale 1 --output ./benchmark_runs
benchbox run --platform bigquery --benchmark tpch --scale 1 --output ./benchmark_runs

benchbox run --platform snowflake --benchmark tpch --scale 1 --table-mode external \
  --platform-option staging_root=s3://bucket/benchbox/ --output ./benchmark_runs
benchbox run --platform athena --benchmark tpch --scale 1 --table-mode external \
  --platform-option staging_root=s3://bucket/benchbox/ --output ./benchmark_runs
benchbox run --platform bigquery --benchmark tpch --scale 1 --table-mode external \
  --platform-option staging_root=gs://bucket/benchbox/ --output ./benchmark_runs

The first command is the local baseline. The cloud runs require credentials. The --table-mode external examples use file-backed registration. Locate the result files with benchbox results --limit and pass them to benchbox compare.

Platform Requirements

Platform	Setup Required
DuckDB	None (embedded)
SQLite	None (embedded)
DataFusion	None (embedded)
Snowflake	Account + credentials
BigQuery	Project + credentials
Databricks	Workspace + warehouse
Redshift	Cluster + credentials
ClickHouse	Server + credentials

See Platform Documentation for setup guides.

DataFrame Platforms

Compare SQL and DataFrame execution:

benchbox run --platform duckdb --benchmark tpch --scale 0.1 --output ./benchmark_runs

benchbox run --platform polars-df --benchmark tpch --scale 0.1 --output ./benchmark_runs

The first command is SQL execution, the second is DataFrame execution with Polars. Find the two result files with benchbox results --limit 2 and pass them to benchbox compare.

Best Practices

Use Same Scale Factor

benchbox run --platform duckdb --benchmark tpch --scale 1
benchbox run --platform snowflake --benchmark tpch --scale 1

benchbox run --platform duckdb --benchmark tpch --scale 0.1
benchbox run --platform snowflake --benchmark tpch --scale 10

The first pair is correct because both platforms use the same scale factor. The second pair is incorrect because the scale factors differ, so the results are not comparable.

Run Multiple Iterations

For statistical significance, run multiple times:

for i in 1 2 3; do
  benchbox run --platform duckdb --benchmark tpch --output ./benchmark_runs
done

Each run writes a new result file; list them with benchbox results --limit 3.

Consider Cold vs Warm

First run includes caching overhead. For warm comparisons:

benchbox run --platform duckdb --benchmark tpch --output ./benchmark_runs

benchbox run --platform duckdb --benchmark tpch --output ./benchmark_runs

Power tests already run one warm-up iteration plus three measured iterations by default. For an extra-primed comparison, treat the first command as the cache-priming run and compare using the second run’s result file.

Comparison Script

For systematic platform evaluation:

import glob
import json
import os
import subprocess

PLATFORMS = ["duckdb", "sqlite", "datafusion"]
SCALE = 0.1

for platform in PLATFORMS:
    runs_root = f"./runs-{platform}"
    subprocess.run(
        [
            "benchbox", "run",
            "--platform", platform,
            "--benchmark", "tpch",
            "--scale", str(SCALE),
            "--output", "./benchmark_runs",
            "--non-interactive",
        ],
        env={**os.environ, "BENCHBOX_OUTPUT_DIR": runs_root},
        check=True,
    )

for platform in PLATFORMS:
    paths = glob.glob(f"./runs-{platform}/results/*.json")
    latest = max(paths, key=os.path.getmtime)
    with open(latest) as f:
        data = json.load(f)
    total_ms = data.get("summary", {}).get("timing", {}).get("total_ms", 0)
    print(f"{platform}: {total_ms / 1000:.1f}s")

The script runs TPC-H on each platform into its own runs root, reads the newest result file per platform, and prints total measured query time. It is a standalone Python 3 script.

Next Steps

DataFrame Benchmarking - SQL vs DataFrame comparison
Platform Selection Guide - Choosing a platform