tpcds-benchmark

Automate TPC-DS benchmarking across SQL engines and compile comparative results.

Updated Mar 18, 2026
One-click install
npx skills add https://github.com/kindofluke/albatross-data --skill tpcds-benchmark-kindofluke
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: tpcds-benchmark
Source: https://github.com/kindofluke/albatross-data/tree/main/.vibe/skills/tpcds-benchmark
Command: npx skills add https://github.com/kindofluke/albatross-data --skill tpcds-benchmark-kindofluke

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Runs and compares TPC-DS benchmarks across Legacy Sirius, Super Sirius, or DuckDB CPU baseline, enabling performance visibility across engines and aiding engineering decisions.

Core Features & Use Cases

  • Orchestrates data generation for TPC-DS datasets at multiple scales.
  • Executes benchmarks across Legacy Sirius, Super Sirius, and DuckDB CPU, and aggregates results.
  • Use Case: A data analytics team evaluates GPU-accelerated Sirius variants against CPU DuckDB to choose the best engine for analytics workloads.

Quick Start

Run the full TPC-DS benchmark workflow across engines using the provided scripts.

Frequently Asked Questions about tpcds-benchmark

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I benchmark TPC-DS queries across DuckDB and GPU-accelerated Sirius engines?

You can benchmark TPC-DS queries by automating data generation, running 99 queries across DuckDB CPU, Legacy Sirius, and Super Sirius, and compiling comparative results to evaluate engine performance.

Can I compare GPU vs CPU performance for TPC-DS workloads?

Yes, you can compare GPU vs CPU performance by controlling GPU behavior through environment variables and config files during the TPC-DS benchmark execution across Legacy Sirius, Super Sirius, and DuckDB.

How does TPC-DS benchmark validation identify query failures and GPU fallbacks?

TPC-DS benchmark validation identifies failures, timeouts, and GPU fallbacks by parsing log files and timing outputs after executing the 99 queries across the configured SQL engines.

What is the best way to run TPC-DS data generation at multiple scales for analytics?

The best way to run TPC-DS data generation at multiple scales is using the provided scripts to orchestrate dataset creation before executing the 99 benchmark queries across your target engines.

Do I need specific environment variables to run TPC-DS benchmarks on Super Sirius?

Yes, you need specific environment variables and config files to run TPC-DS benchmarks on Super Sirius, as these settings control GPU versus CPU behavior and ensure proper execution across engines.

Why does my TPC-DS benchmark show inconsistent results between DuckDB and Sirius engines?

Inconsistent TPC-DS benchmark results between DuckDB and Sirius engines often stem from GPU fallbacks or timeouts, which the validation process detects by parsing logs and timing outputs.