Tag

DuckDB

6 posts

DuckDB GPU Acceleration After 2.0: Where It Wins, Where It Doesn't, and Why the Mac Is Different

DuckDB 2.0 turned DuckDB into a server, AWS is buying the company behind it, and the GPU question is back. DuckDB 2.0 also changed the query format my GPU extension reads, and gpudb 0.7.1 adapts to it: on an Apple M4 Max, 18 of 22 TPC-H SF10 queries run on the GPU under DuckDB 2.0, 3.7x faster end to end, with every answer identical to DuckDB's. Here is where a GPU helps DuckDB, how gpudb decides, and why the Mac is the interesting case.

Oct 30, 2026163

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Data Engineering27 min read

DuckDB vs SQL Server vs PostgreSQL for Analytics: SQL Server Standard's Ceilings, What They Cost, and What a Lakehouse Does Instead

SQL Server ships a real analytical engine and then fences Standard edition off from it: batch mode capped at 2 threads, a 32 GB columnstore cache, no batch mode on rowstore, no SIMD, no adaptive joins, all Enterprise at 3.83x the price per core. On one Apple M4 Max, DuckDB 2.0 ran six TPC-H-style queries over 60 million rows 18x to 47x faster than PostgreSQL 18, and DuckDB held to 2 threads still beat PostgreSQL on 8 workers by 3.7x to 8.6x. Where PostgreSQL's row store runs out, how a DuckDB lakehouse starts, what MotherDuck costs, and where DuckDB sits in AWS.

Oct 21, 2026249

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Data Engineering10 min read

sqljev: TypeSafe Jev's jev() for SQL Server, Postgres, Snowflake, BigQuery and DuckDB, on Jev or Open-Weight Laya

SQL cannot say 'the customer threatens to cancel'. sqljev adds jev(), jev_prob() and jev_choice() to SQL Server, PostgreSQL, MySQL, Snowflake, Databricks, BigQuery, Redshift and DuckDB, and answers them with Laya, an open-weight decision model that runs on your hardware, returns calibrated probabilities instead of text, and can be fine-tuned on your own tables. 140,000 decisions in 271 seconds on one laptop GPU; re-running all 13 queries, 0.9 seconds. Apache 2.0, version 0.1.0.

Sep 30, 2026400

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Data Engineering15 min read

DuckDB on the Apple Silicon GPU: Plain SQL, 19 of 22 TPC-H Queries on the Mac's Own GPU, and a Rule That Says Never Slower

DuckDB has no GPU backend of its own, and the GPU engines built for it need an NVIDIA card. gpudb 0.7 is my Apache-2.0 DuckDB extension for the GPU already inside your Mac, and for CUDA too. You write plain DuckDB SQL; the GPU takes a statement only where it has been measured faster than DuckDB on your own machine. On an Apple M4 Max, 19 of 22 TPC-H SF10 queries run on the Metal GPU at 1.06x to 48x with zero rows differing. The one row below parity is printed, not dropped.

Sep 22, 2026286

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Data Engineering12 min read

Polars vs DuckDB vs ArrowMetal GPU on Apple Silicon: Sort and Group-By Benchmarks

Polars, DuckDB and ArrowMetal on an Apple M4 Max: sort and group-by benchmarks at 10M and 50M rows, wall time next to CPU time, and the rows where the CPU is still ahead.

Sep 13, 2026433

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Data Engineering8 min read

The First SQL Engine for Apple Silicon GPUs Is Now a DuckDB Community Extension

In May 2026 I shipped gpudb v0.1 — the first SQL execution engine targeting Apple Silicon GPUs, built as a DuckDB extension with a CUDA backend on Linux. Three releases later, the project crossed two lines at once. v0.3.0's streaming-aggregate rewrite reached parity with native DuckDB on end-to-end TPC-H queries — the worst cell improved roughly 100×, from 11.05 s to 0.109 s. And gpudb became an official DuckDB Community Extension: INSTALL gpudb FROM community now works in any DuckDB ≥ 1.5.5, signed, no flags. This is the full arc — what v0.1 proved, what v0.2 honestly lost, what v0.3 fixed, and why the next GPU frontier is joins.

Aug 11, 2026383

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