June 18, 2026
Duck, duck… boom
DuckDB Internals: Why Is DuckDB Fast? (Part 1)
The little database everyone won’t shut up about just got its origin story
TLDR: DuckDB’s new internals post explains how a tiny, easy-to-install database became shockingly fast and widely used. In the comments, people are obsessed with one thing: it feels ridiculously simple for a tool they say can handle serious work, and that’s turning curiosity into full-on fandom.
DuckDB’s latest deep dive is supposed to explain why this tiny database feels freakishly fast, but the real show is in the comments, where users sound less like customers and more like a fan club. The blog lays out the basics: DuckDB is a small, easy-to-install tool that runs right inside your app, opens files like Parquet, CSV, and JSON without a lot of setup, and somehow keeps punching way above its weight. It’s already showing up everywhere from dashboards to phone demos to products built by bigger companies. In plain English: this is the compact data tool that keeps embarrassing bulkier rivals.
And the community reaction? Pure adoration with a side of disbelief. One commenter flat-out says ease of use was the gateway drug, then DuckDB stayed because it’s “absurdly capable, versatile, and fast.” Another basically swoons over being able to type select * from 'data.json' like it’s a magic trick. The strongest mood in the thread is: how is something this simple also this powerful? That contrast is what has people hooked.
There’s also a mini culture clash brewing. One puzzled commenter asks why all the data scientists around them keep using DuckDB instead of old standbys like MySQL or PostgreSQL, giving the thread a mild “what do they know that I don’t?” energy. Meanwhile, extension builders are already calling DuckDB “data superglue,” which is either visionary branding or the start of a full-blown ecosystem takeover. Even the image of an iPhone in a box of dry ice running a giant benchmark feels like the kind of over-the-top flex the internet lives for.
Key Points
- •The article presents DuckDB as an in-process analytical SQL database that originated as a research project at CWI Amsterdam in 2019 and has since seen broad adoption.
- •It says DuckDB is widely used in notebooks, ETL pipelines, dashboards, CI test runners, embedded analytics, and commercial products built by companies including MotherDuck, Hex, Omni, Evidence, Fivetran, Rill, and Greybeam.
- •DuckDB is described as easy to deploy because it runs as a single binary under 20 MB, has no external dependencies, and can directly query Parquet, CSV, and JSON files.
- •The post identifies key architectural reasons for DuckDB’s speed, including in-process execution, columnar compressed storage with zonemaps, vectorized execution, morsel-driven parallelism, and snapshot isolation with optimistic MVCC.
- •Part 1 of the series focuses on the path from SQL input to query readiness and on the storage layer, contrasting DuckDB’s local in-process execution with server-based databases such as Snowflake, Postgres, BigQuery, and Redshift.