alt.hn

4/22/2026 at 2:49:02 PM

DuckDB 1.5.2 – SQL database that runs on laptop, server, in the browser

 https://duckdb.org/2026/04/13/announcing-duckdb-152

by janandonly

4/22/2026 at 4:04:07 PM

DuckDB also runs in Excel, by the way, via the free xlwings Lite add-in that you can install from the add-in store. It’s using the Python package and allows to write scripts, custom functions, as well as use a Jupyter-like notebook workflow.

by fzumstein

4/22/2026 at 4:27:46 PM

If you start with Excel, I'll counter with Postgres: https://github.com/duckdb/pg_duckdb. I haven't found the time to check this on one of our installation, though.

by goerch

4/22/2026 at 9:41:41 PM

I had a project a year ago that worked with lots and lots of csv files and it was just such a lifesaver. Doing a JOIN and WHERE on two (massive) csv files and getting instant results felt like magic. Saved me a lot of time on ad hoc queries. Also Claude being good with the tool is a bonus too. Easily my favourite piece of software recently.

by NorthSouthNorth

4/22/2026 at 3:57:00 PM

I benchmarked DuckDB 1.5.2 with the latest Java JDBC driver which now supports user defined functions. This allows very fast modifications https://sqg.dev/blog/java-duckdb-benchmark/

by uwemaurer

4/22/2026 at 6:45:00 PM

It's been a lifesaver for some analysis I had to do on 70GB of Cloudflare logs.

by mmarian

4/22/2026 at 6:55:25 PM

I'd be lost without it for log analysis. It's like a swiss army knife for making sense of disparate or large sets of data. So easy to pull up, so cooperative with data that's easy to compose from curl and bash, etc. It makes life so much easier

by steve_adams_86

4/22/2026 at 6:59:36 PM

Yep. And easy to reuse as well since it's just SQL.

by mmarian

4/22/2026 at 5:39:02 PM

Data engineer here: I use this all the time. It's amazing. For most of the data the sizes we often deal with it's perfect.

by gigatexal

4/22/2026 at 5:45:43 PM

> For most of the data the sizes we often deal with it's perfect.

Interested here: for me it works for out of core work. Where is the limit? On a related note: do you need to handle concurrency restrictions?

by goerch

4/23/2026 at 2:55:10 PM

From my very limited experience with duckdb, being a column based database, simple query also need loads entire columns and that uses a lots of RAM if a table has millions of rows. Perhaps there is a way to make it more memory friendly but I don't know how.

by infinet

4/22/2026 at 5:59:33 PM

i must be doing something wrong but if i try a huge join on a table bigger than my ram no matter the flags or the spill-to-disk modes enabled i get crashes. im sure im doing something wrong.

by gigatexal

4/22/2026 at 3:01:43 PM

duckdb is a generational technology innovation. insanely good ergonomics, great performance, it's awesome.

by whalesalad

4/22/2026 at 3:39:10 PM

Can confirm: together with `dbt` and `rill` I'm able do to [this](https://github.com/idesis-gmbh/GitHubExperiments/blob/master...) on my laptop.

by goerch

4/23/2026 at 8:04:28 PM

OP here. Thanks for sharing. This is why I keep coming back to HN.

by janandonly

4/22/2026 at 3:44:14 PM

Whoa, nice! I could see this being useful to people I work with. Do you think it would be a good setup for people who are technical but not great software developers? People who use basic R and Python for ETL and analysis, mostly.

by steve_adams_86

4/22/2026 at 3:50:29 PM

I'm using DuckDB in another project (on my laptop) where `NetworkX` fails due to the memory limit of 32 GB. So yes, as soon as you are doing out of core work I'd assume the combination to be quite powerful. Knowledge in SQL would be a plus, though.

by goerch

4/22/2026 at 4:14:40 PM

Why did you pick rill?

by esafak

4/22/2026 at 4:51:51 PM

It is a educational/R&D type project. We are more of backend developers and `rill` worked fine as a rapid visualization frontend with low learning curve for us.

Edit: still realizing that I can't use markdown on HN...

by goerch

4/22/2026 at 5:29:08 PM

I got introduced to it by Claude the other day as I was interrogating several GB of public csv files. Seemed magical as it out them all in parquet files and transformed what I needed into the normalized sqllite for my server. Coding agents seen quite comfortable with it!

by meetingthrower

4/22/2026 at 5:58:19 PM

claude + duckdb combo is legendary for doing quick analysis of huge datasets. every time i need to analyze a big ass csv (200mb+) or as you noted a parquet file or really anything columnar i'll tell claude, 'you have duckdb at your disposal for this' and within minutes it's all sorted (no pun intended)

by whalesalad

4/22/2026 at 3:47:54 PM

I use it almost daily. Any time I benchmark changes or analyze logs, I collect the data I need as CSV and analyze it with duckdb. The flexibility and ease makes it so I find so much more interesting information. It's indispensable to me now

by steve_adams_86

4/22/2026 at 7:15:42 PM

So is DuckDb a database or a cli tool to query all sorts of file format using SQL statements? I've used it as a CLI tool, somehow don't understand the comparison to a database, which stores your data reliably, besides responding to your SQL queries.

by yolo3000

4/22/2026 at 8:00:15 PM

It can act as an in-process database, like SQLite. You can import the library directly into your code.

by simonw

4/22/2026 at 7:26:13 PM

My personal use case is a replacement for pandas for ad hoc analysis in Jupyter notebooks, which I have to do very often these days. If I had to store the data I'd pick S3+Glue+Athena.

by mmarian

4/22/2026 at 8:47:34 PM

Along with parsing various file formats, you can create duckdb files to store tables, and make related views, schema, etc. They also have a newer ducklake tool

by biophysboy

4/22/2026 at 3:09:42 PM

Any opinions on DuckLake?

by esafak

4/22/2026 at 6:17:45 PM

I’ve had very good experience with it last year. I used it at large scale with data that had been in iceberg previously and it worked flawlessly. It’s only improved since. Highly recommend.

by erikcw

4/22/2026 at 7:00:25 PM

The problem space that ducklake solves is smaller, but it helped me to get a working metabase dashboard quickly on ~1tb of data with 128gb ram. Queries were much, much faster than all alternatives.

Some downsides are: No unique constraints with indexes (can accidentally shoot yourself in the foot with double ingestion), writing is a bit cumbersome if you already have parquet files.

by uniqueuid

4/22/2026 at 7:22:29 PM

With my enterprise hat on, I'd say Athena + S3 is good enough. Only use DuckDB for ad hoc analysis.

by mmarian

4/22/2026 at 4:01:19 PM

Seems stable enough, they patched a bunch of things.

by denom

4/22/2026 at 7:12:30 PM

I use duckdb often too, but the way it is being hyped in these comments makes me feel like I'm missing out on some insane usecase.

I basically use it to load csv, jsonl, parquet etc etc formats and do arbitrary transformations. Are people doing something else with it?

by porridgeraisin

4/22/2026 at 7:21:07 PM

Maybe you are unconsciously doing the right thing(TM) already? So try doing it with SQLite instead :)

by goerch

4/22/2026 at 7:18:30 PM

Probably because you probably don't have to do those arbitrary transformations that often. I do, being in a security-related role. But I wouldn't have recognized its usefulness in my previous roles as a front/backend dev.

by mmarian

4/22/2026 at 11:21:49 PM

loading data and doing arbitrary transformations turns out to be a huge PITA with most other tools over a long enough timescale.

by datadrivenangel

4/22/2026 at 6:19:20 PM

I found it unusable due to out of memory errors with a billion row 8 column dataset.

It needs manual tuning to avoid those errors and I couldn’t find the right incantation, nor should I need to - memory management is the job of the db, not me. Far too flakey for any production usage.

by andrewstuart

4/22/2026 at 6:37:21 PM

That sounds like a rather serious application. Did you file an issue?

by goerch

4/22/2026 at 8:21:41 PM

I filed many issues. They were aurtoclosed after 3 months of inactivity

by andriy_koval

4/22/2026 at 6:39:02 PM

No, I tried Clickhouse instead, which worked without crashing or manual memory tuning.

Search the issues of the duckdb GitHub there’s at least 110 open and closed oom (out of memory) and maybe 400 to 500 that reference “memory”.

by andrewstuart

4/22/2026 at 6:50:36 PM

> Search the issues of the duckdb GitHub there’s at least 110 open and closed oom (out of memory) and maybe 400 to 500 that reference “memory”.

Ah, missed this the first time around. Will check this out. And yes, I noticed that DuckDB rather aggressively tries to use the resources of your computer.

by goerch

4/22/2026 at 6:43:38 PM

Understood: SQLite is to Postgres as DuckDB is to ClickHouse.

by goerch

4/22/2026 at 6:45:30 PM

I don’t see the analogy, if you’re using it to excuse crashing on small data sets and indexes.

SQLite isn’t small and crashy, it’s small and reliable.

There’s something fundamentally wrong with the codebase/architecture if there’s so many memory problems.

And the absolute baseline requirement for a production database is no crashes.

by andrewstuart

4/22/2026 at 7:17:36 PM

I think the authors disagree with me, but I see it like a online analytical processing (OLAP) database, not like a OLTP (online transaction processing) database, so crashes are more tolerable.

by ciupicri

4/22/2026 at 7:07:37 PM

Agree with your assessment of small and reliable for SQLite. Disagree with your baseline requirement. ACID is more important for me and does not contain `No crashes`.

by goerch

4/22/2026 at 5:11:15 PM

Did they finally enable full SIMD or keep insisting its okay not to have it?

by ramraj07

4/22/2026 at 5:51:30 PM

Hm, our internal benchmarking shows something like a 30x speedup compared to SQLite (https://github.com/ClickHouse/ClickBench shows an even greater speedup due to not considering cache size). Calculating back on the envelope I'd estimate 8x for multithreading and 4x for SIMD. Should we expect even more?

by goerch

4/22/2026 at 5:39:56 PM

fwiw:

"Performance Does DuckDB use SIMD? DuckDB does not use explicit SIMD (single instruction, multiple data) instructions because they greatly complicate portability and compilation. Instead, DuckDB uses implicit SIMD, where we go to great lengths to write our C++ code in such a way that the compiler can auto-generate SIMD instructions for the specific hardware. As an example why this is a good idea, it took 10 minutes to port DuckDB to the Apple Silicon architecture."

https://duckdb.org/faq

by gigatexal

4/22/2026 at 7:26:21 PM

sqlite

by syngrog66

4/22/2026 at 7:50:41 PM

both

by goerch