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duckdb
duckdb-wasm
TypeScript2023–2026
duckdb / duckdb-wasm

duckdb-wasm

WebAssembly version of DuckDB

Web DevelopmentData & Databases
Explore on GitHubRead Hacker News discussions
11Distinct people
14Mentions
0Explicit recommenders
2.2KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

WebAssembly version of DuckDB

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year14 dated mentions

Observed mentions: 2023-02-13 16:14:18 UTC to 2026-10-21 17:43:01 UTC.

02462023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments20232024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 7 mentions; 2 submissions, 5 comments20242025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC303
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC725
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC202
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC211

What Hacker News says

14 sources
westurner·Oct 29, 2025
From "Show HN: TeaTime – distributed book library powered by SQLite, IPFS and GitHub" https://news.ycombinator.com/item?id=42264274 : >> phiresky/sql.js-httpvfs: https://github.com/phiresky/sql.js-httpvfs >> mmomtchev/sqlite-wasm-http: https://github.com/mmomtchev/sqlite-wasm-http >> This project is inspired from @phiresky/sql.js-httpvfs but uses the new official SQLite WASM distribution duckdb/duckdb-wasm: https://github.com/duckdb/duckdb-wasm
In Hosting SQLite Databases on GitHub Pages (2021)
thenaturalist·Dec 2, 2024
DuckDB should give you everything with regards to DevEx that you seem to be needing. You're not specifying what volume we're talking about here, but I'd be surprised if you can't go a long way with DuckDB. Ultimately the bottleneck for latency in a non-big data scenario will be memory? You can run it in a browser: https://github.com/duckdb/duckdb-wasm DuckDB's docs and blog are full of great content with regards to optimizing: How to tune workloads: https://duckdb.org/docs/guides/performancehow_to_tune_worklo... Performance guide: https://duckdb.org/docs/guides/performance/overview.html Optimizers in DuckDB: https://duckdb.org/2024/11/14/optimizers.html I'm sure there is more. If I were in your shoes, even if you don't settle on DuckDB, it would be my go to benchmark due to ease of use and unoptimized speed. Anything else out there like c
Read the rest
hDB (Clickhouse) or Apache Datafusion should be worth the effort. For a detailed benchmark of DuckDB vs. Clickhouse for an orientation, see: https://bicortex.com/duckdb-vs-clickhouse-performance-compar...
In Ask HN: How did you scale your analytics workloads (Postgres)?
jarpineh·Oct 15, 2024
You can use DuckDB WASM independently of Pyodide and can extend it with SQLite. Though it seems to be somewhat limited. I couldn't even check what version it has, since sqlite_version() was missing. Version in the repository [1] is 3.38.1, which is from quite a ways ago. At the moment DuckDB web shell can't load SQLite extension, since that hasn't been released for yesterday's 1.1.2. Earlier version does work using recently updated WASM edition. That can be extended with spatial including GDAL, vector search etc [2]. Making your own "SQL web shell" wasn't too hard, though docs weren't quite complete enough for me. [1] https://github.com/duckdb/sqlite_scanner/blob/main/src/sqlit... [2] https://github.com/duckdb/duckdb-wasm/releases/tag/v1.29.0
In Sqlite3 WebAssembly
tobilg·May 27, 2024
I witness the overengineering regarding "big" data tools and pipelines since many years... For a lot of use cases, data warehouses and data lakes are only in the gigabytes or single-digit terabytes range, thus their architecture could be much more simplified, e.g. running DuckDB on a decent EC2 instance. In my experience, doing this will yield the query results faster than some other systems even starting the query execution (yes, I'm looking at you Athena)... I even think that a lot of queries can be run from a browser nowadays, that's why I created https://sql-workbench.com/ with the help of DuckDB WASM (https://github.com/duckdb/duckdb-wasm) and perspective.js (https://github.com/finos/perspective).
In Big data is dead (2023)
prasoonds·Mar 15, 2024Submission

Show HN: Open-source, browser-local data exploration using DuckDB-WASM and PRQL

Hey HN! We’ve built Pretzel, an open-source data exploration and visualization tool that runs fully in the browser and can handle large files (200 MB CSV on my 8gb MacBook air is snappy). It’s also reactive - so if, for example, you change a filter, all the data transform blocks after it re-evaluate automatically. You can try it here: https://pretzelai.github.io/ (static hosted webpage) or see a demo video here: https://www.youtube.com/watch?v=73wNEun_L7w You can play with the demo CSV that’s pre-loaded (GitHub data of text-editor adjacent projects) or upload your own CSV/XLSX file. The tool runs fully in-browser—you can disconnect from the internet once the website loads—so feel free to use sensitive data if you like. Here’s how it works: You upload a CSV file and then, explore your data as a series of successive data transforms and plot
Read the rest
s. For example, you might: (1) Remove some columns; (2) Apply some filters (remove nulls, remove outliers, restrict time range etc); (3) Do a pivot (i.e, a group-by but fancier); (4) Plot a chart; (5) Download the chart and the the transformed data. See screenshot: https://imgur.com/a/qO4yURI In the UI, each transform step appears as a “Block”. You can always see the result of the full transform in a table on the right. The transform blocks are editable - for instance in the example above, you can go to step 2, change some filters and the reactivity will take care of re-computing all the cells that follow, including the charts. We wanted Pretzel to run locally in the browser and be extremely performant on large files. So, we parse CSVs with the fastest CSV parser (uDSV: https://github.com/leeoniya/uDSV) and use DuckDB-Wasm (https://github.com/duckdb/duckdb-wasm) to do all the heavy lifting of processing the data. We also wanted to allow for chained data transformations where each new block operates on the result of the previous block. For this, we’re using PRQL (https://prql-lang.org/) since it maps 1-1 with chained data transform blocks - each block maps to a chunk of PRQL which when combined, describes the full data transform chain. (PRQL doesn’t support DuckDB’s Pivot statement though so we had to make some CTE based hacks). There’s also an AI block: This is the only (optional) feature that requires an internet connection but we’re working on adding local model support via Ollama. For now, you can use your own OpenAI API key or use an AI server we provide (GPT4 proxy; it’s loaded with a few credits), specify a transform in plain english and get back the SQL for the transform which you can edit. Our roadmap includes allowing API calls to create new columns; support for an SQL block with nice autocomplete features, and a Python block (using Pyodide to run Python in the browser) on the results of the data transforms, much like a jupyter notebook. There’s two of us and we’ve only spent about a week coding this and fixing major bugs so there are still some bugs to iron out. We’d love for you to try this and to get your feedback!
227 points · Open discussion
westurner·Dec 28, 2023
Similar tools for running SQL queries over CSVs in Python: - pandas.dataframe.read_csv(), dask.dataframe.read_csv(), dask_cudf.read_csv(); .read_parquet() - DuckDB / DuckDB-WASM: https://news.ycombinator.com/item?id=36502107 https://github.com/duckdb/duckdb https://github.com/duckdb/duckdb-wasm - sqlite-utils > "Running queries directly against CSV or JSON" https://sqlite-utils.datasette.io/en/stable/cli.html#running... and optionally datasette / datasette-lite ?csv= for a GUI: https://github.com/simonw/datasette-lite#loading-csv-data
In [dead]

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