bacalhau VS duckdb-wasm

This is really interesting - we’ve tried really hard to solve some of these with Bacalhau[1] - a much simpler distributed compute platform. Would love your feedback!

[1] https://github.com/bacalhau-project/bacalhau

Disclosure: I confounded Bacalhau

It's an interesting question!

The problem is that the data is spread everywhere - no choice about that. So with that in mind, how do you query that data? Today, the idea is that you HAVE to put it into a central location. With tools like Bacalhau[1] and DuckDB [2], you no longer have to - a single query can be sharded amongst all your data - EFFECTIVELY giving you a lot of what you want from a data lake.

It's not a replacement, but if you can do a few of these items WITHOUT moving the data, you will be able to see really significant cost and time savings.

[1] https://github.com/bacalhau-project/bacalhau

[2] https://github.com/duckdb/duckdb

Very selfishly - distributed compute. Not decentralized, distributed.

Compute and data are being created and run everywhere, we need platforms that understand how to use it and get insights without (or before) moving it.

Our contribution: https://github.com/bacalhau-project/bacalhau (think Kubernetes but built for the distributed world).

Disclosure: I co-founded the Bacalhau Project

Thank you so much for the feedback. Yeah, we REALLY do want to figure out a better naming/reference scheme. Do you have anything you've seen you really like?

Disclosure: I work on Bacalhau[1]

https://github.com/bacalhau-project/bacalhau

i think duckdb-wasm is closer to 6MB over wire, but ~36MB once decompressed. (see net panel when loading https://shell.duckdb.org/)

the decompressed size should be okay since it's not the same as parsing and JITing 36MB of JS.

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 plots. 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!

Amazing, I was eagerly waiting for this one. Loading extensions in previous DuckDB-WASM releases didn't work seamlessly. Looks like now it's the case :D

ref: https://github.com/duckdb/duckdb-wasm/issues/1542#issuecomme...

Thanks!!

Btw, it's already happening:

Go to https://shell.duckdb.org, and type

I like Pandas, but you will love duckdb, which is solving this exact problem: https://duckdb.org/; https://shell.duckdb.org/

There's also https://shell.duckdb.org/ for playing around.

I know xls != csv, but would be cool to compare against https://github.com/duckdb/duckdb-wasm as well