duckdb-wasm VS pbf

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).

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.

it's been about 3 years, but in Grafana at the time we were using something like ArrowJS + Arrow Flight + protobuf.js and then render the datasets into dashboards on Canvas, especially for streaming at ~20hz.

when i benchmarked the fastest lib to simply convert the protobuf decode (https://github.com/mapbox/pbf), it was 5x slower than native JSON parsing in browsers for dataframe-like structures (e.g. a few dozen 2k-long arrays of floats).

does it beat https://github.com/mapbox/pbf ?

at least in the frontend (without WASM), it depends.

i tested https://github.com/mapbox/pbf and while it was faster for deep/complex structs vs an unoptimized/repetative JSON blob, it was slower at shallow structs and flat arrays of stuff. if you spend a bit of time encode stuff as flat arrays to avoid mem alloc, JSON parsing wins by a lot since it goes through highly optimized C or assembly, while decoding protobuf in the JIT does not.

The runtime is taken from mapbox/pbf (with basic type definitions)