exception-handling VS prql

Interesting article, thanks!

Notes on the issues mentioned there:

* The need for a manual shadow stack: This is fixed in WasmGC (in the same way it works in JS, as the link mentions).

* Lack of try-catch: This is fixed by the Wasm exception handling proposal, which has already shipped in browsers, https://github.com/WebAssembly/exception-handling/blob/main/...

* Null checks: Mostly fixed by WasmGC. The spec defines non-nullable local types, and VMs can use the techniques the article mentions to optimize them using signals (Wizard does, for example).

* Class initialization: This is a difficult problem, as the article says. J2Wasm and Binaryen are working to optimize it through static analysis at the toolchain level. Here is a recent PR I wrote that makes progress there: https://github.com/WebAssembly/binaryen/pull/6061

* The vtable overhead issue the article mentions may be a problem. I'm not aware of good measurements on it, through. There are some ideas on post-MVP solutions for method dispatch that might help, but nothing concrete yet.

* Checks for null and trapping: There has been discussion of variants on the GC instructions that throw instead of trap. Measurements, however, have not shown it to be a big problem atm, so it is low priority.

The author is right that stack walking, signals, and memory control are important areas that could help here.

Overall with WasmGC and exceptions we are in a pretty good place for Java as emitted by J2Wasm today: it is usually faster than J2CL which compiles Java to JavaScript. But there is definitely room for improvement.

It uses https://github.com/WebAssembly/exception-handling/blob/master/proposals/exception-handling/Exceptions.md which is actually supported by all major browsers already.

Yep, you're right. It's also more than just the DOM, it's web APIs in general, such as fetch, audio, webgl/webgpu, etc. WASM still needs GC, exceptions, and WASI to be able to fully interop with any host without any of the current limitations. This'll take a few years. I'm looking forward to the future in which I will be shipping WASM-only web apps to my users.

Its a part of the wasm plan to support gc https://github.com/WebAssembly/gc exceptions https://github.com/WebAssembly/exception-handling

As of Node.js 17, the v8 JavaScript engine has been updated to v9.5. The changes in this release are primarily aimed at expanding internationalization for dates and calendars as well as for the output of time zones. It also implements the WebAssembly Exception Handling proposal, designed to reduce overhead compared to current JavaScript-based workarounds.

Agreed, although at some point in a not very far feature most of those missing features will resolved. So in my mind is just a matter of time. The Wasm Community group is doing an awesome work on that :)

Here are a few examples of what needs move forward in Wasm:

* [1] Wasm Exceptions Handling: Right now Wasm is missing a way to handle exceptions natively (C++ programs can only compile to Wasm using the asyncify or longjmp/setjmp tricks via Js try/catch)

* [2] Wasm GC: Wasm Binary files are quite big (specially in interpreted languages). This is partially caused by the GC being included in the Binary itself. The GC proposal will solve this while also providing faster execution.

* [3] Wasm 64-bit Memory: currently Wasm can only operate with 32-bit data. In some contexts you may want you operate with more than 4GB of memory (for example, when operating over terabytes of data). The 64-bit memory proposal will solve that.

[1]: https://github.com/WebAssembly/exception-handling

> I completely attribute this to SQL being difficult or "backwards" to parse. I mean backwards in the way that in SQL you start with what you want first (the SELECT) rather than what you have and widdling it down.

> The turning point for me was to just accept SQL for what it is.

Or just write PRQL and compile it to SQL

https://github.com/PRQL/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 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!

> Looks like PRQL doesn't have a Go library so I guess they just really wanted something in Go?

There's some C bindings and the example in the README shows integration with Go:

https://github.com/PRQL/prql/tree/main/prqlc/bindings/prqlc-...

Can someone tell me why PRQL is better? I went here: https://github.com/PRQL/prql

It looks nice, but what's the strengths compared to SQL?

PRQL [1] is a compile-to-SQL relational querying language that puts FROM first.

[1] https://prql-lang.org

https://prql-lang.org/ might be an answer for this. As a cross-database pipelined language, it would allow RAG to be intermixed with the query, and the syntax may(?) be more reliable to generate