prql VS honeysql

> 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

I recall using korma way back I and I don’t recall it being terrible but I would say https://github.com/seancorfield/honeysql has very much superseded it by this point… (but I can see how that might not be obviously clear if one is to look at superficial metrics like GitHub stars for example…)

Joins can certainly work in a data format like YAML. For an example, see Honey SQL from the Clojure community [0] (though without something to contrast strings like Clojure's keywords, you miss out on the automatic parameterization).

You mentioned moving JOINs around, so I'll mention that if represented as structured data, you can move any of the top level components around, so you could more closely follow the "true order of SQL" [1]. For example, I would love to be able to put FROM before SELECT in all or almost all cases. There's also being able to share and add to something like a complicated WHERE clause, where essentially all programming languages have built-in facilities for robustly manipulating ordered and associative data compared to string manipulation, which is not well-suited for the task.

Now don't get me wrong, I don't particularly care for YAML (though it doesn't bother me that much), but as someone who's done their fair share of programmatic SQL creation and manipulation in strings, not having a native way to represent SQL as data is a mistake in my opinion.

0: https://github.com/seancorfield/honeysql#big-complicated-exa...

In Clojure-land, we are also using HoneySQL [1] which has similar characteristics. You are still working within SQL semantics so it's a bit more complicated, but we are doing great complicated things with just maps, no API necessary.

[1] https://github.com/seancorfield/honeysql

using honeysql we can make structured queries as well

As someone else already pointed out, next.jdbc is good for database connectivity (for Postgres and beyond). For composing the queries themselves, I strongly recommend Honey SQL. It lets you represent queries themselves as normal Clojure data structures, just vectors and maps.

Perhaps you're looking for a way of arranging SQL as an AST represented by data structures (or objects) that can be fed to a compiler. HoneySQL[0] is one such implementation of this idea and it makes your general transformation trivial for Clojure programs. You don't need to mess around with string concatenation because you have a predictable and extensible compiler for data structures (which are themselves easily composable/transformable/storable with Clojure) that you can trust to do the right thing. If you're using some weird database or need an esoteric syntax, extending the compiler to your clause is easy to do[1].

[0] https://github.com/seancorfield/honeysql

[1] https://github.com/seancorfield/honeysql#extensibility

https://github.com/seancorfield/honeysql (write SQL without having to write SQL)

I've used it from Clojure, via HoneySQL, so no ORM, no danger of SQL injection. It was really wonderful!

https://github.com/seancorfield/honeysql

I used it to quickly iterate on the development of migration SQL scripts for a MySQL DB, which was running in production on RDS.

I might have switched to H2 DB later, because that was more compatible with MariaDB, but I could use the same Clojure code, representing the SQL queries, because HoneySQL can emit different syntaxes.