ArangoDB VS prql

ArangoDB

... or working in an environment or on a problem for which functional patterns apply.

Suppose you are writing a "CRUD" app that writes to a relational database, how do you apply functional programming to that? The whole point of an application like that is that it makes side effects.

In some cases you can break those problems down into functional pieces. Consider Python drivers for a product like

https://www.arangodb.com/

One major problem is that you want drivers that work synchronously and asynchronously, the structure of the average api call is something like

   def query(parameters):

First, you need to choose a specific graph database platform to work with, such as Neo4j, OrientDB, JanusGraph, Arangodb or Amazon Neptune. Once you have selected a platform, you can then start working with graph data using the platform's query language.

Some databases like ArangoDB (https://www.arangodb.com/) allow you to use Javascript instead of SQL.

However, using a type-unsafe, turing-complete language introduces type unsafety and turing-complete problems to the query layer; the usual problems we know and love, such as infinite loops, runtime type errors, exceptions, and the like.

Personally, I'm looking forward to a WASM runtime for databases -- so we can run webassembly on the database. This COULD be carefully designed to be statically checked and, possibly, make it really hard to write runaway loops.

When it comes to graphdb's, my favorite is still ArangoDB, definitely worth checking out if you are looking for alternatives.

https://www.arangodb.com

Personally my favorite db for pet projects is

https://www.arangodb.com/

I think you hear very little about it because ADB users see it as a "secret weapon" to crush their competitors with. I've done large ontology work (MESH and other health ontologies) and IoT work (keep several years of sensor readings for sensors in my house) and workflow systems (select interesting HN articles or jobs I want to apply to) and it has never let me down. I haven't run a real instance serving customers in the cloud though.

For the last few years every eng manager I have worked with has been a fan of

https://www.postgresql.org/

In the early 2000s I thought it overpromised and underdelivered and called it CrashGreSlow but after MySQL got bought by Oracle the pgsql team has worked hard to improve it I think it is great today. It supports all kinds of advanced features such as stored procs, full-text search, JSON equivalent fields, etc.

Hi! I recently came across ArangoDb and used in some POCs, but I really want to know if someone here already used it in a Real World environment or even if chose to not use in a production environment. So... have you ever used ArangoDb? Why? Why not?

For mutual friends, we can build a social graph for every user. Each node in the graph will represent a user and a directional edge will represent followers and followees. After that, we can traverse the followers of a user to find and suggest a mutual friend. This would require a graph database such as Neo4j and ArangoDB.

ArangoDB

This a NOSQL database built for high availability and high scalability, a perfect fit for implementing persistence in microservices. ArangoDB is an open source native multi-model database that supports graph, document and key-value data models allowing users to freely combine all data models in a single query. Dive deeper into this database and its features here.

> 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