seafowl VS django-rest-framework

In case you're interested in scale-to-zero database hosting, a few months ago I paired gcsfuse with Seafowl [0][1], an early stage open source database written in Rust. Was a lot of fun balancing tradeoffs that are usually not possible with classical databases e.g. Postgres. Thank you gcsfuse contributors.

[0] https://seafowl.io

> why someone would start something in a memory unsafe language these days

You might like what we (Splitgraph) are building with Seafowl [0], a new database which is written in Rust and based on Datafusion and delta-rs [1]. It's optimized for running at the edge and responding to queries via HTTP with cache-friendly semantics.

[0] https://seafowl.io

[1] https://www.splitgraph.com/blog/seafowl-delta-storage-layer

For example, here's the IPInfo dataset, and here's a some commodities data from Trase which is proxying to their live Postgres database, and powering their interactive dashboard. Also, here's the repository of Socrata metadata powering the newsfeed - we scrape it nightly and then push it to Seafowl, our new open-source database optimized for running cache-friendly queries "at the edge." The code for Open Data Monitor is on GitHub, if you're curious.

This is basically how we do authentication in the Splitgraph DDN [0], which is kind of like a multi-tenant serverless Postgres.

We implement the Postgres frontend with a forked version of PgBouncer, and we changed the authentication method such that when the user authenticates, we issue them a JWT which we store as a session variable. That session variable has the same security properties as a cookie in a web browser (the user can change/manipulate it, but if it's signed by us we can trust its claims).

That's the simple explanation that skips over the multi-tenant part. I don't want to derail from the thread - Neon is very cool, and we are actually experimenting with it right now, for storing the Seafowl [1] catalog when deploying to "scale to zero" services like Google Cloud Run or AWS Lambda, which don't have persistent storage.

[0] https://www.splitgraph.com/connect/query

[1] https://seafowl.io

This is really cool! I've always found IP data to be a compelling example of a data product, especially when talking about Splitgraph, a company of which I'm a co-founder (and btw - I also met my co-founder on HN!).

So, I exported the CSV files for country and asn data, and then uploaded them to Splitgraph. You can see some sample queries in the readme of the repository [0]. Since Splitgraph is built on Postgres, it's possible to use all the `inet` and `cidr` tools available from Postgres, so you can make range queries easily. One sample query also demonstrates a join between the two tables, resulting in the equivalent of your combined country_asn.csv.

Another idea: We have a newer project called Seafowl [1], which is an open-source analytical database optimized for running "at the edge," with cache-friendly semantics making it ideal for querying from Web applications. We don't have a self-hosted version of this yet, but perhaps the next thing to try would be loading this data into Seafowl and querying it "at the edge" - I've been thinking about ways that we could package Seafowl along as an OpenResty module, which could allow for true "at the edge" use cases like querying IP data in your reverse proxy. (Although the .mmdb format already solves this particular problem pretty efficiently and interoperably, although I'd be curious to measure the difference).

[0] https://www.splitgraph.com/miles/ipinfo-country-asn

[1] https://seafowl.io/

You can indeed run LiteFS by yourself, without Consul, as a sidecar / wrapper around your application. We do it in our project and have a Docker Compose example at [0]. In this case, you specify a specific known leader node. We haven't tried getting it running independently with Consul to do leader election / failover.

[0] https://github.com/splitgraph/seafowl/blob/main/examples/lit...

This is the vision of what we're building at Splitgraph. [0] You might be most interested in our recent project Seafowl [1] which is an open-source analytical database optimized for running "at the edge," with cache-friendly semantics making it ideal for querying from Web applications. It's built in Rust using DataFusion and incorporates many of the lessons we've learned building the Data Delivery Network [2] for Splitgraph.

[0] https://www.splitgraph.com

[1] https://seafowl.io

[2] https://www.splitgraph.com/connect

> why not just accept SQL and cut out all the unnecessary mapping?

You might be interested in what we're building: Seafowl, a database designed for running analytical SQL queries straight from the user's browser, with HTTP CDN-friendly caching [0]. It's a second iteration of the Splitgraph DDN [1] which we built on top of PostgreSQL (Seafowl is much faster for this use case, since it's based on Apache DataFusion + Parquet).

The tradeoff for allowing the client to run any SQL vs a limited API is that PostgREST-style queries have a fairly predictable and low overhead, but aren't as powerful as fully-fledged SQL with aggregations, joins, window functions and CTEs, which have their uses in interactive dashboards to reduce the amount of data that has to be processed on the client.

There's also ROAPI [2] which is a read-only SQL API that you can deploy in front of a database / other data source (though in case of using databases as a data source, it's only for tables that fit in memory).

[0] https://seafowl.io/

[1] https://www.splitgraph.com/connect

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

It's possible! Currently this is running GROUP BY queries using Socrata's query API on the original government data portal. We're adding the ability to import data from these sources into a columnar format in the future, either into Splitgraph itself or syncing the data out into Seafowl (https://seafowl.io/) which uses Parquet and is much faster.

Technically, the ability is already there (you can add a dataset to Splitgraph and select Socrata as a source if you know the dataset ID), but it's not as turnkey as landing on a dataset page and clicking a button. More to come!

Just wanted to give a shout out to Apache DataFusion[0] that IOx relies on a lot (and contributes to as well!).

It's a framework for writing query engines in Rust that takes care of a lot of heavy lifting around parsing SQL, type casting, constructing and transforming query plans and optimizing them. It's pluggable, making it easy to write custom data sources, optimizer rules, query nodes etc.

It's has very good single-node performance (there's even a way to compile it with SIMD support) and Ballista [1] extends that to build it into a distributed query engine.

Plenty of other projects use it besides IOx, including VegaFusion, ROAPI, Cube.js's preaggregation store. We're heavily using it to build Seafowl [2], an analytical database that's optimized for running SQL queries directly from the user's browser (caching, CDNs, low latency, some WASM support, all that fun stuff).

[0] https://github.com/apache/arrow-datafusion

[1] https://github.com/apache/arrow-ballista

[2] https://github.com/splitgraph/seafowl

- Django Rest Framework has no async support? https://github.com/encode/django-rest-framework/issues/7260

A URL shortener is a service that transforms long and complex URLs into short, easily memorable ones. This tutorial guides you through creating a URL shortener using Django REST framework and Postgres, deploying it on Koyeb.

Now, I'm converting this app into a Vue-based SPA (still powered by Django). I'm using the Django REST Framework to build the API that the SPA will interact with. (I'll be using token-based auth, via django-rest-knox. ETA: I'll actually be using djangorestframework-simplejwt.)

For the backend, we chose Python, Django Rest Framework. On the frontend, React, Redux, Saga, Sass. Let’s start with the backend, which was managed by Yegor. He writes about the server part of the project himself.

With regard to JSON API aspects of Django, have you used https://www.django-rest-framework.org/ ? I find it to be very satisfactory.

django-rest-framework and the Fetch API, or

Django REST Framework (DRF) Django REST Framework is a famous, powerful, and flexible toolkit for building Web APIs in Django. It provides a set of reusable components and tools to simplify API development, including serialization, authentication, permissions, and view sets.

I wish Django would take async more seriously. This comment gives a pretty good overview of the current situation (some points are more valid than others): https://github.com/encode/django-rest-framework/discussions/...

The Python ecosystem is strange. Where other dev communities will embrace new ways of doing things faster than most people can keep up — the Python community needs to be pulled kicking and screaming into the light once ever decade or so. Python 2 to 3, ~10 years.

async/await has been in Python since 2015, it feels like it's going to be another 5 years before we see people taking async seriously in the big packages. Same problem we had during the 2/3 transition. No library support, no developer support.

Thanks for helping Steve to complete his REST API, he is now partying🥳. You can get more information on how to use the Django REST framework to best way read the documentation. There are many alternatives to the Django REST framework, you can give them a try if you want,

[Django Rest Framework](https://www.django-rest-framework.org/) has the very nice idea of a [ViewSet](https://www.django-rest-framework.org/api-guide/viewsets/#modelviewset) that allows really minimal code to perform all the basic CRUD actions on a model.