cyanide VS datasette

No, it stands for "Binary JSON".

BSON specification

I recommend looking at https://ubjson.org and https://bsonspec.org , this will answer most of your questions.

or https://bsonspec.org/

BJSON Binary JSON, with a Swift Library here and should be one for your other end

Starting this tutorial we specified that data in MongoDB is stored in collections. We also specified that in MongoDB we use syntax similar to JSON. That syntax is called "Binary JSON" or BSON. BSON is similar to JSON; but it's more like an encoded serialization of JSON. We can find useful information in the BSON website.

> I'm saying that when you decode an Avro document, the result that comes out (presuming you don't tell the Avro decoder anything special about custom types your runtime supports and how it should map them) is a JSON document.

Semantic point: it's not a "document".

There are tools which will decode Avro and output the data in JSON (typically using the JSON encoding of Avro: https://avro.apache.org/docs/current/spec.html#json_encoding), but the ADT that is created is by no means a JSON document. The ADT that is created has more complex semantics than JSON; JSON is not the canonical representation.

> By which I don't mean JSON-encoded text, but rather an in-memory ADT that has the exact set of types that exist in JSON, no more and no less.

Except Avro has data types that are not the exact set of types that exist in JSON. The first clue on this might be that the Avro spec includes mappings that list how primitive Avro types are mapped to JSON types.

> Or, to put that another way, Avro is a way to encode JSON-typed data, just as "JSON text", or https://bsonspec.org/, is a way to encode JSON-typed data

BSON, by design, was meant to be a more efficient way to encode JSON data, so yes, it is a way to encode JSON-typed data. Avro, however, was not defined as a way to encode JSON data. It was defined as a way to encode data (with a degree of specialization for the case of Hadoop sequence files, where you are generally storing a large number of small records in one file).

A simple counter example: Avro has a "float" type, which is a 32-bit IEEE 754 floating point number. Neither JSON nor BSON have that type.

Technically, JSON doesn't really have types, it has values, but even if you pretend that JavaScript's types are JSON's types, there's nothing "canonical" about JavaScript's types for Avro.

Yes, you can represent JSON data in Avro, and Avro in JSON, much as you can represent data in two different serialization formats. Avro's data model is very much defined independently of JSON's data model (as you'd expect).

For me, this exploration started when I was attempting to improve handling of Unicode surrogate pair values in the MongoDB Go driver's Extended JSON unmarshaler. The Extended JSON format is an extension to the standard JSON format that adds type information and allows deterministic conversion to and from BSON.

Simon Willison's github would be a great place to get started imo -

https://github.com/simonw/datasette

I'm a fan on simonw's datasette/dogsheep ecosystem https://datasette.io/

I use Anki the exact same way. After a lifetime of learning I have accepted that I will never read over anything I write for myself voluntarily - so my two options are:

1. Write an article so good I can publish it and look it over myself later on. I did this last year with https://andrew-quinn.me/fzf/, for example.

2. Create Anki cards out of the material. Use the builtin Card Browser or even https://datasette.io/ on the underlying SQLite database in a pinch to search for my notes any time I have to.

Were you aware of, or tempted by https://datasette.io/ for creating your solution?

Browsing HN, GitHub and the like we get to see a huge variety of software products and code bases.

I often see products and think - if this product X, got together with Y, it would be pretty cool - kind of like if they had a kid together.

Not too literally, but more on the conceptual level - my level of programming is low.

E.g. Just some....

- pocketable.io & datasette (+with some more charting) [https://pocketbase.io, https://datasette.io]

You might like the Datasette project: https://datasette.io/

I don't think they are desperate for contributions but it's a welcoming environment and a fun project to hack on. You'll learn a lot just from reading the source and the incredibly informative PRs. The creator is a really talented developer with a great blog which shows up on the HN front page often.

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

> uploads of boring raw excel data and receive a nice UI

https://datasette.io/