semvec/pypi-packages huggingface dataset:
kson-jbrazeal 0.0.1
KSON is JSON with embedded SQL and networking
Requires Python: >=3.8

# KSON: JSON with SQL and Networking
> Of course it's a good idea: why would you ask?

KSON is a superset of JSON with the following features:

- Remote document references (so you can embed a JSON, KSON, or CSV file available at a public URL or file address)
- Embedded SQL: Write queries against other objects in your JSON file (including references and deeply nested objects)
  with the full power of SQLite and have the queries evaluate to JSON
- Use comments (`/* ... */`) and global named references (`"foo": "bar" as myRef`).
- Compiles to JSON: Run `kson file.kson` (see installation instructions below) and boom! you have JSON.

KSON combines the portability of the top data exchange formats (JSON, CSV) with the expressiveness of the leading data
querying language
(SQL), and the flexibility of dynamic embedded references.

## Installation
Run 

```bash
python3 -m pip install -g kson
```

This will create a global executable `kson` which you can run on
`.kson` files to produce `.json` files:

```bash
kson file.kson # Sends JSON to stdout
```

or 
```bash
kson file.kson > file.json # Pipe the output to a file
```

## Examples
You can find examples in the `examples/` directory. 

- [examples/gdp.kson](examples/gdp.kson): Demonstrates how you can query an external data source (in this case, CSV file on GitHub.)
- [examples/join-gdp-and-population.kson](examples/join-gdp-and-population.kson): Fetch data from _two_ data sources (GDP by country
  and population by country) and perform a join to see GDP per capita.


## FAQ

### How does this work?

It's pretty simple, actually: First we parse the KSON file via [recursive descent](https://en.wikipedia.org/wiki/Recursive_descent_parser). 
Where JSON has arrays and
dictionaries, we throw in a few extra types - refs, aliases, and SQL queries.

To compile the file, we traverse the tree, making network requests, building appropriately-named SQLite tables, and
performing SQL queries as we go, eventually collapsing the whole business to JSON.

Some constraints of this approach are that we make network requests in serial, and that you must define an alias before
any SQL queries which use it. On the other hand, we achieve a great deal of flexibility in indexing into deeply nested
remote documents: suppose a remote document has a structure like so:

```json
{
  "foo": {
    "bar": {
      "baz": [
        1,
        2,
        3,
        4
      ]
    }
  }
}

```

If we alias the document as `doc`, then our SQL queries can acess the contents of the array by querying
`select * from "$doc$foo$bar$baz"`.

### Is this a good idea, and where did you get it?

Haha, well, um. More appropriate adjectives might include "interesting", "tempting", and "risky".

For reasons which are best elided, I had to write an enormous number of JSON parsers in a short period of time, and then
got some additional ideas about the format. It's called "KSON" because k comes after j, get it? :-)