NestedText is a file format for holding structured data. It is similar in concept to JSON, except that NestedText is designed to make it easy for people to enter, edit, or view the data directly. It organizes the data into a nested collection of name-value pairs, lists, and strings. The syntax is intended to be very simple and intuitive for most people.
A unique feature of this file format is that it only supports one scalar type: strings. As such, quoting strings is unnecessary, and without quoting there is no need for escaping. While the decision to forego other types (integers, reals, Booleans, etc.) may seem counter productive, it leads to simpler data files and applications that are more robust.
NestedText is convenient for configuration files, data journals, address books, account information, and the like. Here is an example of a file that contains a few addresses:
# Contact information for our officers
Katheryn McDaniel:
position: president
address:
> 138 Almond Street
> Topeka, Kansas 20697
phone:
cell: 1-210-555-5297
home: 1-210-555-8470
# Katheryn prefers that we always call her on her cell phone.
email: [email protected]
additional roles:
- board member
Margaret Hodge:
position: vice president
address:
> 2586 Marigold Lane
> Topeka, Kansas 20682
phone: 1-470-555-0398
email: [email protected]
additional roles:
- new membership task force
- accounting task force
Configuration files are an attractive application for NestedText. NestedText configuration files tend to be simple, clean and unambiguous. Plus, they handle hierarchy much better than alternatives such as Ini and TOML.
One way to build tools to tackle difficult and complex tasks is to provide an application specific language. That can be a daunting challenge. However, in certain cases, such as specifying complex configurations, NestedText can help make the task much easier. NestedText conveys the structure of data leaving the end application to interpret the data itself. It can do so with a collection of small parsers that are tailored to the specific piece of data to which they are applied. This generally results in a simpler specification since each piece of data can be given in its natural format, which might otherwise confuse a shared parser. In this way, rather than building one large very general language and parser, a series of much smaller and simpler parsers are needed. These smaller parsers can be as simple as splitters or partitioners, value checkers, or converters for numbers in special forms (numbers with units, times or dates, GPS coordinates, etc.). Or they could be full-blown expression evaluators or mini-languages. Structured code provides a nice middle ground between data and code and its use is growing in popularity.
An example of structured code is provided by GitHub with its workflow specification files. They use YAML. Unfortunately, the syntax of the code snippets held in the various fields can be confused with YAML syntax, which leads to unnecessary errors, confusion, and complexity (see YAML issues). JSON suffers from similar problems. NestedText excels for these applications as it holds code snippets without any need for quoting or escaping. NestedText provides simple unambiguous rules for defining the structure of your data and when these rules are followed there is no way for any syntax or special characters in the values of your data to be confused with NestedText syntax. In fact, it is possible for NestedText to hold NestedText snippets without conflict.
Another example of structured code is provided by the files that contain the test cases used by Parametrize From File, a PyTest plugin. Parametrize From File simplifies the task of specifying test cases for PyTest by separating the test cases from the test code. Here it is being applied to test a command line program. Its response is checked using regular expressions. Each entry includes a shell command to run the program and a regular expression that must match the output for the test to pass:
-
cmd: emborg version
expected: emborg version: \d+\.\d+(\.\d+(\.?\w+\d+)?)? \(\d\d\d\d-\d\d-\d\d\)
expected type: regex
-
cmd: emborg --quiet files -D
expected:
> Archive: home-\d\d\d\d-\d\d-\d\dT\d\d:\d\d:\d\d
> \d\d\d\d-\d\d-\d\dT\d\d:\d\d:\d\d.\d\d\d\d\d\d configs/subdir/(file|)
> \d\d\d\d-\d\d-\d\dT\d\d:\d\d:\d\d.\d\d\d\d\d\d configs/subdir/(file|)
# Unfortunately, we cannot check the order as they were both
# created at the same time.
expected type: regex
-
cmd: emborg due --backup-days 1 --message "{elapsed} since last {action}"
expected: home: (\d+(\.\d)? (seconds|minutes)) since last backup\.
expected type: regex
Notice that the regular expressions are given clean, without any quoting or escaping.
Another attractive use-case for NestedText is command line programs whose
output is meant to be consumed by either people or other programs. This is
another growing trend. Many programs do this by supporting a --json
command-line flag that indicates the output should be computer readable rather
than human readable. But, with NestedText it is not necessary to make people
choose. Just output the result in NestedText and it can be read by people or
computers. For example, consider a program that reads your address list and
output particular fields on demand:
> address --email Katheryn McDaniel: [email protected] Margaret Hodge: [email protected]
This output could be fed directly into another program that accepts NestedText as input:
> address --email | mail-to-list
This package contains a Python reference implementation of NestedText and a test suite. Implementation in many languages is required for NestedText to catch on widely. If you like the format, please consider contributing additional implementations.
Also, please consider using NestedText for any applications you create.