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A YAML/JSON/dictionary schema validator with terse schema definitions

Project description

YSchema is a terse and simple schema format with a reference validator implementation for YAML, JSON and other dictionary based data structures.

YSchema is quite minimal (in terms of lines of code) and is continuously tested against a set of of valid and invalid example data (see the examples directory). The code works nicely for its intended purpose, but may not be the most powerful or popular, even if it does what it was intended for very well. The main assumption (at least for now) is that all keys are strings without whitespace.

YSchema is written in Python (v. 3) and validates dictionaries containing basic datatypes like strings, ints, floats, lists and nested dictionaries. The schema is also a dictionary, so both the data and the schema can be written in Python, JSON, YAML, TOML, … formats. YSchema cannot validate all possible YAML / JSON data, in fact it cannot even validate its own schema files since those use significant white space in dictionary keys to describe expected data types and whether the data is required or not.

To install the YSchema Python library along with the yschema command line program run:

python3 -m pip install -U yschema

Consider using a virtual environment or adding --user to the pip command if you do not want to install into the system’s site-packages directory. PS: You may also want to look at older and more established schema and validators such as Yamale or json-schema in case those serve your needs better.

Introduction to YSchema

A simple example schema:

# There must be a key "name" that maps to a string
required name: str

# There can be an integer age, but it is not required
optional age: int

# The optional height must be above 0
optional height: float(min_val=0)

To validate this, first load the schema above into a dictionary, then load the data to validate into another dictionary, and finally run:

import yschema

# possibly loaded from json or yaml or just a plain old dict
schema = my_load_schema_function()
data_dict = {'name': 'Tormod'}

yschema.validate(data_dict, schema_dict)

If the function does not raise yschema.ValidationError then the data is valid according to the given schema. You can also use the yschema command to validate YAML files from the command line.

A more complicated example, showing constants and nested dictionaries:

# Example of a constant that can be used in validation functions
constant minimum_string_length: 5

# A sub-dictionary
type Whale:
    # The name is a string of a given minimum length
    required name: str(min_len=minimum_string_length)

    # The length must be between 0 and 500 meters
    optional length: float(min_val=0, max_val=500.0)

required whales: list(type=Whale)

The above schema validates data like this:

  - name: Unknown Whale
  - name: Enormous Whale
    length: 200.0

Note that when working with aliases and types the order of the keys in the dictionary starts to matter. Either use a Python 3.6 or later, or load your schema into an OrderedDict. YSchema contains a helper function for ordered safe loading of YAML files:

with open(schema_file_name, 'rt') as yml:
    schema = yschema.yaml_ordered_load(yml)

More advanced features

Built in types: the following types are implemented. Optional parameters are listed below each type:

  • Any

  • bool

  • str
    • min_len

    • max_len

    • equals - e.g. str(equals='Hi!') or matching one of several pissibilities with str(equals=('a', 'b', 'c'))

    • prefix

  • int
    • min_val

    • max_val

    • equals - e.g. int(equals=3) or int(equals=(2, 4, 6))

  • float
    • min_val

    • max_val

    • equals - e.g. float(equals=3.2) or float(equals=(2.1, 4.4))

  • list
    • min_len

    • max_len

    • type - e.g. list(type=int) or list(type=Whale)

  • one_of
    • types - e.g. one_of(types=(int, str)) or one_of(types=(str(prefix='Moby'), Whale))

  • any_of
    • types - see one_of (any_of matches if any of the types match, one_of requires exactly one match)

Alias: you can give an alias to avoid typing the same type definition over and over again:

alias Cat: one_of(types=(HouseCat, Tiger, Lynx))
alias Cats: list(type=Cat)

Glob: you can allow undefined keys by using a glob. The following will validate OK for all documents

optional *: Any

Inherit: a sub-schema introduced by type can contain a key inherit with the name of a previously defined sub-schema to avoid repeating definitions that are shared among several types:

type MeshBase:
    optional move: list(type=str)
    optional sort_order: list(type=int)
    optional mesh_file: str
type MeshDolfinFile:
    inherit: MeshBase
    required type: str(equals=('XML', 'XDMF', 'HDF5'))
    required mesh_file: str
    optional facet_region_file: str
type MeshMeshio:
    inherit: MeshBase
    required type: str(equals='meshio')
    required mesh_file: str
    optional meshio_type: str
required mesh: one_of(types=(MeshMeshio, MeshDolfinFile))


Version 1.0.2 - June 11. 2018

Improve error messages and add convinience function to safe-load YAML into an OrderedDict

Version 1.0.1 - June 7. 2018

Completed v 1.0 implementation goals. The YSchema language is powerful enough to express most of what I wanted for validating Ocellaris input files. The code base is decently tested (using the fantastic CircleCI service) and a command line tool is also included for validating YAML files from the shell.

There may not be a large number of additional releases if no more features are found to be necessary for the author’s uses. It is relatively easy to add new type validators from user code, but feel free to submit a pull request if you are finding YSchema useful and have implemented some general purpose validators. YSchema does not intend to compete with complex and more fully featured schema languages like json-schema.

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