source/projects/yamlschema

YAML Schema

A library that implements some amount of jsonschema validation against YAML document ASTs. Unlike other JSON-schema validation tools which give document path relative errors, this approach allows for line & file errors more appropriate to user-facing tools.

API Overview

yamlschema.LintRecord(level, node, schema, message)

LintRecords are what linting produces. Each LintRecord contains the YAML AST node which failed validation, the schema it failed to validate against, and some metadata.

level is a LintLevel which attempts to explain what "kind" of error this piece of lint represents. For instance LintLevel.MISSING encodes missing properties. LintLevel.MISSMATCH encodes type mismatches. LintLevel.UNEXPECTED encodes unexpected/disallowed keys and other errors.

yamlschema.YamlLinter(schema)

The linter itself is implemented as a class with a variety of instance methods; allowing the linter to be hacked by users much in the same way that JSON encodiers and decoders can be hacked.

The linter "interface" consists of __init__(schema: dict); being a loaded JSON schema as a dict tree and lint_document(schema, node) -> Iterable[LintRecord] which initiates the recursive linting.

The reference implementation of the linter recursively calls lint_document on every sub-structure in the document.

yamlschema.lint_file(schema, path, cls=YamlLinter)

As conveniences, yamlschema gives you a couple entrypoints that handle constructing the linter class, using yaml.compose() to get an AST and starting linting for you. lint_file and lint_buffer respectively allow the user to either bring a file path or a string of YAML.

Example

>>> from yamlschema import lint_buffer
>>> list(lint_buffer({"type": "integer"}, "---\n1.0"))
[
  LintRecord(
    level=<LintLevel.MISSMATCH: 2>,
    node=ScalarNode(tag='tag:yaml.org,2002:float', value='1.0'),
    schema={'type': 'integer'},
    message="Expected an integer, got a 'tag:yaml.org,2002:float'"
  )
]