Schema Annotations for Linked Avro Data (SALAD)
Project description
Schema Salad
Salad is a schema language for describing JSON or YAML structured linked data documents. Salad schema describes rules for preprocessing, structural validation, and hyperlink checking for documents described by a Salad schema. Salad supports rich data modeling with inheritance, template specialization, object identifiers, object references, documentation generation, code generation, and transformation to RDF. Salad provides a bridge between document and record oriented data modeling and the Semantic Web.
The Schema Salad library is Python 3.8+ only.
Installation
pip3 install schema_salad
If you intend to use the schema-salad-tool –codegen=python feature, please include the [pycodegen] extra:
pip3 install schema_salad[pycodegen]
To install from source:
git clone https://github.com/common-workflow-language/schema_salad cd schema_salad pip3 install . # or pip3 install .[pycodegen] if needed
Commands
Schema salad can be used as a command line tool or imported as a Python module:
$ schema-salad-tool
usage: schema-salad-tool [-h] [--rdf-serializer RDF_SERIALIZER] [--skip-schemas]
[--strict-foreign-properties] [--print-jsonld-context]
[--print-rdfs] [--print-avro] [--print-rdf] [--print-pre]
[--print-index] [--print-metadata] [--print-inheritance-dot]
[--print-fieldrefs-dot] [--codegen language] [--codegen-target CODEGEN_TARGET]
[--codegen-examples directory] [--codegen-package dotted.package]
[--codegen-copyright copyright_string] [--print-oneline]
[--print-doc] [--strict | --non-strict]
[--verbose | --quiet | --debug] [--only ONLY] [--redirect REDIRECT]
[--brand BRAND] [--brandlink BRANDLINK] [--brandstyle BRANDSTYLE]
[--brandinverse] [--primtype PRIMTYPE] [--version]
[schema] [document]
$ python
>>> import schema_salad
Validate a schema:
$ schema-salad-tool myschema.yml
Validate a document using a schema:
$ schema-salad-tool myschema.yml mydocument.yml
Generate HTML documentation:
$ schema-salad-tool --print-doc myschema.yml > myschema.html $ # or $ schema-salad-doc myschema.yml > myschema.html
Get JSON-LD context:
$ schema-salad-tool --print-jsonld-context myschema.yml mydocument.yml
Convert a document to JSON-LD:
$ schema-salad-tool --print-pre myschema.yml mydocument.yml > mydocument.jsonld
Generate Python classes for loading/generating documents described by the schema (Requires the [pycodegen] extra):
$ schema-salad-tool --codegen=python myschema.yml > myschema.py
Display inheritance relationship between classes as a graphviz ‘dot’ file and render as SVG:
$ schema-salad-tool --print-inheritance-dot myschema.yml | dot -Tsvg > myschema.svg
Codegen Examples
The examples in the tables below are helpful to see how to use the output of schema-salad-tool –codegen in different languages for loading and/or creating/editing/saving objects.
First set of examples is using the CWL v1.2 schema:
Language |
Repository |
Serialization Example | Deserialization Example |
|
|---|---|---|---|
Python |
|||
Java |
(Not yet implemented) |
||
TypeScript |
|||
.Net |
|||
C++ |
|||
D |
Second set of examples is for the Galaxy Workflow Format 2 schema:
Language |
Path |
|---|---|
Python |
https://github.com/galaxyproject/gxformat2/blob/master/gxformat2/schema/v19_09.py |
Java |
|
TypeScript |
https://github.com/galaxyproject/gxformat2/tree/master/typescript |
Quick Start
Let’s say you have a ‘basket’ record that can contain items measured either by weight or by count. Here’s an example:
basket:
- product: bananas
price: 0.39
per: pound
weight: 1
- product: cucumbers
price: 0.79
per: item
count: 3
We want to validate that all the expected fields are present, the measurement is known, and that “count” cannot be a fractional value. Here is an example schema to do that:
- name: Product
doc: |
The base type for a product. This is an abstract type, so it
can't be used directly, but can be used to define other types.
type: record
abstract: true
fields:
product: string
price: float
- name: ByWeight
doc: |
A product, sold by weight. Products may be sold by pound or by
kilogram. Weights may be fractional.
type: record
extends: Product
fields:
per:
type:
type: enum
symbols:
- pound
- kilogram
jsonldPredicate: '#per'
weight: float
- name: ByCount
doc: |
A product, sold by count. The count must be a integer value.
type: record
extends: Product
fields:
per:
type:
type: enum
symbols:
- item
jsonldPredicate: '#per'
count: int
- name: Basket
doc: |
A basket of products. The 'documentRoot' field indicates it is a
valid starting point for a document. The 'basket' field will
validate subtypes of 'Product' (ByWeight and ByCount).
type: record
documentRoot: true
fields:
basket:
type:
type: array
items: Product
You can check the schema and document in schema_salad/tests/basket_schema.yml and schema_salad/tests/basket.yml:
$ schema-salad-tool basket_schema.yml basket.yml Document `basket.yml` is valid
Documentation
See the specification and the metaschema (salad schema for itself). For an example application of Schema Salad see the Common Workflow Language.
Rationale
The JSON data model is an popular way to represent structured data. It is attractive because of it’s relative simplicity and is a natural fit with the standard types of many programming languages. However, this simplicity comes at the cost that basic JSON lacks expressive features useful for working with complex data structures and document formats, such as schemas, object references, and namespaces.
JSON-LD is a W3C standard providing a way to describe how to interpret a JSON document as Linked Data by means of a “context”. JSON-LD provides a powerful solution for representing object references and namespaces in JSON based on standard web URIs, but is not itself a schema language. Without a schema providing a well defined structure, it is difficult to process an arbitrary JSON-LD document as idiomatic JSON because there are many ways to express the same data that are logically equivalent but structurally distinct.
Several schema languages exist for describing and validating JSON data, such as JSON Schema and Apache Avro data serialization system, however none understand linked data. As a result, to fully take advantage of JSON-LD to build the next generation of linked data applications, one must maintain separate JSON schema, JSON-LD context, RDF schema, and human documentation, despite significant overlap of content and obvious need for these documents to stay synchronized.
Schema Salad is designed to address this gap. It provides a schema language and processing rules for describing structured JSON content permitting URI resolution and strict document validation. The schema language supports linked data through annotations that describe the linked data interpretation of the content, enables generation of JSON-LD context and RDF schema, and production of RDF triples by applying the JSON-LD context. The schema language also provides for robust support of inline documentation.
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