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Schema Annotations for Linked Avro Data (SALAD)

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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 3.6+ 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

These 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, using the CWL v1.2 schema as an example.

Language

Repository

Serialization Example | Deserialization Example

Python

https://github.com/common-workflow-language/cwl-utils/

create_cwl_from_objects.py

load_document()

Java

https://github.com/common-workflow-language/cwljava/

(Not yet implemented)

PackedWorkflowClassTest.java

TypeScript

https://github.com/common-workflow-lab/cwl-ts-auto

Creating, editing, and saving CWL docs with TypeScript

Loading CWL documents with TypeScript

.Net

https://github.com/common-workflow-lab/CWLDotNet

Creating, editing, and saving CWL docs with .Net

Loading CWL documents with .Net

C++

https://github.com/common-workflow-lab/cwl-cpp-auto

cwl_output_example.cpp

(Not yet implemented)

D

https://github.com/common-workflow-lab/cwl-d-auto

How to use

How to use

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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SHA256 3fff6797a7900c150cb0dae1800ad09f501b5acbbdac12de19fcd8342282b698
MD5 710c3775dfd6062f92e0e70a5840852a
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SHA256 c9d2fb0fc04011f30f38fa3b45e52fafc2ea3aefca44b3d00b34c357f24a048f
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SHA256 dd57618b40425cbd737251412aa466a37d2a7badfd2df1d2bc392efd882cd629
MD5 5a5caa56b29dbf47d9b7423d3ea5e7af
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SHA256 9bdc168823caa4d9b386c7360c26f5ba3e4ca536a3cc4a1326e838d24ecbcd96
MD5 c4bbec86096dd9ce6938ad46581f7be0
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SHA256 1d3947868576c698e8cd20d78e300e34359484d94c3771c986d512b1e8366295
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SHA256 78ad98187183dc3832a6a374b6b9dd5dca65b21f0f0b755555c7be32cfa86dfe
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SHA256 7f8d9180d5364e072f93176fee1b410b9d7cc36110c7db6d14851f7a829c9ed9
MD5 f92eb10676fbaa46d5487db1137239ee
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SHA256 4d22c5dd4f38666220e35e02e239e1ad19404cbcd1e41dc2b5a2c3cbf06cc133
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SHA256 246061392d81a6e5f0aca78cd3a68b649f0779e2e3315facbfb7ff96dac865f0
MD5 9f0f849f7f2b87cbed5c647a4ccb250f
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SHA256 9f8e64caf0eaab8e164387ccc7570f5230cec83bcb9f8b2eab92b1bced51294e
MD5 957a0c1f6431033dacfafa3786242e4a
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SHA256 544a920bcebca71a566123ad9aecd64264631941d3d14c9167f1925eb6e52545
MD5 619550d37c2182abf9efceebe26e5e5d
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SHA256 8f36377abf066d6a929e4385850fd10a06879c0eb77733885594c70c2878461d
MD5 5b0834f89ca2d6001bad1f320e552210
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SHA256 905e73d481eda9a32dce31468a88b319bab2cb2c3c45dd67a2d2f5092785b031
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SHA256 137812eb775c0d44d9b51c9ce6173c3fb00d3121c3f81b263c9d5c2b3b6d2d26
MD5 4b41a8c5b4d652bf1cf56a7615c4755d
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SHA256 ba4e1e3e755459c974943351ab3e73e858f8260296138c3ee1dd830601fb4eab
MD5 c6f9b0f54117491d19de41557b60083a
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SHA256 b326a3358aecf872621b4f5b5bb4d0b20a2e06533835d1f050883397392b8624
MD5 14db49881877c1358fdb425243913135
BLAKE2b-256 0e4495c345e729200f9bbf3e89cfadf620057d2fa955edbfd1718248dd0dd11c

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SHA256 531d8f35dca37662f9bfd468cb19dddaff6dfc4b0162c68008a237224bad674a
MD5 8b864e9197c235b5e07e478561988921
BLAKE2b-256 17a91da2f79c3ab91ce8ae32a464cadf293ce787abaac258e270844b8a629ff7

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SHA256 24270ffb5a4d069c7fd3de7ab6204b93e92d2f798909089d0c35745c0dd45eef
MD5 b8c06246fa4cf2d782bc44afb0538a07
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SHA256 d54110c5c33df2e463e97fc9d32019a66dfb0d9e5c53c7bb2b03145565536849
MD5 e8563437c7aa510c254bc961123ec088
BLAKE2b-256 b67ec03a4a64352e8750830d8cafe07f042b9d388f1869d4e18f5aa66c4761af

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SHA256 29dd263716a40096eaa9e5e4913116ef05f6f63a8cb71baf525b8c1adf0ae02b
MD5 b77e5bb2f67c18e265c94e03a293a24d
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SHA256 caf7cc73b6ec8fae6fa7d2a89607a0caca76fbb45197c83c804a009d01b6fbc1
MD5 503feb2a9987ab02c85f097411cb2e5d
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SHA256 cd65df444a66cec120e5c08f8b6dbe61ff394ee3f9376a51b4c6dfa4ecb84413
MD5 845ff2b141d65402c405a0c58bab4cd2
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SHA256 aba36c20c674f4bb7365df5c6c6535995a9a4b410498fc5326e86d4239771318
MD5 7f5e7744ac3a0b16f84554ba69d0e1f1
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SHA256 62898b4c46c36508f4204525469ca7459d7e0204ddff72a717ade988e16a0e15
MD5 cb5486c75f4e51daffe2467e12c7bfc1
BLAKE2b-256 164c78734d06ec255bc71c81bbdb1c78e02df2afb5887ea142771692030ecec6

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SHA256 cb1c87beb97ee0463cc999442822b0ebcc1f475a2dc8d47bc68330963957a73f
MD5 ba69cfff53f4d4a3944a878bc87025a5
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SHA256 f3ed9fd4e44a67ebbd4e1e87d17a59a4a367c241fbce3bfcd232c377f8177eb9
MD5 db45c40f1c42a9a9879a52cbff3190ad
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SHA256 8ec21bb577f6888712354abd54abf6c66150b412234254e0ba9e29f9ace95cff
MD5 1d917dcf855e7d5960bfce156f6d3624
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SHA256 2a88585bf5ecb53d3caa0278bb896701168bed828eb896e6188035b1a9eb9bf9
MD5 4c98c86c6bb651793b8d006f5f72162b
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SHA256 30ac51e273361fdb7362e3a65c25aacfb6feb8760d3201bdff68d61015d413b0
MD5 f598b2a452461f31d55e818d4f08cfe3
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SHA256 ed9a027b051e4b3f469decedb1c52f42561dabe78cdd23da037a23effda73d85
MD5 1784214000cbaa1cde21ee4faf8d8a5a
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Algorithm Hash digest
SHA256 a16e7e6eab15026c0b7079a927cd6c8f8c2694b74c7066be960fcfb28c8d3a3f
MD5 6ba43ad82494c0395b81ffbd3fe01bad
BLAKE2b-256 4673c0967e36a29cb825ac532567918fe660c9a96f6132e3569b8a2c74266313

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