Patterns

Meta

This document describes various patterns for solving common problems, in ways that are not (yet) specified in any Frictionless Data specification. If we see increased adoption, or wide support, for any pattern, it is a prime candidate for formalising as part of a specification.

Private properties

Overview

Some software that implements the Frictionless Data specifications may need to store additional information on the various Frictionless Data descriptors.

For example, a data registry that provides metadata via datapackage.json may wish to set an internal version or identifier that is system-specific, and should not be considered as part of the user-generated metadata.

Properties to store such information should be considered "private", and by convention, the names should be prefixed by an underscore _.

Implementations

There are no known implementations at present.

Specification

On any Frictionless Data descriptor, data that is not generated by the author/contributors, but is generated by software/a system handling the data, SHOULD be considered as "private", and be prefixed by an underscore _.

To demonstrate, let's take the example of a data registry that implements datapackage.json for storing dataset metadata.

A user might upload a datapackage.json as follows:

{
  "name": "my-package",
  "resources": [
    {
      "name": "my-resource",
      "data": [ "my-resource.csv" ]
    }
  ]
}

The registry itself may have a platform-specific version system, and increment versions on each update of the data. To store this information on the datapackage itself, the platform could save this information in a "private" _platformVersion property as follows:

{
  "name": "my-package",
  "_platformVersion": 7
  "resources": [
    {
      "name": "my-resource",
      "data": [ "my-resource.csv" ]
    }
  ]
}

Usage of "private" properties ensures a clear distinction between data stored on the descriptor that is user (author/contributor) defined, and any additional data that may be stored by a 3rd party.

Caching of resources

Overview

All Frictionless Data specifications allow for referencing resources via http or a local filesystem.

In the case of remote resources via http, there is always the possibility that the remote server will be unavailable, or, that the resource itself will be temporarily or permanently removed.

Applications that are concerned with the persistent storage of data described in Frictionless Data specifications can use a _cache property that mirrors the functionality and usage of the data property, and refers to a storage location for the data that the application can fall back to if the canonical resource is unavailable.

Implementations

There are no known implementations of this pattern at present.

Specification

Implementations MAY handle a _cache property on any descriptor that supports a data property. In the case that the data referenced in data is unavailable, _cache should be used as a fallback to access the data. The handling of the data stored at _cache is beyond the scope of the specification. Implementations might store a copy of the resources in data at ingestion time, update at regular intervals, or any other method to keep an up-to-date, persistent copy.

Some examples of the _cache property.

{
  "name": "my-package",
  "resources": [
    {
      "name": "my-resource",
      "data": [ "http://example.com/data/csv/my-resource.csv" ],
      "_cache": "my-resource.csv"
    },
    {
      "name": "my-resource",
      "data": [ "http://example.com/data/csv/my-resource.csv" ],
      "_cache": "http://data.registry.com/user/files/my-resource.csv"
    },
    {
      "name": "my-resource",
      "data": [
        "http://example.com/data/csv/my-resource.csv",
        "http://somewhere-else.com/data/csv/resource2.csv"
      ],
      "_cache": [
        "my-resource.csv",
        "resource2.csv"
      ]
    },
    {
      "name": "my-resource",
      "data": [ "http://example.com/data/csv/my-resource.csv" ],
      "_cache": "my-resource.csv"
    }
  ]
}

Language support

Overview

Language support is a different concern to translation support. Language support deals with declaring the default language of a descriptor and the data it contains in the resources array. Language support makes no claim about the presence of translations when one or more languages are supported in a descriptor or in data. Via the introduction of a languages array to any descriptor, we can declare the default language, and any other languages that SHOULD be found in the descriptor and the data.

Implementations

There are no known implementations of this pattern at present.

Specification

Any Frictionless Data descriptor can declare the language configuration of its metadata and data with the languages array.

languages MUST be an array, and the first item in the array is the default (non-translated) language.

If no languages array is present, the default language is English (en), and therefore is equivalent to:

{
  "name": "my-package",
  "languages": ["en"]
}

The presence of a languages array does not ensure that the metadata or the data has translations for all supported languages.

The descriptor and data sources MUST be in the default language. The descriptor and data sources MAY have translations for the other languages in the array, using the same language code. IF a translation is not present, implementing code MUST fallback to the default language string.

Example usage of languages, implemented in the metadata of a descriptor:

{
  "name": "sun-package",
  "languages": ["es", "en"],
  "title": "Sol"
}

# which is equivalent to
{
  "name": "sun-package",
  "languages": ["es", "en"],
  "title": {
    "": "Sol",
    "en": "Sun"
  }
}

Example usage of languages implemented in the data described by a resource:

# resource descriptor
{
  "name": "solar-system",
  "data": [ "solar-system.csv" ]
  "fields": [
    ...
  ],
  "languages": ["es", "en", "he", "fr", "ar"]
}

# data source
# some languages have translations, some do not
# assumes a certain translation pattern, see the related section
id,name,[email protected],[email protected],[email protected]
1,Sol,Soleil,שמש,Sun
2,Luna,Lune,ירח,Moon

Translation support

Overview

Following on from a general pattern for language support, and the explicit support of metadata translations in Frictionless Data descriptors, it would be desirable to support translations in source data.

We currently have two patterns for this in discussion. Both patterns arise from real-world implementations that are not specifically tied to Frictionless Data.

One pattern suggests inline translations with the source data, reserving the @ symbol in the naming of fields to denote translations.

The other describes a pattern for storing additional translation sources, co-located with the "source" file described in a descriptor data.

Implementations

There are no known implementations of this pattern in the Frictionless Data core libraries at present.

Specification

Inline

Uses a column naming convention for accessing translations.

Tabular resource descriptors support translations using {field_name}@{lang_code} syntax for translated field names. lang_code MUST be present in the languages array that applies to the resource.

Any field with the @ symbol MUST be a translation field for another field of data, and MUST be parsable according to the {field_name}@{lang_code} pattern.

If a translation field is found in the data that does not have a corresponding field (e.g.: [email protected] but no title), then the translation field SHOULD be ignored.

If a translation field is found in the data that uses a lang_code not declared in the applied languages array, then the translation field SHOULD be ignored.

Translation fields MUST NOT be described in a schema fields array.

Translation fields MUST match the type, format and constraints of the field they translate, with a single exception: Translation fields are never required, and therefore constraints.required is always false for a translation field.

Co-located translation sources

Uses a file storage convention for accessing translations.

To be contributed by @jheeffer

  • Has to handle local and remote resources
  • Has to be explicit about the translation key/value pattern in the translation files
# local
data/file1.csv
data/lang/file1-en.csv
data/lang/file1-es.csv

# remote
http://example/com/data/file2.csv
http://example/com/data/lang/file2-en.csv
http://example/com/data/lang/file2-es.csv

Table Schema: Foreign Keys to Data Packages

Overview

A foreign key is a reference where values in a field (or fields) in a Tabular Data Resource link to values in a field (or fields) in a Tabular Data Resource in the same Tabular Data Package.

This pattern allows users to link values in a field (or fields) in a Tabular Data Resource to values in a field (or fields) in a Tabular Data Resource in a different Tabular Data Package.

Specification

The foreignKeys array MAY have a property datapackage. This property MUST be, either:

  • a string that is a fully qualified HTTP address to a Data Package datapackage.json file
  • a data package name that can be resolved by a canonical data package registry

If the referenced data package has an id that is a fully qualified HTTP address, it SHOULD be used as the datapackage value.

For example:

"foreignKeys": [{
    "fields": ["code"],
    "reference": {
      "datapackage": "https://raw.githubusercontent.com/frictionlessdata/example-data-packages/master/donation-codes/datapackage.json",
      "resource": "donation-codes",
      "fields": ["donation code"]
    }
  }]

Data Package Version

Specification

The Data Package version format follows the Semantic Versioning specification format: MAJOR.MINOR.PATCH

The version numbers, and the way they change, convey meaning about how the data package has been modified from one version to the next.

Given a Data Package version number MAJOR.MINOR.PATCH, increment the:

MAJOR version when you make incompatible changes, e.g.

  • Change the data package, resource or field name or identifier
  • Add, remove or re-order fields
  • Change a field type or format
  • Change a field constraint to be more restrictive
  • Combine, split, delete or change the meaning of data that is referenced by another data resource

MINOR version when you add data or change metadata in a backwards-compatible manner, e.g.

  • Add a new data resource to a data package
  • Add new data to an existing data resource
  • Change a field constraint to be less restrictive
  • Update a reference to another data resource
  • Change data to reflect changes in referenced data

PATCH version when you make backwards-compatible fixes, e.g.

  • Correct errors in existing data
  • Change descriptive metadata properties

Scenarios

  • You are developing your data though public consultation. Start your initial data release at 0.1.0
  • You release your data for the first time. Use version 1.0.0
  • You append last months data to an existing release. Increment the MINOR version number
  • You append a column to the data. Increment the MAJOR version number
  • You relocate the data to a new URL or path. No change in the version number
  • You change a title, description, or other descriptive metadata. Increment the PATCH version
  • You fix a data entry error by modifying a value. Increment the PATCH version
  • You split a row of data in a foreign key reference table. Increment the MAJOR version number
  • You update the data and schema to refer to a new version of a foreign key reference table. Increment the MINOR version number

Data Dependencies

Consider a situation where data packages are part of a tool chain that, say, loads all of the data into an SQL db. You can then imagine a situation where one requires package A which requires package B + C.

In this case you want to specify that A depends on B and C -- and that "installing" A should install B and C. This is the purpose of dataDependencies property.

Specification

dataDependencies is an object. It follows same format as CommonJS Packages spec v1.1. Each dependency defines the lowest compatible MAJOR[.MINOR[.PATCH]] dependency versions (only one per MAJOR version) with which the package has been tested and is assured to work. The version may be a simple version string (see the version property for acceptable forms), or it may be an object group of dependencies which define a set of options, any one of which satisfies the dependency. The ordering of the group is significant and earlier entries have higher priority. Example:

"dataDependencies": {
   "country-codes": "",
   "unemployment": "2.1",
   "geo-boundaries": {
     "acmecorp-geo-boundaries": ["1.0", "2.0"],
     "othercorp-geo-boundaries": "0.9.8",
   },
}

Implementations

None known.

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