This RFC defines a simple schema for tabular data. The schema is designed to be expressible in JSON.

Version1.0-pre9
Last Updated3 March 2015
Created12 November 2012

Language

The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”, “SHOULD”, “SHOULD NOT”, “RECOMMENDED”, “MAY”, and “OPTIONAL” in this document are to be interpreted as described in RFC 2119.

Changelog

  • 1.0.0-pre9: make date formats stricter for default issue. Define value of fmt:PATTERN for dates issue
  • 1.0-pre8: Rename contraints.oneOf to contraints.enum issue
  • 1.0-pre7: Add contraints.oneOf issue
  • 1.0-pre6: clarify types and formats issue
  • 1.0-pre5: add type validation issue
  • 1.0-pre4: add foreign key support - see this issue
  • 1.0-pre3.2: add primary key support (see this issue)
  • 1.0-pre3.1: breaking changes.
    • label (breaking) changed to title - see Closer alignment with JSON Schema
    • id changed to name (with slight alteration in semantics - i.e. SHOULD be unique but no longer MUST be unique)

Table of Contents

Concepts

A Table consists of a set of rows. Each row has a set of fields (columns). We usually expect that each Row has the same set of fields and thus we can talk about the fields for the table as a whole.

In cases of tables in spreadsheets or CSV files we often interpret the first row as a header row giving the names of the fields. By contrast, in other situations, e.g. tables in SQL databases the fields (columns) are explicitly designated.

To illustrate here’s a classic spreadsheet table:

field     field
  |         |
  |         |
  V         V

 A     |    B    |    C    |    D      <--- Row
 ------------------------------------
 valA  |   valB  |  valC   |   valD    <--- Row
 ...

In JSON a table would be:

[
  { "A": value, "B": value, ... },
  { "A": value, "B": value, ... },
  ...
]

Specification

A JSON Table Schema consists of:

  • a required list of field descriptors
  • optionally, a primary key description
  • optionally, a foreign _key description

A schema is described using JSON. This might exist as a standalone document or may be embedded within another JSON structure, e.g. as part of a data package description.

Schema

A schema has the following structure:

{
  # fields is an ordered list of field descriptors
  # one for each field (column) in the table
  "fields": [
    # a field-descriptor
    {
      "name": "name of field (e.g. column name)",
      "title": "A nicer human readable label or title for the field",
      "type": "A string specifying the type",
      "format": "A string specifying a format",
      "description": "A description for the field"
      ...
    },
    ... more field descriptors
  ],
  # (optional) specification of the primary key
  "primaryKey": ...
  # (optional) specification of the foreign keys
  "foreignKeys": ...

}

That is, a JSON Table Schema is:

  • a Hash which MUST contain a key fields
  • fields MUST be an array where each entry in the array is a field descriptor. (Structure and usage described below)
  • the Hash MAY contain a property primaryKey (structure and usage specified below)
  • the Hash MAY contain a property foreignKeys (structure and usage specified below)
  • the Hash MAY contain any number of other properties (not defined in this spec)

Field Descriptors

A field descriptor is a simple JSON hash that describes a single field. The descriptor provides additional human-readable documentation for a field, as well as additional information that may be used to validate the field or create a user interface for data entry.

At a minimum a field descriptor will contain at least a name key, but MAY have additional keys as described below:

{
  "name": "name of field (e.g. column name)",
  "title": "A nicer human readable label or title for the field",
  "type": "A string specifying the type",
  "format": "A string specifying a format",
  "description": "A description for the field",
  "constraints": {
      # a constraints-descriptor
  }
}
  • a field descriptor MUST be a Hash
  • the field descriptor Hash MUST contain a name property. This property SHOULD correspond to the name of field/column in the data file (if it has a name). As such it SHOULD be unique (though it is possible, but very bad practice, for the data file to have multiple columns with the same name). Additionally, name SHOULD be considered case sensitive. In practice, case sensistivity for names can be limiting in certain scenarios, so consumers MAY choose to ignore case sensitivity for name values.
  • the field descriptor Hash MAY contain any number of other properties
  • specific properties that MAY be included in the Hash and whose meaning is defined in this spec are:

    • title: A nicer human readable label or title for the field
    • description: A description for this field e.g. “The recipient of the funds”
    • type: The type of the field (string, number etc) - see below for more detail. If type is not provided a consumer should assume a type of “string”.
    • format: A description of the format e.g. “DD.MM.YYYY” for a date. See below for more detail.
    • constraints: A constraints descriptor that can be used by consumers to validate field values

Field Constraints

A set of constraints can be associated with a field. These constraints can be used to validate data against a JSON Table Schema. The constraints might be used by consumers to validate, for example, the contents of a data package, or as a means to validate data being collected or updated via a data entry interface.

A constraints descriptor is a JSON hash. It MAY contain any of the following keys.

  • required – A boolean value which indicates whether a field must have a value in every row of the table. An empty string is considered to be a missing value.
  • minLength – An integer that specifies the minimum number of characters for a value
  • maxLength – An integer that specifies the maximum number of characters for a value
  • unique – A boolean. If true, then all values for that field MUST be unique within the data file in which it is found. This defines a unique key for a row although a row could potentially have several such keys.
  • pattern – A regular expression that can be used to test field values. If the regular expression matches then the value is valid. Values will be treated as a string of characters. It is recommended that values of this field conform to the standard XML Schema regular expression syntax. See also this reference.
  • minimum – specifies a minimum value for a field. This is different to minLength which checks number of characters. A minimum value constraint checks whether a field value is greater than or equal to the specified value. The range checking depends on the type of the field. E.g. an integer field may have a minimum value of 100; a date field might have a minimum date. If a minimum value constraint is specified then the field descriptor MUST contain a type key
  • maximum – as above, but specifies a maximum value for a field.
  • enum – An array of values, where each value MUST comply with the type and format of the field. The field value must exactly match a value in the enum array.

A constraints descriptor may contain multiple constraints, in which case a consumer MUST apply all the constraints when determining if a field value is valid.

A data file, e.g. an entry in a data package, is considered to be valid if all of its fields are valid according to their declared type and constraints.

Field Types and Formats

A field’s type property is a string indicating the type of this field.

A field’s format property is a string, being a keyword indicating a format for the field type.

Both type and format are optional: in a field descriptor, the absence of a type property indicates that the field is of the type “string”, and the absence of a format property indicates that the field’s type format is “default”.

Types are based on the type set of json-schema with some additions and minor modifications (cf other type lists include those in Elasticsearch types).

The type and format list is as follows:

  • string
    • string formats:
      • default: any valid string. Equivalent to not declaring a format.
      • email: A valid email address.
      • uri: A valid URI.
      • binary: A base64 encoded string representing binary data.
      • uuid: A string that is a uuid.
  • number
    • number formats:
      • default: any valid number. Equivalent to not declaring a format.
      • currency: A number that may include additional currency symbols and/or commas/semi-colons.
  • integer
    • integer formats:
      • default: any valid integer. Equivalent to not declaring a format.
  • boolean
    • In addition to primitive types, boolean values can be indicated with the following strings:
      • true: ‘yes’, ‘y’, ‘true’, ‘t’, ‘1’
      • false: ‘no’, ‘n’, ‘false’, ‘f’, ‘0’
    • boolean formats:
      • default: any valid boolean value or string that indicates a boolean value. Equivalent to not declaring a format.
  • null
    • In addition to primitive null types, null can be indicated with the following strings:
      • null: ‘null’, ‘none’, ‘nil’, ‘nan’, ‘-‘, ‘’
    • null formats:
      • default: Equivalent to not declaring a format.
  • object
    • object formats:
      • default: any valid JSON object. Equivalent to not declaring a format.
  • array
    • array formats:
      • default: any valid JSON array. Equivalent to not declaring a format.
  • datetime; date; time
    • datetime, date and time share the following format options:
      • default: An ISO8601 format string. Equivalent to not declaring a format.
        • date: This MUST be in ISO6801 format YYYY-MM-DD
        • datetime: a date-time. This MUST be in ISO 8601 format of YYYY-MM-DDThh:mm:ssZ in UTC time
        • time: a time without a date
      • any: Any parsable representation of the type. The implementing library can attempt to parse the datetime via a range of strategies. An example is dateutil.parser.parse from the python-dateutils library.
      • fmt:PATTERN: date/time values in this field conform to PATTERN where [PATTERN] follows the syntax of standard Python / C strptime. (That is, values in the this field should be parseable by Python / C standard strptime using PATTERN). Example: fmt:%d %b %y would correspond to dates like: 30 Nov 14
  • geopoint
    • geopoint formats:
      • default: A string of the pattern “lon, lat”, where lon is the longitude and lat is the latitude. Equivalent to not declaring a format.
      • array: An array of exactly two items, where each item is either a number, or a string parsable as a number, and the first item is lon and the second item is lat.
      • object: An object with exactly two keys, lat and lon
  • geojson
    • geojson formats:
      • default: A geojson object as per the GeoJSON spec. Equivalent to not declaring a format.
      • topojson: A topojson object as per the TopoJSON spec
  • any
    • Any type or format is accepted.

Primary Key

A primary key is a field or set of fields that uniquely identifies each row in the table.

The primaryKey entry in the schema Hash is optional. If present it specifies the primary key for this table.

The primaryKey, if present, MUST be:

  • Either: an array of strings with each string corresponding to one of the field name values in the fields array (denoting that the primary key is made up of those fields). It is acceptable to have an array with a single value (indicating just one field in the primary key). Strictly, order of values in the array does not matter. However, it is RECOMMENDED that one follow the order the fields in the fields has as client applications may utitlize the order of the primary key list (e.g. in concatenating values together).
  • Or: a single string corresponding to one of the field name values in the fields array (indicating that this field is the primary key). Note that this version corresponds to the array form with a single value (and can be seen as simply a more convenient way of specifying a single field primary key).

Here’s an example:

  "fields": [
    {
      "name": "a"
    },
    ...
  ],
  "primaryKey": "a"

Here’s an example with an array primary key:

"schema": {
  "fields": [
    {
      "name": "a"
    },
    {
      "name": "b"
    },
    {
      "name": "c"
    },
    ...
  ],
  "primaryKey": ["a", "c"]
 }

Foreign Keys

Foreign Keys by necessity must be able to reference other data objects. These data objects require a specific structure for the spec to work. Therefore, this spec makes two assumptions: * You have a Foreign Key to *self*, so no further meta data is required, and a special `self` keyword is employed. * You have a Foreign Key to data objects "elsewhere", in which case, the data objects being referenced must be Data Packages.

A foreign key is a reference where entries in a given field (or fields) on this table (‘resource’ in data package terminology) is a reference to an entry in a field (or fields) on a separate resource.

The foreignKeys property, if present, MUST be an Array. Each entry in the array must be a foreignKey. A foreignKey MUST be a Hash and:

  • MUST have a property fields. fields is a string or array specifying the field or fields on this resource that form the source part of the foreign key. The structure of the string or array is as per primaryKey above.
  • MUST have a property reference which MUST be a Hash. The Hash
    • MAY have a property datapackage. This property is a string being a url or name to a datapackage. If absent the implication is that this is a reference to a resource within the current data package. For self-referencing foreign keys, the value of datapackage MUST be empty.
    • MUST have a property resource which is the name of the resource within the referenced data package. For self-referencing foreign keys, the value of resource MUST be self.
    • MUST have a property fields which is a string if the outer fields is a string, else an array of the same length as the outer fields, describing the field (or fields) references on the destination resource. The structure of the string or array is as per primaryKey above.

Here’s an example:

  "fields": [
    {
      "name": "state"
    }
  ],
  "foreignKeys": [
    {
      "fields": "state"
      "reference": {
        "datapackage": "http://data.okfn.org/data/mydatapackage/",
        "resource": "the-resource",
        "fields": "state_id"
      }
    }
  ]

An example of a self-referencing foreign key:

  "fields": [
    {
      "name": "parent"
    },
    {
      "name": "id"
    }
  ],
  "foreignKeys": [
    {
      "fields": "parent"
      "reference": {
        "datapackage": "",
        "resource": "self",
        "fields": "id"
      }
    }
  ]

Appendix: Related Work

See Web-Oriented Data Formats for more details and links for each format.

DSPL

See https://developers.google.com/public-data/docs/schema/dspl18. Allowed values:

  • string
  • float
  • integer
  • boolean
  • date
  • concept

Google BigQuery

Example schema:

"schema": {
  "fields":[
     {
        "mode": "nullable",
        "name": "placeName",
        "type": "string"
     },
     {
        "mode": "nullable",
        "name": "kind",
        "type": "string"
     },  ...
   ]
 }

Types:

  • string - UTF-8 encoded string up to 64K of data (as opposed to 64K characters).
  • integer - IEEE 64-bit signed integers: [-263-1, 263-1]
  • float - IEEE 754-2008 formatted floating point values
  • boolean - “true” or “false”, case-insensitive
  • record (JSON only) - a JSON object; also known as a nested record

XML Schema

See http://www.w3.org/TR/xmlschema-2/#built-in-primitive-datatypes

  • string
  • boolean
  • decimal
  • float
  • double
  • duration
  • dateTime
  • time
  • date
  • gYearMonth
  • gYear
  • gMonthDay
  • gDay
  • gMonth
  • hexBinary
  • base64Binary
  • anyURI

Type Lists