Table Schema

Authors	Rufus Pollock, Paul Walsh, Adam Kariv, Evgeny Karev, Peter Desmet, Ethan Welty, DC Slagel
Profile	https://datapackage.org/profiles/2.0/tableschema.json

A simple format to declare a schema for tabular data. The schema is designed to be expressible in JSON

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

Introduction

Table Schema is a simple language- and implementation-agnostic way to declare a schema for tabular data. Table Schema is well suited for use cases around handling and validating tabular data in text formats such as CSV, but its utility extends well beyond this core usage, towards a range of applications where data benefits from a portable schema format.

Concepts

This specification heavily relies on the following concepts:

• Tabular Data
• Data Representation

In this document, we will explicitly refer to either the Native Representation or Logical Representation of data in places where it prevents ambiguity for those engaging with the specification, especially implementors. Note, that this specification does not deal in any way with Physical Representation of data.

Note, that whenever a native value is allowed to be provided in this spec, the most similar JSON type should be used to represent it. If no such type exists (e.g. in case there’s a native date value), a string representation of that value should be provided. Such mappings between native types and JSON types, and the string representations described above are file format specific and left for implementors to decide (unless defined explicitly in this specification or its appendixes).

Descriptor

Table Schema descriptor MUST be a descriptor as per Descriptor definition. A list of standard properties that can be included into a descriptor is defined in the Properties section.

An example of a Table Schema descriptor:

{
  "fields": [
    {
      "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",
      "example": "An example value for the field",
      "description": "A description for the field"
      ...
    },
    ...
  ],
  "missingValues": [ ... ],
  "primaryKey": [ ... ],
  "foreignKeys": [... ]
}

Properties

Schema

A Table Schema descriptor MAY contain these standard properties:

fields [required]

A Table Schema descriptor MUST contain a property fields. fields MUST be an array where each entry in the array is a field descriptor as defined below.

The way Table Schema fields are mapped onto the data source fields are defined by the fieldsMatch property. By default, the most strict approach is applied, i.e. fields in the data source MUST completely match the elements in the fields array, both in number and order. Using different options of the fieldsMatch property, a data producer can relax requirements for the data source.

$schema

A root level Table Schema descriptor MAY have a $schema property that MUST point to a profile as per Profile definition that MUST include all the metadata constraints required by this specification.

The default value is https://datapackage.org/profiles/1.0/tableschema.json and the recommended value is https://datapackage.org/profiles/2.0/tableschema.json.

fieldsMatch

A Table Schema descriptor MAY contain a property fieldsMatch that MUST be a string with the following possible values and the exact value by default:

• exact (default): The data source MUST have exactly the same fields as defined in the fields array. Fields MUST be mapped by their order.
• equal: The data source MUST have exactly the same fields as defined in the fields array. Fields MUST be mapped by their names.
• subset: The data source MUST have all the fields defined in the fields array, but MAY have more. Fields MUST be mapped by their names.
• superset: The data source MUST only have fields defined in the fields array, but MAY have fewer. Fields MUST be mapped by their names.
• partial: The data source MUST have at least one field defined in the fields array. Fields MUST be mapped by their names.

missingValues

Many datasets arrive with missing data values, either because a value was not collected or it never existed. Missing values may be indicated simply by the value being empty in other cases a special value may have been used e.g. -, NaN, 0, -9999 etc.

The missingValues property configures which native values MUST be treated as logical null values. If provided, the missingValues property MUST be an array of values.

This conversion to null is done before any other attempted type-specific conversion. The default value [ "" ] means that empty strings will be converted to null before any other processing takes place. Providing the empty list [] means that no conversion to null will be done, on any value.

Examples of the missingValues property:

"missingValues": [""]
"missingValues": ["-"]
"missingValues": ["NaN", "-"]
"missingValues": [-9999]

primaryKey

A primary key is a field or set of fields that uniquely identifies each row in the table. Per SQL standards, the fields cannot be null, so their use in the primary key is equivalent to adding required: true to their constraints.

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

The primaryKey, if present, MUST be 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 utilize the order of the primary key list (e.g. in concatenating values together).

Here’s an example:

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

Backward Compatibility

Data consumer MUST support the primaryKey property in a form of a single string e.g. primaryKey: a which was a part of the v1.0 of the specification.

uniqueKeys

A unique key is a field or a set of fields that are required to have unique logical values in each row in the table. It is directly modeled on the concept of unique constraint in SQL.

The uniqueKeys property, if present, MUST be a non-empty array. Each entry in the array MUST be a uniqueKey. A uniqueKey MUST be an array of strings with each string corresponding to one of the field name values in the fields array, denoting that the unique key is made up of those fields. It is acceptable to have an array with a single value, indicating just one field in the unique key.

An example of using the uniqueKeys property:

"fields": [
  {
    "name": "a"
  },
  {
    "name": "b"
  },
  {
    "name": "c"
  }
],
"uniqueKeys": [
  ["a"],
  ["a", "b"],
  ["a", "c"]
]

In the case of the definition above, the data in the table has to be considered valid only if:

• each row has a unique logical value in the field a
• each row has a unique set of logical values in the fields a and b
• each row has a unique set of logical values in the fields a and c

Handling null values

All the field values that are on the logical level are considered to be null values MUST be excluded from the uniqueness check, as the uniqueKeys property is modeled on the concept of unique constraint in SQL.

Relation to constraints.unique

In contrast with field.constraints.unique, uniqueKeys allows to define uniqueness as a combination of fields. Both properties SHOULD be assessed separately.

foreignKeys

A foreign key is a reference where values in a field (or fields) on the table (‘resource’ in data package terminology) described by this Table Schema connect to values a field (or fields) on this or a separate table (resource). They are directly modelled on the concept of foreign keys in SQL.

The foreignKeys property, if present, MUST be an Array. Each entry in the array MUST be a foreignKey. A foreignKey MUST be a object and MUST have the following properties:

• fields - fields is an array of strings specifying the field or fields on this resource that form the source part of the foreign key. The structure of the array is as per primaryKey above.
• reference - reference MUST be a object. The object
  • MUST have a property fields which is an array of strings of the same length as the outer fields, describing the field (or fields) references on the destination resource. The structure of the array is as per primaryKey above.
  • MAY have a property resource which is the name of the resource within the current data package, i.e. the data package within which this Table Schema is located. For referencing another data resource the resource property MUST be provided. For self-referencing, i.e. references between fields in this Table Schema, the resource property MUST be omitted.

Here’s an example:

"resources": [
  {
    "name": "state-codes",
    "schema": {
      "fields": [
        {"name": "code"}
      ]
    }
  },
  {
    "name": "population-by-state",
    "schema": {
      "fields": [
        {"name": "state-code"}
      ],
      "foreignKeys": [
        {
          "fields": ["state-code"],
          "reference": {
            "resource": "state-codes",
            "fields": ["code"]
          }
        }
      ]
    }
  }
]

An example of a self-referencing foreign key:

"resources": [
  {
    "name": "xxx",
    "schema": {
      "fields": [
        {"name": "parent"},
        {"name": "id"}
      ],
      "foreignKeys": [
        {
          "fields": ["parent"],
          "reference": {
            "fields": ["id"]
          }
        }
      ]
    }
  }
]

Foreign Keys create links between one Table Schema and another Table Schema, and implicitly between the data tables described by those Table Schemas. If the foreign key is referring to another Table Schema how is that other Table Schema discovered? The answer is that a Table Schema will usually be embedded inside some larger descriptor for a dataset, in particular as the schema for a resource in the resources array of a Data Package. It is the use of Table Schema in this way that permits a meaningful use of a non-empty resource property on the foreign key.

Backward Compatibility

If the value of the foreignKey.reference.resource property is an empty string "" a data consumer MUST interpret it as an omited property as an empty string for self-referencing was a part of the v1.0 of the specification.

Backward Compatibility

Data consumer MUST support the foreignKey.fields and foreignKey.reference.fields properties in a form of a single string e.g. "fields": "a" which was a part of the v1.0 of the specification.

Field

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

Here is an illustration:

{
  "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",
  "example": "An example value for the field",
  "description": "A description for the field",
  "constraints": {
      ...
  }
}

The field descriptor object MAY contain any number of other properties. Some specific properties are defined below. Of these, only the name property is REQUIRED.

name [required]

The field descriptor MUST contain a name property and it MUST be unique amongst other field names in this Table Schema. This property SHOULD correspond to the name of a column in the data file if it has a name.

Backward Compatibility

If the name properties are not unique amongst a Table Schema a data consumer MUST NOT interpret it as an invalid descriptor as duplicate name properties were allowed in the v1.0 of the specification.

type and format

These properties are used to give the type of the field (string, number, etc.) - see below for more detail. If type is not provided a consumer MUST utilize the any type for the field instead of inferring it from the field’s values.

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

A field’s format property is a string, 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 “any”, 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).

Backward Compatibility

If format property start with fmt: prefix, an implementation MUST remove the prefix as it was the way of providing temporal formats in Data Package Standard (v0).

title

A human readable label or title for the field

description

A description for this field e.g. “The recipient of the funds”

example

An example value for the field

constraints

See Field Constraints

missingValues

A list of missing values for this field as per Missing Values definition. If this property is defined, it takes precedence over the schema-level property and completely replaces it for the field without combining the values.

For example, for the Table Schema below:

"fields": [
  {
    "name": "column1"
  },
  {
    "name": "column2",
    "missingValues": ["-"]
  }
],
"missingValues": ["", "NA"]

A data consumer MUST:

• interpret "" and NA as missing values for column1
• interpret only - as a missing value for column2

rdfType

A richer, “semantic”, description of the “type” of data in a given column MAY be provided using a rdfType property on a field descriptor.

The value of the rdfType property MUST be the URI of a RDF Class, that is an instance or subclass of RDF Schema Class object.

Here is an example using the Schema.org RDF Class http://schema.org/Country:

| Country | Year Date | Value |
| ------- | --------- | ----- |
| US      | 2010      | ...   |

The corresponding Table Schema is:

{
  "fields": [
    {
      "name": "Country",
      "type": "string",
      "rdfType": "http://schema.org/Country"
    }
    ...
  ]
}

Field Types

The type list with associated formats and other related properties is as follows.

string

The field contains strings, that is, sequences of characters.

Supported formats:

• default: any valid string.
• email: A valid email address.
• uri: A valid URI.
• binary: A base64 encoded string representing binary data.
• uuid: A string that is a uuid.

Native Representaiton

Values MUST be represented as strings.

number

The field contains numbers of any kind including decimals.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, values MUST be represented as strings following the rules below.

Formatting follows that of decimal in XMLSchema: a non-empty finite-length sequence of decimal digits separated by a period as a decimal indicator. An optional leading sign is allowed. If the sign is omitted, ”+” is assumed. Leading and trailing zeroes are optional. If the fractional part is zero, the period and following zero(es) can be omitted. For example: ‘-1.23’, ‘12678967.543233’, ‘+100000.00’, ‘210’.

The following special string values are permitted (case need not be respected):

• NaN: not a number
• INF: positive infinity
• -INF: negative infinity

A number MAY also have a trailing:

• exponent: this MUST consist of an E followed by an optional + or - sign followed by one or more decimal digits (0-9)

Formatting MAY be modified using these additional properties:

• decimalChar: A string whose value is used to represent a decimal point within the number. The default value is ”.“.
• groupChar: A string whose value is used to group digits within the number. This property does not have a default value. A common value is ”,” e.g. “100,000”.
• bareNumber: a boolean field with a default of true. If true the contents of this field MUST follow the formatting constraints already set out. If false the contents of this field may contain leading and/or trailing non-numeric characters (which implementors MUST therefore strip). The purpose of bareNumber is to allow publishers to publish numeric data that contains trailing characters such as percentages e.g. 95% or leading characters such as currencies e.g. €95 or EUR 95. Note that it is entirely up to implementors what, if anything, they do with stripped text.

integer

The field contains integers - that is whole numbers.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, values MUST be represented as strings following the rules below.

Integer values are indicated in the standard way for any valid integer. Formatting MAY be modified using these additional properties:

• groupChar: A string whose value is used to group digits within the integer. This property does not have a default value. A common value is ”,” e.g. “100,000”.
• bareNumber: a boolean field with a default of true. If true the contents of this field MUST follow the formatting constraints already set out. If false the contents of this field may contain leading and/or trailing non-numeric characters (which implementors MUST therefore strip). The purpose of bareNumber is to allow publishers to publish numeric data that contains trailing characters such as percentages e.g. 95% or leading characters such as currencies e.g. €95 or EUR 95. Note that it is entirely up to implementors what, if anything, they do with stripped text.

boolean

The field contains boolean data i.e. logical true or logical false.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, values MUST be represented as defined by the trueValues and falseValues properties that can be customized to user need:

• trueValues: An array of native values to be interpreted as logical true. The default is [ "true", "True", "TRUE", "1" ].
• falseValues: An array of native values to be interpreted as logical false. The default is [ "false", "False", "FALSE", "0" ].

object

The field contains a valid JSON object.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, values MUST be strings that are valid serialized JSON objects.

array

The field contains a valid JSON array.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, values MUST be strings that are valid serialized JSON arrays.

list

The field contains data that is an ordered one-level depth collection of primitive values with a fixed item type. In comparison to the array type, the list type is directly modelled on the concept of SQL typed collections.

The list field can be customised with this additional property:

• itemType: specifies the list item type in terms of existent Table Schema types. If present, it MUST be one of string, integer, boolean, number, datetime, date, and time. If not present, the default is string. A data consumer MUST process list items as it were individual values of the corresponding data type.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, the field MUST contain a string with list items separated by a delimiter which is , (comma) by default e.g. value1,value2. The list items MUST be serialized using a default format of the corresponding itemType. The delimiter can be customised with this additional property:

• delimiter: specifies the character sequence which separates list items. If not present, the default is , (comma).

datetime

The field contains a date with a time and an optional timezone.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, values MUST be represented as strings in one of the following formats:

• default: values MUST be in a form defined by XML Schema containing required date and time parts, followed by optional milliseconds and timezone parts, for example, 2024-01-26T15:00:00 or 2024-01-26T15:00:00.300-05:00.
• <PATTERN>: values in this field can be parsed according to <PATTERN>. <PATTERN> MUST follow the syntax of standard Python / C strptime. Values in the this field SHOULD be parsable by Python / C standard strptime using <PATTERN>. Example for "format": ""%d/%m/%Y %H:%M:%S" which would correspond to a date with time like: 12/11/2018 09:15:32.
• any: Any parsable representation of the value. 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. It is NOT RECOMMENDED to use any format as it might cause interoperability issues.

date

The field contains a date without a time.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, values MUST be represented as strings in one of the following formats:

• default: values MUST be yyyy-mm-dd e.g. 2024-01-26
• <PATTERN>: The same as for datetime
• any: The same as for datetime

time

The field contains a time without a date.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, values MUST be represented as strings in one of the following formats:

• default: values MUST be hh:mm:ss e.g. 15:00:00
• <PATTERN>: The same as for datetime
• any: The same as for datetime

year

The field contains a calendar year.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, values MUST be represented as strings as per XMLSchema gYear. Usual representation as a string is YYYY.

yearmonth

The field containts a specific month in a specific year.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, values MUST be represented as strings as per XMLSchema gYearMonth. Usual representation as a string is YYYY-MM.

duration

The field contains a duration of time.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, values MUST be represented as strings as per XML Schema duration.

The duration MUST be in the ISO 8601 extended format PnYnMnDTnHnMnS, where nY represents the number of years, nM the number of months, nD the number of days, T is the date/time separator, nH the number of hours, nM the number of minutes and nS the number of seconds. The number of seconds can include decimal digits to arbitrary precision. Date and time elements including their designator MAY be omitted if their value is zero, and lower order elements MAY also be omitted for reduced precision.

geopoint

The field contains data describing a geographic point i.e. lon and lat values that are floating point numbers.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, values MUST be represented as strings in one of the following formats:

• default: A string of the pattern “lon, lat”, where each value is a number, and lon is the longitude and lat is the latitude (note the space is optional after the ,). E.g. "90.50, 45.50".
• array: A JSON array, or a string parsable as a JSON array, of exactly two items, where each item is a number, and the first item is lon and the second item is lat e.g. [90.50, 45.50]
• object: A JSON object with exactly two keys, lon and lat and each value is a number e.g. {"lon": 90.50, "lat": 45.50}

geojson

The field contains a JSON object according to GeoJSON or TopoJSON specifications.

Supported formats:

• default: A geojson object as per the GeoJSON spec.
• topojson: A topojson object as per the TopoJSON spec.

Native Representaiton

If supported, values MUST be natively represented by a data format. If not supported, values MUST be represented as strings that are valid serialized JSON objects.

any

The field contains values of a unspecified or mixed type. A data consumer MUST NOT perform any processing on values and MUST interpret them as it is in native representation of data. This data type is directly modelled on the concept of the any type of strongly typed object-oriented languages like TypeScript.

For example, having a Table Schema below:

{
  "fields": [
    { "name": "id", "type": "any" },
    { "name": "name", "type": "any" }
  ]
}

This CSV data file will have logical values as below:

id,name
1,apple
2,orange

{id: "1", name: "apple"}
{id: "2", name: "orange"}

While this JSON data file will have logical values as below:

[
  ["id", "name"]
  [1, "apple"]
  [2, "orange"]
]

{id: 1, name: "apple"}
{id: 2, name: "orange"}

Note, that for the CSV data source the id field is interpreted as a string because CSV supports only one data type i.e. string, and for the JSON data source the id field is interpreted as an integer because JSON supports a numeric data type and the value was declared as an integer. Also, for the Table Schema above a type property for each field can be omitted as it is a default field type.

Native Representaiton

Values MUST be natively represented by a data format.

Field Constraints

The constraints property on Table Schema Fields can be used by consumers to list constraints for validating field values. For example, validating the data in a Tabular Data Resource against its Table Schema; or as a means to validate data being collected or updated via a data entry interface.

All constraints MUST be tested against the logical representation of data, and the native representation of constraint values MAY be primitive types as possible in JSON, or represented as strings that are castable with the type and format rules of the field.

A constraints descriptor MUST be a JSON object and MAY contain one or more of the following properties:

required

• Type: boolean
• Fields: all

Indicates whether this field cannot be null. If required is false (the default), then null is allowed. See the section on missingValues for how, in the native representation of the data, strings can represent null values.

unique

• Type: boolean
• Fields: all

If true, then all values for that field MUST be unique within the data file in which it is found.

minLength

• Type: integer
• Fields: collections (string, list, array, object)

An integer that specifies the minimum length of a value.

maxLength

• Type: integer
• Fields: collections (string, list, array, object)

An integer that specifies the maximum length of a value.

minimum

• Type: integer, number, date, time, datetime, duration, year, yearmonth
• Fields: integer, number, date, time, datetime, duration, year, yearmonth

Specifies a minimum value for a field. This is different to minLength which checks the number of items in the value. 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

• Type: integer, number, date, time, datetime, duration, year, yearmonth
• Fields: integer, number, date, time, datetime, duration, year, yearmonth

As for minimum, but specifies a maximum value for a field.

exclusiveMinimum

• Type: integer, number, date, time, datetime, duration, year, yearmonth
• Fields: integer, number, date, time, datetime, duration, year, yearmonth

As for minimum, but for expressing exclusive range.

exclusiveMaximum

• Type: integer, number, date, time, datetime, duration, year, yearmonth
• Fields: integer, number, date, time, datetime, duration, year, yearmonth

As for maximum, but for expressing exclusive range.

jsonSchema

• Type: object
• Fields: array, object

A valid JSON Schema object to validate field values. If a field value conforms to the provided JSON Schema then this field value is valid.

pattern

• Type: string
• Fields: string

A regular expression that can be used to test field values. If the regular expression matches then the value is valid. The values of this field MUST conform to the standard XML Schema regular expression syntax.

enum

• Type: array
• Fields: all

The value of the field MUST exactly match one of the values in the enum array.

Implementation Note

• Implementations SHOULD report an error if an attempt is made to evaluate a value against an unsupported constraint.
• A constraints descriptor MAY contain multiple constraints, in which case implementations MUST apply all the constraints when determining if a field value is valid.
• Constraints MUST be applied on the logical representation of field values and constraint values.

Related Work

Table Schema draws content and/or inspiration from, among others, the following specifications and implementations:

• XML Schema
• Google BigQuery
• JSON Schema
• DSPL
• HTML5 Forms
• Elasticsearch