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array

Arrays are used for ordered elements. In JSON, each element in an array may be of a different type.

Language-specific info:
Python
Ruby

In Python, "array" is analogous to a list or tuple type, depending on usage. However, the json module in the Python standard library will always use Python lists to represent JSON arrays.

schema
1
{ "type": "array" }
data
1
[1, 2, 3, 4, 5]
compliant to schema
data
1
[3, "different", { "types": "of values" }]
compliant to schema
data
1
{"Not": "an array"}
not compliant to schema

There are two ways in which arrays are generally used in JSON:

  • List validation: a sequence of arbitrary length where each item matches the same schema.
  • Tuple validation: a sequence of fixed length where each item may have a different schema. In this usage, the index (or location) of each item is meaningful as to how the value is interpreted. (This usage is often given a whole separate type in some programming languages, such as Python's tuple).

Items

List validation is useful for arrays of arbitrary length where each item matches the same schema. For this kind of array, set the items keyword to a single schema that will be used to validate all of the items in the array.

In the following example, we define that each item in an array is a number:

schema
1
{
2
"type": "array",
3
"items": {
4
"type": "number"
5
}
6
}
data
1
[1, 2, 3, 4, 5]
compliant to schema

A single "non-number" causes the whole array to be invalid:

data
1
[1, 2, "3", 4, 5]
not compliant to schema

The empty array is always valid:

data
1
[]
compliant to schema

Tuple validation

Tuple validation is useful when the array is a collection of items where each has a different schema and the ordinal index of each item is meaningful.

For example, you may represent a street address such as 1600 Pennsylvania Avenue NW as a 4-tuple of the form:

data
1
[number, street_name, street_type, direction]

[number, street_name, street_type, direction]

Each of these fields will have a different schema:

  • number: The address number. Must be a number.
  • street_name: The name of the street. Must be a string.
  • street_type: The type of street. Should be a string from a fixed set of values.
  • direction: The city quadrant of the address. Should be a string from a different set of values.

To do this, we use the prefixItems keyword. prefixItems is an array, where each item is a schema that corresponds to each index of the document's array. That is, an array where the first element validates the first element of the input array, the second element validates the second element of the input array, etc.

Draft-specific info
In Draft 4 - 2019-09, tuple validation was handled by an alternate form of the items keyword. When items was an array of schemas instead of a single schema, it behaved the way prefixItems behaves.

Here's the example schema:

schema
1
{
2
"type": "array",
3
"prefixItems": [
4
{ "type": "number" },
5
{ "type": "string" },
6
{ "enum": ["Street", "Avenue", "Boulevard"] },
7
{ "enum": ["NW", "NE", "SW", "SE"] }
8
]
9
}
data
1
[1600, "Pennsylvania", "Avenue", "NW"]
compliant to schema

"Drive" is not one of the acceptable street types:

data
1
[24, "Sussex", "Drive"]
not compliant to schema

This address is missing a street number:

data
1
["Palais de l'Élysée"]
not compliant to schema

It's okay to not provide all of the items:

data
1
[10, "Downing", "Street"]
compliant to schema

And, by default, it's also okay to add additional items to end:

data
1
[1600, "Pennsylvania", "Avenue", "NW", "Washington"]
compliant to schema

Additional Items

The items keyword can be used to control whether it's valid to have additional items in a tuple beyond what is defined in prefixItems. The value of the items keyword is a schema that all additional items must pass in order for the keyword to validate.

Draft-specific info:
Draft 4 - 2019-09
Draft 6 - 2019-09

Before to Draft 2020-12, you would use the additionalItems keyword to constrain additional items on a tuple. It works the same as items, only the name has changed.

Here, we'll reuse the example schema above, but set items to false, which has the effect of disallowing extra items in the tuple.

schema
1
{
2
"type": "array",
3
"prefixItems": [
4
{ "type": "number" },
5
{ "type": "string" },
6
{ "enum": ["Street", "Avenue", "Boulevard"] },
7
{ "enum": ["NW", "NE", "SW", "SE"] }
8
],
9
"items": false
10
}
data
1
[1600, "Pennsylvania", "Avenue", "NW"]
compliant to schema

It's ok to not provide all of the items:

data
1
[1600, "Pennsylvania", "Avenue"]
compliant to schema

But, since items is false, we can't provide extra items:

data
1
[1600, "Pennsylvania", "Avenue", "NW", "Washington"]
not compliant to schema

You can express more complex constraints by using a non-boolean schema to constrain what value additional items can have. In that case, we could say that additional items are allowed, as long as they are all strings:

schema
1
{
2
"type": "array",
3
"prefixItems": [
4
{ "type": "number" },
5
{ "type": "string" },
6
{ "enum": ["Street", "Avenue", "Boulevard"] },
7
{ "enum": ["NW", "NE", "SW", "SE"] }
8
],
9
"items": { "type": "string" }
10
}

Extra string items are ok ...

data
1
[1600, "Pennsylvania", "Avenue", "NW", "Washington"]
compliant to schema

... but not anything else

data
1
[1600, "Pennsylvania", "Avenue", "NW", 20500]
not compliant to schema

Unevaluated Items

New in draft 2019-09

Documentation Coming Soon

Contains

New in draft 6

While the items schema must be valid for every item in the array, the contains schema only needs to validate against one or more items in the array.

schema
1
{
2
"type": "array",
3
"contains": {
4
"type": "number"
5
}
6
}

A single "number" is enough to make this pass:

data
1
["life", "universe", "everything", 42]
compliant to schema

But if we have no number, it fails:

data
1
["life", "universe", "everything", "forty-two"]
not compliant to schema

All numbers is, of course, also okay:

data
1
[1, 2, 3, 4, 5]
compliant to schema

minContains / maxContains

New in draft 2019-09

minContains and maxContains can be used with contains to further specify how many times a schema matches a contains constraint. These keywords can be any non-negative number including zero.

schema
1
{
2
"type": "array",
3
"contains": {
4
"type": "number"
5
},
6
"minContains": 2,
7
"maxContains": 3
8
}

Fails minContains

data
1
["apple", "orange", 2]
not compliant to schema
data
1
["apple", "orange", 2, 4]
compliant to schema
data
1
["apple", "orange", 2, 4, 8]
compliant to schema

Fails maxContains

data
1
["apple", "orange", 2, 4, 8, 16]
not compliant to schema

Length

The length of the array can be specified using the minItems and maxItems keywords. The value of each keyword must be a non-negative number. These keywords work whether doing list validation or tuple-validation.

schema
1
{
2
"type": "array",
3
"minItems": 2,
4
"maxItems": 3
5
}
data
1
[]
not compliant to schema
data
1
[1]
not compliant to schema
data
1
[1, 2]
compliant to schema
data
1
[1, 2, 3]
compliant to schema
data
1
[1, 2, 3, 4]
not compliant to schema

Uniqueness

A schema can ensure that each of the items in an array is unique. Simply set the uniqueItems keyword to true.

schema
1
{
2
"type": "array",
3
"uniqueItems": true
4
}
data
1
[1, 2, 3, 4, 5]
compliant to schema
data
1
[1, 2, 3, 3, 4]
not compliant to schema

The empty array always passes:

data
1
[]
compliant to schema