9.16. JSON Functions and Operators
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## 9.16. JSON Functions and Operators

This section describes:

- functions and operators for processing and creating JSON data

- the SQL/JSON path language

To learn more about the SQL/JSON standard, see [sqltr-19075-6]. For details on JSON types supported in PostgreSQL, see Section 8.14.

### 9.16.1. Processing and Creating JSON Data

Table 9.45 shows the operators that are available for use with JSON data types (see Section 8.14). In addition, the usual comparison operators shown in Table 9.1 are available for `jsonb`, though not for `json`. The comparison operators follow the ordering rules for B-tree operations outlined in Section 8.14.4.

Table 9.45. `json` and `jsonb` Operators

Operator Description Example(s)
`json` `->` `integer` → `json` `jsonb` `->` `integer` → `jsonb` Extracts `n`'th element of JSON array (array elements are indexed from zero, but negative integers count from the end). `'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json -> 2` → `{"c":"baz"}` `'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json -> -3` → `{"a":"foo"}`
`json` `->` `text` → `json` `jsonb` `->` `text` → `jsonb` Extracts JSON object field with the given key. `'{"a": {"b":"foo"}}'::json -> 'a'` → `{"b":"foo"}`
`json` `->>` `integer` → `text` `jsonb` `->>` `integer` → `text` Extracts `n`'th element of JSON array, as `text`. `'[1,2,3]'::json ->> 2` → `3`
`json` `->>` `text` → `text` `jsonb` `->>` `text` → `text` Extracts JSON object field with the given key, as `text`. `'{"a":1,"b":2}'::json ->> 'b'` → `2`
`json` `#>` `text[]` → `json` `jsonb` `#>` `text[]` → `jsonb` Extracts JSON sub-object at the specified path, where path elements can be either field keys or array indexes. `'{"a": {"b": ["foo","bar"]}}'::json #> '{a,b,1}'` → `"bar"`
`json` `#>>` `text[]` → `text` `jsonb` `#>>` `text[]` → `text` Extracts JSON sub-object at the specified path as `text`. `'{"a": {"b": ["foo","bar"]}}'::json #>> '{a,b,1}'` → `bar`

### Note

The field/element/path extraction operators return NULL, rather than failing, if the JSON input does not have the right structure to match the request; for example if no such key or array element exists.

Some further operators exist only for `jsonb`, as shown in Table 9.46. Section 8.14.4 describes how these operators can be used to effectively search indexed `jsonb` data.

Table 9.46. Additional `jsonb` Operators

Operator Description Example(s)
`jsonb` `@>` `jsonb` → `boolean` Does the first JSON value contain the second? (See Section 8.14.3 for details about containment.) `'{"a":1, "b":2}'::jsonb @> '{"b":2}'::jsonb` → `t`
`jsonb` `<@` `jsonb` → `boolean` Is the first JSON value contained in the second? `'{"b":2}'::jsonb <@ '{"a":1, "b":2}'::jsonb` → `t`
`jsonb` `?` `text` → `boolean` Does the text string exist as a top-level key or array element within the JSON value? `'{"a":1, "b":2}'::jsonb ? 'b'` → `t` `'["a", "b", "c"]'::jsonb ? 'b'` → `t`
`jsonb` `?|` `text[]` → `boolean` Do any of the strings in the text array exist as top-level keys or array elements? `'{"a":1, "b":2, "c":3}'::jsonb ?| array['b', 'd']` → `t`
`jsonb` `?&` `text[]` → `boolean` Do all of the strings in the text array exist as top-level keys or array elements? `'["a", "b", "c"]'::jsonb ?& array['a', 'b']` → `t`

`jsonb` `||` `jsonb` → `jsonb`

Concatenates two `jsonb` values. Concatenating two arrays generates an array containing all the elements of each input. Concatenating two objects generates an object containing the union of their keys, taking the second object's value when there are duplicate keys. All other cases are treated by converting a non-array input into a single-element array, and then proceeding as for two arrays. Does not operate recursively: only the top-level array or object structure is merged.

`'["a", "b"]'::jsonb || '["a", "d"]'::jsonb` → `["a", "b", "a", "d"]`

`'{"a": "b"}'::jsonb || '{"c": "d"}'::jsonb` → `{"a": "b", "c": "d"}`

`'[1, 2]'::jsonb || '3'::jsonb` → `[1, 2, 3]`

`'{"a": "b"}'::jsonb || '42'::jsonb` → `[{"a": "b"}, 42]`

To append an array to another array as a single entry, wrap it in an additional layer of array, for example:

`'[1, 2]'::jsonb || jsonb_build_array('[3, 4]'::jsonb)` → `[1, 2, [3, 4]]`

`jsonb` `-` `text` → `jsonb` Deletes a key (and its value) from a JSON object, or matching string value(s) from a JSON array. `'{"a": "b", "c": "d"}'::jsonb - 'a'` → `{"c": "d"}` `'["a", "b", "c", "b"]'::jsonb - 'b'` → `["a", "c"]`
`jsonb` `-` `text[]` → `jsonb` Deletes all matching keys or array elements from the left operand. `'{"a": "b", "c": "d"}'::jsonb - '{a,c}'::text[]` → `{}`
`jsonb` `-` `integer` → `jsonb` Deletes the array element with specified index (negative integers count from the end). Throws an error if JSON value is not an array. `'["a", "b"]'::jsonb - 1` → `["a"]`
`jsonb` `#-` `text[]` → `jsonb` Deletes the field or array element at the specified path, where path elements can be either field keys or array indexes. `'["a", {"b":1}]'::jsonb #- '{1,b}'` → `["a", {}]`
`jsonb` `@?` `jsonpath` → `boolean` Does JSON path return any item for the specified JSON value? `'{"a":[1,2,3,4,5]}'::jsonb @? '$.a[*] ? (@ > 2)'` → `t`
`jsonb` `@@` `jsonpath` → `boolean` Returns the result of a JSON path predicate check for the specified JSON value. Only the first item of the result is taken into account. If the result is not Boolean, then `NULL` is returned. `'{"a":[1,2,3,4,5]}'::jsonb @@ '$.a[*] > 2'` → `t`

### Note

The `jsonpath` operators `@?` and `@@` suppress the following errors: missing object field or array element, unexpected JSON item type, datetime and numeric errors. The `jsonpath`-related functions described below can also be told to suppress these types of errors. This behavior might be helpful when searching JSON document collections of varying structure.

Table 9.47 shows the functions that are available for constructing `json` and `jsonb` values.

Table 9.47. JSON Creation Functions

Function Description Example(s)

`to_json` ( `anyelement` ) → `json`

`to_jsonb` ( `anyelement` ) → `jsonb`

Converts any SQL value to `json` or `jsonb`. Arrays and composites are converted recursively to arrays and objects (multidimensional arrays become arrays of arrays in JSON). Otherwise, if there is a cast from the SQL data type to `json`, the cast function will be used to perform the conversion;[a] otherwise, a scalar JSON value is produced. For any scalar other than a number, a Boolean, or a null value, the text representation will be used, with escaping as necessary to make it a valid JSON string value.

`to_json('Fred said "Hi."'::text)` → `"Fred said \"Hi.\""`

`to_jsonb(row(42, 'Fred said "Hi."'::text))` → `{"f1": 42, "f2": "Fred said \"Hi.\""}`

`array_to_json` ( `anyarray` [, `boolean` ] ) → `json` Converts an SQL array to a JSON array. The behavior is the same as `to_json` except that line feeds will be added between top-level array elements if the optional boolean parameter is true. `array_to_json('{{1,5},{99,100}}'::int[])` → `[[1,5],[99,100]]`
`row_to_json` ( `record` [, `boolean` ] ) → `json` Converts an SQL composite value to a JSON object. The behavior is the same as `to_json` except that line feeds will be added between top-level elements if the optional boolean parameter is true. `row_to_json(row(1,'foo'))` → `{"f1":1,"f2":"foo"}`
`json_build_array` ( `VARIADIC` `"any"` ) → `json` `jsonb_build_array` ( `VARIADIC` `"any"` ) → `jsonb` Builds a possibly-heterogeneously-typed JSON array out of a variadic argument list. Each argument is converted as per `to_json` or `to_jsonb`. `json_build_array(1, 2, 'foo', 4, 5)` → `[1, 2, "foo", 4, 5]`

`json_build_object` ( `VARIADIC` `"any"` ) → `json`

`jsonb_build_object` ( `VARIADIC` `"any"` ) → `jsonb`

Builds a JSON object out of a variadic argument list. By convention, the argument list consists of alternating keys and values. Key arguments are coerced to text; value arguments are converted as per `to_json` or `to_jsonb`.

`json_build_object('foo', 1, 2, row(3,'bar'))` → `{"foo" : 1, "2" : {"f1":3,"f2":"bar"}}`

`json_object` ( `text[]` ) → `json`

`jsonb_object` ( `text[]` ) → `jsonb`

Builds a JSON object out of a text array. The array must have either exactly one dimension with an even number of members, in which case they are taken as alternating key/value pairs, or two dimensions such that each inner array has exactly two elements, which are taken as a key/value pair. All values are converted to JSON strings.

`json_object('{a, 1, b, "def", c, 3.5}')` → `{"a" : "1", "b" : "def", "c" : "3.5"}`

`json_object('{{a, 1}, {b, "def"}, {c, 3.5}}')` → `{"a" : "1", "b" : "def", "c" : "3.5"}`

`json_object` ( `keys` `text[]`, `values` `text[]` ) → `json` `jsonb_object` ( `keys` `text[]`, `values` `text[]` ) → `jsonb` This form of `json_object` takes keys and values pairwise from separate text arrays. Otherwise it is identical to the one-argument form. `json_object('{a,b}', '{1,2}')` → `{"a": "1", "b": "2"}`
[a] For example, the hstore extension has a cast from `hstore` to `json`, so that `hstore` values converted via the JSON creation functions will be represented as JSON objects, not as primitive string values.

Table 9.48 shows the functions that are available for processing `json` and `jsonb` values.

Table 9.48. JSON Processing Functions

Function Description Example(s)

`json_array_elements` ( `json` ) → `setof json`

`jsonb_array_elements` ( `jsonb` ) → `setof jsonb`

Expands the top-level JSON array into a set of JSON values.

`select * from json_array_elements('[1,true, [2,false]]')` → ``

       value
    -----------
     1
     true
     [2,false]

`json_array_elements_text` ( `json` ) → `setof text`

`jsonb_array_elements_text` ( `jsonb` ) → `setof text`

Expands the top-level JSON array into a set of `text` values.

`select * from json_array_elements_text('["foo", "bar"]')` → ``

       value
    -----------
     foo
     bar

`json_array_length` ( `json` ) → `integer` `jsonb_array_length` ( `jsonb` ) → `integer` Returns the number of elements in the top-level JSON array. `json_array_length('[1,2,3,{"f1":1,"f2":[5,6]},4]')` → `5`

`json_each` ( `json` ) → `setof record` ( `key` `text`, `value` `json` )

`jsonb_each` ( `jsonb` ) → `setof record` ( `key` `text`, `value` `jsonb` )

Expands the top-level JSON object into a set of key/value pairs.

`select * from json_each('{"a":"foo", "b":"bar"}')` → ``

     key | value
    -----+-------
     a   | "foo"
     b   | "bar"

`json_each_text` ( `json` ) → `setof record` ( `key` `text`, `value` `text` )

`jsonb_each_text` ( `jsonb` ) → `setof record` ( `key` `text`, `value` `text` )

Expands the top-level JSON object into a set of key/value pairs. The returned `value`s will be of type `text`.

`select * from json_each_text('{"a":"foo", "b":"bar"}')` → ``

     key | value
    -----+-------
     a   | foo
     b   | bar

`json_extract_path` ( `from_json` `json`, `VARIADIC` `path_elems` `text[]` ) → `json`

`jsonb_extract_path` ( `from_json` `jsonb`, `VARIADIC` `path_elems` `text[]` ) → `jsonb`

Extracts JSON sub-object at the specified path. (This is functionally equivalent to the `#>` operator, but writing the path out as a variadic list can be more convenient in some cases.)

`json_extract_path('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}', 'f4', 'f6')` → `"foo"`

`json_extract_path_text` ( `from_json` `json`, `VARIADIC` `path_elems` `text[]` ) → `text` `jsonb_extract_path_text` ( `from_json` `jsonb`, `VARIADIC` `path_elems` `text[]` ) → `text` Extracts JSON sub-object at the specified path as `text`. (This is functionally equivalent to the `#>>` operator.) `json_extract_path_text('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}', 'f4', 'f6')` → `foo`

`json_object_keys` ( `json` ) → `setof text`

`jsonb_object_keys` ( `jsonb` ) → `setof text`

Returns the set of keys in the top-level JSON object.

`select * from json_object_keys('{"f1":"abc","f2":{"f3":"a", "f4":"b"}}')` → ``

     json_object_keys
    ------------------
     f1
     f2

`json_populate_record` ( `base` `anyelement`, `from_json` `json` ) → `anyelement`

`jsonb_populate_record` ( `base` `anyelement`, `from_json` `jsonb` ) → `anyelement`

Expands the top-level JSON object to a row having the composite type of the `base` argument. The JSON object is scanned for fields whose names match column names of the output row type, and their values are inserted into those columns of the output. (Fields that do not correspond to any output column name are ignored.) In typical use, the value of `base` is just `NULL`, which means that any output columns that do not match any object field will be filled with nulls. However, if `base` isn't `NULL` then the values it contains will be used for unmatched columns.

To convert a JSON value to the SQL type of an output column, the following rules are applied in sequence:

- A JSON null value is converted to an SQL null in all cases.

- If the output column is of type `json` or `jsonb`, the JSON value is just reproduced exactly.

- If the output column is a composite (row) type, and the JSON value is a JSON object, the fields of the object are converted to columns of the output row type by recursive application of these rules.

- Likewise, if the output column is an array type and the JSON value is a JSON array, the elements of the JSON array are converted to elements of the output array by recursive application of these rules.

- Otherwise, if the JSON value is a string, the contents of the string are fed to the input conversion function for the column's data type.

- Otherwise, the ordinary text representation of the JSON value is fed to the input conversion function for the column's data type.

While the example below uses a constant JSON value, typical use would be to reference a `json` or `jsonb` column laterally from another table in the query's `FROM` clause. Writing `json_populate_record` in the `FROM` clause is good practice, since all of the extracted columns are available for use without duplicate function calls.

`create type subrowtype as (d int, e text);` `create type myrowtype as (a int, b text[], c subrowtype);`

`select * from json_populate_record(null::myrowtype, '{"a": 1, "b": ["2", "a b"], "c": {"d": 4, "e": "a b c"}, "x": "foo"}')` → ``

     a |   b       |      c
    ---+-----------+-------------
     1 | {2,"a b"} | (4,"a b c")

`json_populate_recordset` ( `base` `anyelement`, `from_json` `json` ) → `setof anyelement`

`jsonb_populate_recordset` ( `base` `anyelement`, `from_json` `jsonb` ) → `setof anyelement`

Expands the top-level JSON array of objects to a set of rows having the composite type of the `base` argument. Each element of the JSON array is processed as described above for `json[b]_populate_record`.

`create type twoints as (a int, b int);`

`select * from json_populate_recordset(null::twoints, '[{"a":1,"b":2}, {"a":3,"b":4}]')` → ``

     a | b
    ---+---
     1 | 2
     3 | 4

`json_to_record` ( `json` ) → `record`

`jsonb_to_record` ( `jsonb` ) → `record`

Expands the top-level JSON object to a row having the composite type defined by an `AS` clause. (As with all functions returning `record`, the calling query must explicitly define the structure of the record with an `AS` clause.) The output record is filled from fields of the JSON object, in the same way as described above for `json[b]_populate_record`. Since there is no input record value, unmatched columns are always filled with nulls.

`create type myrowtype as (a int, b text);`

`select * from json_to_record('{"a":1,"b":[1,2,3],"c":[1,2,3],"e":"bar","r": {"a": 123, "b": "a b c"}}') as x(a int, b text, c int[], d text, r myrowtype)` → ``

     a |    b    |    c    | d |       r
    ---+---------+---------+---+---------------
     1 | [1,2,3] | {1,2,3} |   | (123,"a b c")

`json_to_recordset` ( `json` ) → `setof record`

`jsonb_to_recordset` ( `jsonb` ) → `setof record`

Expands the top-level JSON array of objects to a set of rows having the composite type defined by an `AS` clause. (As with all functions returning `record`, the calling query must explicitly define the structure of the record with an `AS` clause.) Each element of the JSON array is processed as described above for `json[b]_populate_record`.

`select * from json_to_recordset('[{"a":1,"b":"foo"}, {"a":"2","c":"bar"}]') as x(a int, b text)` → ``

     a |  b
    ---+-----
     1 | foo
     2 |

`jsonb_set` ( `target` `jsonb`, `path` `text[]`, `new_value` `jsonb` [, `create_if_missing` `boolean` ] ) → `jsonb`

Returns `target` with the item designated by `path` replaced by `new_value`, or with `new_value` added if `create_if_missing` is true (which is the default) and the item designated by `path` does not exist. All earlier steps in the path must exist, or the `target` is returned unchanged. As with the path oriented operators, negative integers that appear in the `path` count from the end of JSON arrays. If the last path step is an array index that is out of range, and `create_if_missing` is true, the new value is added at the beginning of the array if the index is negative, or at the end of the array if it is positive.

`jsonb_set('[{"f1":1,"f2":null},2,null,3]', '{0,f1}', '[2,3,4]', false)` → `[{"f1": [2, 3, 4], "f2": null}, 2, null, 3]`

`jsonb_set('[{"f1":1,"f2":null},2]', '{0,f3}', '[2,3,4]')` → `[{"f1": 1, "f2": null, "f3": [2, 3, 4]}, 2]`

`jsonb_set_lax` ( `target` `jsonb`, `path` `text[]`, `new_value` `jsonb` [, `create_if_missing` `boolean` [, `null_value_treatment` `text` ]] ) → `jsonb`

If `new_value` is not `NULL`, behaves identically to `jsonb_set`. Otherwise behaves according to the value of `null_value_treatment` which must be one of `'raise_exception'`, `'use_json_null'`, `'delete_key'`, or `'return_target'`. The default is `'use_json_null'`.

`jsonb_set_lax('[{"f1":1,"f2":null},2,null,3]', '{0,f1}', null)` → `[{"f1":null,"f2":null},2,null,3]`

`jsonb_set_lax('[{"f1":99,"f2":null},2]', '{0,f3}', null, true, 'return_target')` → `[{"f1": 99, "f2": null}, 2]`

`jsonb_insert` ( `target` `jsonb`, `path` `text[]`, `new_value` `jsonb` [, `insert_after` `boolean` ] ) → `jsonb`

Returns `target` with `new_value` inserted. If the item designated by the `path` is an array element, `new_value` will be inserted before that item if `insert_after` is false (which is the default), or after it if `insert_after` is true. If the item designated by the `path` is an object field, `new_value` will be inserted only if the object does not already contain that key. All earlier steps in the path must exist, or the `target` is returned unchanged. As with the path oriented operators, negative integers that appear in the `path` count from the end of JSON arrays. If the last path step is an array index that is out of range, the new value is added at the beginning of the array if the index is negative, or at the end of the array if it is positive.

`jsonb_insert('{"a": [0,1,2]}', '{a, 1}', '"new_value"')` → `{"a": [0, "new_value", 1, 2]}`

`jsonb_insert('{"a": [0,1,2]}', '{a, 1}', '"new_value"', true)` → `{"a": [0, 1, "new_value", 2]}`

`json_strip_nulls` ( `json` ) → `json` `jsonb_strip_nulls` ( `jsonb` ) → `jsonb` Deletes all object fields that have null values from the given JSON value, recursively. Null values that are not object fields are untouched. `json_strip_nulls('[{"f1":1, "f2":null}, 2, null, 3]')` → `[{"f1":1},2,null,3]`

`jsonb_path_exists` ( `target` `jsonb`, `path` `jsonpath` [, `vars` `jsonb` [, `silent` `boolean` ]] ) → `boolean`

Checks whether the JSON path returns any item for the specified JSON value. If the `vars` argument is specified, it must be a JSON object, and its fields provide named values to be substituted into the `jsonpath` expression. If the `silent` argument is specified and is `true`, the function suppresses the same errors as the `@?` and `@@` operators do.

`jsonb_path_exists('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2, "max":4}')` → `t`

`jsonb_path_match` ( `target` `jsonb`, `path` `jsonpath` [, `vars` `jsonb` [, `silent` `boolean` ]] ) → `boolean`

Returns the result of a JSON path predicate check for the specified JSON value. Only the first item of the result is taken into account. If the result is not Boolean, then `NULL` is returned. The optional `vars` and `silent` arguments act the same as for `jsonb_path_exists`.

`jsonb_path_match('{"a":[1,2,3,4,5]}', 'exists($.a[*] ? (@ >= $min && @ <= $max))', '{"min":2, "max":4}')` → `t`

`jsonb_path_query` ( `target` `jsonb`, `path` `jsonpath` [, `vars` `jsonb` [, `silent` `boolean` ]] ) → `setof jsonb`

Returns all JSON items returned by the JSON path for the specified JSON value. The optional `vars` and `silent` arguments act the same as for `jsonb_path_exists`.

`select * from jsonb_path_query('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2, "max":4}')` → ``

     jsonb_path_query
    ------------------
     2
     3
     4

`jsonb_path_query_array` ( `target` `jsonb`, `path` `jsonpath` [, `vars` `jsonb` [, `silent` `boolean` ]] ) → `jsonb`

Returns all JSON items returned by the JSON path for the specified JSON value, as a JSON array. The optional `vars` and `silent` arguments act the same as for `jsonb_path_exists`.

`jsonb_path_query_array('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2, "max":4}')` → `[2, 3, 4]`

`jsonb_path_query_first` ( `target` `jsonb`, `path` `jsonpath` [, `vars` `jsonb` [, `silent` `boolean` ]] ) → `jsonb`

Returns the first JSON item returned by the JSON path for the specified JSON value. Returns `NULL` if there are no results. The optional `vars` and `silent` arguments act the same as for `jsonb_path_exists`.

`jsonb_path_query_first('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2, "max":4}')` → `2`

`jsonb_path_exists_tz` ( `target` `jsonb`, `path` `jsonpath` [, `vars` `jsonb` [, `silent` `boolean` ]] ) → `boolean`

`jsonb_path_match_tz` ( `target` `jsonb`, `path` `jsonpath` [, `vars` `jsonb` [, `silent` `boolean` ]] ) → `boolean`

`jsonb_path_query_tz` ( `target` `jsonb`, `path` `jsonpath` [, `vars` `jsonb` [, `silent` `boolean` ]] ) → `setof jsonb`

`jsonb_path_query_array_tz` ( `target` `jsonb`, `path` `jsonpath` [, `vars` `jsonb` [, `silent` `boolean` ]] ) → `jsonb`

`jsonb_path_query_first_tz` ( `target` `jsonb`, `path` `jsonpath` [, `vars` `jsonb` [, `silent` `boolean` ]] ) → `jsonb`

These functions act like their counterparts described above without the `_tz` suffix, except that these functions support comparisons of date/time values that require timezone-aware conversions. The example below requires interpretation of the date-only value `2015-08-02` as a timestamp with time zone, so the result depends on the current TimeZone setting. Due to this dependency, these functions are marked as stable, which means these functions cannot be used in indexes. Their counterparts are immutable, and so can be used in indexes; but they will throw errors if asked to make such comparisons.

`jsonb_path_exists_tz('["2015-08-01 12:00:00 -05"]', '$[*] ? (@.datetime() < "2015-08-02".datetime())')` → `t`

`jsonb_pretty` ( `jsonb` ) → `text`

Converts the given JSON value to pretty-printed, indented text.

`jsonb_pretty('[{"f1":1,"f2":null}, 2]')` → ``

    [
        {
            "f1": 1,
            "f2": null
        },
        2
    ]

`json_typeof` ( `json` ) → `text`

`jsonb_typeof` ( `jsonb` ) → `text`

Returns the type of the top-level JSON value as a text string. Possible types are `object`, `array`, `string`, `number`, `boolean`, and `null`. (The `null` result should not be confused with an SQL NULL; see the examples.)

`json_typeof('-123.4')` → `number`

`json_typeof('null'::json)` → `null`

`json_typeof(NULL::json) IS NULL` → `t`

See also Section 9.21 for the aggregate function `json_agg` which aggregates record values as JSON, the aggregate function `json_object_agg` which aggregates pairs of values into a JSON object, and their `jsonb` equivalents, `jsonb_agg` and `jsonb_object_agg`.

### 9.16.2. The SQL/JSON Path Language

SQL/JSON path expressions specify the items to be retrieved from the JSON data, similar to XPath expressions used for SQL access to XML. In PostgreSQL, path expressions are implemented as the `jsonpath` data type and can use any elements described in Section 8.14.7.

JSON query functions and operators pass the provided path expression to the path engine for evaluation. If the expression matches the queried JSON data, the corresponding JSON item, or set of items, is returned. Path expressions are written in the SQL/JSON path language and can include arithmetic expressions and functions.

A path expression consists of a sequence of elements allowed by the `jsonpath` data type. The path expression is normally evaluated from left to right, but you can use parentheses to change the order of operations. If the evaluation is successful, a sequence of JSON items is produced, and the evaluation result is returned to the JSON query function that completes the specified computation.

To refer to the JSON value being queried (the context item), use the `$` variable in the path expression. It can be followed by one or more accessor operators, which go down the JSON structure level by level to retrieve sub-items of the context item. Each operator that follows deals with the result of the previous evaluation step.

For example, suppose you have some JSON data from a GPS tracker that you would like to parse, such as:

    {
      "track": {
        "segments": [
          {
            "location":   [ 47.763, 13.4034 ],
            "start time": "2018-10-14 10:05:14",
            "HR": 73
          },
          {
            "location":   [ 47.706, 13.2635 ],
            "start time": "2018-10-14 10:39:21",
            "HR": 135
          }
        ]
      }
    }

To retrieve the available track segments, you need to use the `.`key`` accessor operator to descend through surrounding JSON objects:

    $.track.segments

To retrieve the contents of an array, you typically use the `[*]` operator. For example, the following path will return the location coordinates for all the available track segments:

    $.track.segments[*].location

To return the coordinates of the first segment only, you can specify the corresponding subscript in the `[]` accessor operator. Recall that JSON array indexes are 0-relative:

    $.track.segments[0].location

The result of each path evaluation step can be processed by one or more `jsonpath` operators and methods listed in Section 9.16.2.2. Each method name must be preceded by a dot. For example, you can get the size of an array:

    $.track.segments.size()

More examples of using `jsonpath` operators and methods within path expressions appear below in Section 9.16.2.2.

When defining a path, you can also use one or more filter expressions that work similarly to the `WHERE` clause in SQL. A filter expression begins with a question mark and provides a condition in parentheses:

    ? (condition)

Filter expressions must be written just after the path evaluation step to which they should apply. The result of that step is filtered to include only those items that satisfy the provided condition. SQL/JSON defines three-valued logic, so the condition can be `true`, `false`, or `unknown`. The `unknown` value plays the same role as SQL `NULL` and can be tested for with the `is unknown` predicate. Further path evaluation steps use only those items for which the filter expression returned `true`.

The functions and operators that can be used in filter expressions are listed in Table 9.50. Within a filter expression, the `@` variable denotes the value being filtered (i.e., one result of the preceding path step). You can write accessor operators after `@` to retrieve component items.

For example, suppose you would like to retrieve all heart rate values higher than 130. You can achieve this using the following expression:

    $.track.segments[*].HR ? (@ > 130)

To get the start times of segments with such values, you have to filter out irrelevant segments before returning the start times, so the filter expression is applied to the previous step, and the path used in the condition is different:

    $.track.segments[*] ? (@.HR > 130)."start time"

You can use several filter expressions in sequence, if required. For example, the following expression selects start times of all segments that contain locations with relevant coordinates and high heart rate values:

    $.track.segments[*] ? (@.location[1] < 13.4) ? (@.HR > 130)."start time"

Using filter expressions at different nesting levels is also allowed. The following example first filters all segments by location, and then returns high heart rate values for these segments, if available:

    $.track.segments[*] ? (@.location[1] < 13.4).HR ? (@ > 130)

You can also nest filter expressions within each other:

    $.track ? (exists(@.segments[*] ? (@.HR > 130))).segments.size()

This expression returns the size of the track if it contains any segments with high heart rate values, or an empty sequence otherwise.

PostgreSQL's implementation of the SQL/JSON path language has the following deviations from the SQL/JSON standard:

- A path expression can be a Boolean predicate, although the SQL/JSON standard allows predicates only in filters. This is necessary for implementation of the `@@` operator. For example, the following `jsonpath` expression is valid in PostgreSQL:

      $.track.segments[*].HR < 70

- There are minor differences in the interpretation of regular expression patterns used in `like_regex` filters, as described in Section 9.16.2.3.

#### 9.16.2.1. Strict and Lax Modes

When you query JSON data, the path expression may not match the actual JSON data structure. An attempt to access a non-existent member of an object or element of an array results in a structural error. SQL/JSON path expressions have two modes of handling structural errors:

- lax (default) — the path engine implicitly adapts the queried data to the specified path. Any remaining structural errors are suppressed and converted to empty SQL/JSON sequences.

- strict — if a structural error occurs, an error is raised.

The lax mode facilitates matching of a JSON document structure and path expression if the JSON data does not conform to the expected schema. If an operand does not match the requirements of a particular operation, it can be automatically wrapped as an SQL/JSON array or unwrapped by converting its elements into an SQL/JSON sequence before performing this operation. Besides, comparison operators automatically unwrap their operands in the lax mode, so you can compare SQL/JSON arrays out-of-the-box. An array of size 1 is considered equal to its sole element. Automatic unwrapping is not performed only when:

- The path expression contains `type()` or `size()` methods that return the type and the number of elements in the array, respectively.

- The queried JSON data contain nested arrays. In this case, only the outermost array is unwrapped, while all the inner arrays remain unchanged. Thus, implicit unwrapping can only go one level down within each path evaluation step.

For example, when querying the GPS data listed above, you can abstract from the fact that it stores an array of segments when using the lax mode:

    lax $.track.segments.location

In the strict mode, the specified path must exactly match the structure of the queried JSON document to return an SQL/JSON item, so using this path expression will cause an error. To get the same result as in the lax mode, you have to explicitly unwrap the `segments` array:

    strict $.track.segments[*].location

The `.**` accessor can lead to surprising results when using the lax mode. For instance, the following query selects every `HR` value twice:

    lax $.**.HR

This happens because the `.**` accessor selects both the `segments` array and each of its elements, while the `.HR` accessor automatically unwraps arrays when using the lax mode. To avoid surprising results, we recommend using the `.**` accessor only in the strict mode. The following query selects each `HR` value just once:

    strict $.**.HR

#### 9.16.2.2. SQL/JSON Path Operators and Methods

Table 9.49 shows the operators and methods available in `jsonpath`. Note that while the unary operators and methods can be applied to multiple values resulting from a preceding path step, the binary operators (addition etc.) can only be applied to single values.

Table 9.49. `jsonpath` Operators and Methods

Operator/Method Description Example(s)
`number` `+` `number` → ``number`` Addition `jsonb_path_query('[2]', '$[0] + 3')` → `5`
`+` `number` → ``number`` Unary plus (no operation); unlike addition, this can iterate over multiple values `jsonb_path_query_array('{"x": [2,3,4]}', '+ $.x')` → `[2, 3, 4]`
`number` `-` `number` → ``number`` Subtraction `jsonb_path_query('[2]', '7 - $[0]')` → `5`
`-` `number` → ``number`` Negation; unlike subtraction, this can iterate over multiple values `jsonb_path_query_array('{"x": [2,3,4]}', '- $.x')` → `[-2, -3, -4]`
`number` `*` `number` → ``number`` Multiplication `jsonb_path_query('[4]', '2 * $[0]')` → `8`
`number` `/` `number` → ``number`` Division `jsonb_path_query('[8.5]', '$[0] / 2')` → `4.2500000000000000`
`number` `%` `number` → ``number`` Modulo (remainder) `jsonb_path_query('[32]', '$[0] % 10')` → `2`
`value` `.` `type()` → ``string`` Type of the JSON item (see `json_typeof`) `jsonb_path_query_array('[1, "2", {}]', '$[*].type()')` → `["number", "string", "object"]`
`value` `.` `size()` → ``number`` Size of the JSON item (number of array elements, or 1 if not an array) `jsonb_path_query('{"m": [11, 15]}', '$.m.size()')` → `2`
`value` `.` `double()` → ``number`` Approximate floating-point number converted from a JSON number or string `jsonb_path_query('{"len": "1.9"}', '$.len.double() * 2')` → `3.8`
`number` `.` `ceiling()` → ``number`` Nearest integer greater than or equal to the given number `jsonb_path_query('{"h": 1.3}', '$.h.ceiling()')` → `2`
`number` `.` `floor()` → ``number`` Nearest integer less than or equal to the given number `jsonb_path_query('{"h": 1.7}', '$.h.floor()')` → `1`
`number` `.` `abs()` → ``number`` Absolute value of the given number `jsonb_path_query('{"z": -0.3}', '$.z.abs()')` → `0.3`
`string` `.` `datetime()` → ``datetime_type`` (see note) Date/time value converted from a string `jsonb_path_query('["2015-8-1", "2015-08-12"]', '$[*] ? (@.datetime() < "2015-08-2".datetime())')` → `"2015-8-1"`
`string` `.` `datetime(`template`)` → ``datetime_type`` (see note) Date/time value converted from a string using the specified `to_timestamp` template `jsonb_path_query_array('["12:30", "18:40"]', '$[*].datetime("HH24:MI")')` → `["12:30:00", "18:40:00"]`
`object` `.` `keyvalue()` → ``array`` The object's key-value pairs, represented as an array of objects containing three fields: `"key"`, `"value"`, and `"id"`; `"id"` is a unique identifier of the object the key-value pair belongs to `jsonb_path_query_array('{"x": "20", "y": 32}', '$.keyvalue()')` → `[{"id": 0, "key": "x", "value": "20"}, {"id": 0, "key": "y", "value": 32}]`

### Note

The result type of the `datetime()` and `datetime(`template`)` methods can be `date`, `timetz`, `time`, `timestamptz`, or `timestamp`. Both methods determine their result type dynamically.

The `datetime()` method sequentially tries to match its input string to the ISO formats for `date`, `timetz`, `time`, `timestamptz`, and `timestamp`. It stops on the first matching format and emits the corresponding data type.

The `datetime(`template`)` method determines the result type according to the fields used in the provided template string.

The `datetime()` and `datetime(`template`)` methods use the same parsing rules as the `to_timestamp` SQL function does (see Section 9.8), with three exceptions. First, these methods don't allow unmatched template patterns. Second, only the following separators are allowed in the template string: minus sign, period, solidus (slash), comma, apostrophe, semicolon, colon and space. Third, separators in the template string must exactly match the input string.

If different date/time types need to be compared, an implicit cast is applied. A `date` value can be cast to `timestamp` or `timestamptz`, `timestamp` can be cast to `timestamptz`, and `time` to `timetz`. However, all but the first of these conversions depend on the current TimeZone setting, and thus can only be performed within timezone-aware `jsonpath` functions.

Table 9.50 shows the available filter expression elements.

Table 9.50. `jsonpath` Filter Expression Elements

Predicate/Value Description Example(s)
`value` `==` `value` → `boolean` Equality comparison (this, and the other comparison operators, work on all JSON scalar values) `jsonb_path_query_array('[1, "a", 1, 3]', '$[*] ? (@ == 1)')` → `[1, 1]` `jsonb_path_query_array('[1, "a", 1, 3]', '$[*] ? (@ == "a")')` → `["a"]`
`value` `!=` `value` → `boolean` `value` `<>` `value` → `boolean` Non-equality comparison `jsonb_path_query_array('[1, 2, 1, 3]', '$[*] ? (@ != 1)')` → `[2, 3]` `jsonb_path_query_array('["a", "b", "c"]', '$[*] ? (@ <> "b")')` → `["a", "c"]`
`value` `<` `value` → `boolean` Less-than comparison `jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ < 2)')` → `[1]`
`value` `<=` `value` → `boolean` Less-than-or-equal-to comparison `jsonb_path_query_array('["a", "b", "c"]', '$[*] ? (@ <= "b")')` → `["a", "b"]`
`value` `>` `value` → `boolean` Greater-than comparison `jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ > 2)')` → `[3]`
`value` `>=` `value` → `boolean` Greater-than-or-equal-to comparison `jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ >= 2)')` → `[2, 3]`
`true` → `boolean` JSON constant `true` `jsonb_path_query('[{"name": "John", "parent": false}, {"name": "Chris", "parent": true}]', '$[*] ? (@.parent == true)')` → `{"name": "Chris", "parent": true}`
`false` → `boolean` JSON constant `false` `jsonb_path_query('[{"name": "John", "parent": false}, {"name": "Chris", "parent": true}]', '$[*] ? (@.parent == false)')` → `{"name": "John", "parent": false}`
`null` → ``value`` JSON constant `null` (note that, unlike in SQL, comparison to `null` works normally) `jsonb_path_query('[{"name": "Mary", "job": null}, {"name": "Michael", "job": "driver"}]', '$[*] ? (@.job == null) .name')` → `"Mary"`
`boolean` `&&` `boolean` → `boolean` Boolean AND `jsonb_path_query('[1, 3, 7]', '$[*] ? (@ > 1 && @ < 5)')` → `3`
`boolean` `||` `boolean` → `boolean` Boolean OR `jsonb_path_query('[1, 3, 7]', '$[*] ? (@ < 1 || @ > 5)')` → `7`
`!` `boolean` → `boolean` Boolean NOT `jsonb_path_query('[1, 3, 7]', '$[*] ? (!(@ < 5))')` → `7`
`boolean` `is unknown` → `boolean` Tests whether a Boolean condition is `unknown`. `jsonb_path_query('[-1, 2, 7, "foo"]', '$[*] ? ((@ > 0) is unknown)')` → `"foo"`

`string` `like_regex` `string` [ `flag` `string` ] → `boolean`

Tests whether the first operand matches the regular expression given by the second operand, optionally with modifications described by a string of `flag` characters (see Section 9.16.2.3).

`jsonb_path_query_array('["abc", "abd", "aBdC", "abdacb", "babc"]', '$[*] ? (@ like_regex "^ab.*c")')` → `["abc", "abdacb"]`

`jsonb_path_query_array('["abc", "abd", "aBdC", "abdacb", "babc"]', '$[*] ? (@ like_regex "^ab.*c" flag "i")')` → `["abc", "aBdC", "abdacb"]`

`string` `starts with` `string` → `boolean` Tests whether the second operand is an initial substring of the first operand. `jsonb_path_query('["John Smith", "Mary Stone", "Bob Johnson"]', '$[*] ? (@ starts with "John")')` → `"John Smith"`

`exists` `(` `path_expression` `)` → `boolean`

Tests whether a path expression matches at least one SQL/JSON item. Returns `unknown` if the path expression would result in an error; the second example uses this to avoid a no-such-key error in strict mode.

`jsonb_path_query('{"x": [1, 2], "y": [2, 4]}', 'strict $.* ? (exists (@ ? (@[*] > 2)))')` → `[2, 4]`

`jsonb_path_query_array('{"value": 41}', 'strict $ ? (exists (@.name)) .name')` → `[]`

#### 9.16.2.3. SQL/JSON Regular Expressions

SQL/JSON path expressions allow matching text to a regular expression with the `like_regex` filter. For example, the following SQL/JSON path query would case-insensitively match all strings in an array that start with an English vowel:

    $[*] ? (@ like_regex "^[aeiou]" flag "i")

The optional `flag` string may include one or more of the characters `i` for case-insensitive match, `m` to allow `^` and `$` to match at newlines, `s` to allow `.` to match a newline, and `q` to quote the whole pattern (reducing the behavior to a simple substring match).

The SQL/JSON standard borrows its definition for regular expressions from the `LIKE_REGEX` operator, which in turn uses the XQuery standard. PostgreSQL does not currently support the `LIKE_REGEX` operator. Therefore, the `like_regex` filter is implemented using the POSIX regular expression engine described in Section 9.7.3. This leads to various minor discrepancies from standard SQL/JSON behavior, which are cataloged in Section 9.7.3.8. Note, however, that the flag-letter incompatibilities described there do not apply to SQL/JSON, as it translates the XQuery flag letters to match what the POSIX engine expects.

Keep in mind that the pattern argument of `like_regex` is a JSON path string literal, written according to the rules given in Section 8.14.7. This means in particular that any backslashes you want to use in the regular expression must be doubled. For example, to match string values of the root document that contain only digits:

    $.* ? (@ like_regex "^\\d+$")

### 9.16.3. SQL/JSON Functions and Expressions

To provide native support for JSON data types within the SQL environment, PostgreSQL implements the SQL/JSON data model. This model comprises sequences of items. Each item can hold SQL scalar values, with an additional SQL/JSON null value, and composite data structures that use JSON arrays and objects. The model is a formalization of the implied data model in the JSON specification RFC 7159.

SQL/JSON allows you to handle JSON data alongside regular SQL data, with transaction support, including:

- Uploading JSON data into the database and storing it in regular SQL columns as character or binary strings.

- Generating JSON objects and arrays from relational data.

- Querying JSON data using SQL/JSON query functions and SQL/JSON path language expressions.

All SQL/JSON functions fall into one of two groups. Constructor functions generate JSON data from values of SQL types. Query functions evaluate SQL/JSON path language expressions against JSON values and produce values of SQL/JSON types, which are converted to SQL types.

#### 9.16.3.1. Producing JSON Content

PostgreSQL provides several functions that generate JSON data. Taking values of SQL types as input, these functions construct JSON objects, JSON arrays or JSON scalars represented as the `json` or `jsonb` types, or as SQL character or binary strings.

- `JSON`

- `JSON_SCALAR`

- `JSON_OBJECT`

- `JSON_OBJECTAGG`

- `JSON_ARRAY`

- `JSON_ARRAYAGG`

##### 9.16.3.1.1. `JSON`

    JSON (
      expression [ FORMAT JSON [ ENCODING UTF8 ] ]
      [ { WITH | WITHOUT } UNIQUE [ KEYS ] ]
      [ RETURNING json_data_type ]
    )

###### 9.16.3.1.1.1. Description

The `JSON` function generates JSON from text data.

###### 9.16.3.1.1.2. Parameters

``expression` [ FORMAT JSON [ ENCODING UTF8 ] ]`

  The string `expression` provides the JSON text data. It can be any character string (`text`, `char`, etc.) or binary string (`bytea`) in UTF8 encoding. If the `expression` is NULL an SQL null value is returned.

  The optional `FORMAT` clause is provided to conform to the SQL/JSON standard.

`{ WITH | WITHOUT } UNIQUE [ KEYS ]`

  Defines whether duplicate keys are allowed:

  `WITHOUT`

    Default. The constructed JSON object can contain duplicate keys.

  `WITH`

    Duplicate keys are not allowed. If the input data contains duplicate keys, an error is returned.

  Optionally, you can add the `KEYS` keyword for semantic clarity.

`RETURNING `json_data_type``

  The output clause that specifies the type (`json` or `jsonb`) of the generated JSON. The default is `json`.

###### 9.16.3.1.1.3. Notes

Alternatively, you can construct JSON values simply using PostgreSQL-specific casts to `json` and `jsonb` types.

###### 9.16.3.1.1.4. Examples

Construct JSON using the provided strings:

    SELECT JSON('{ "a" : 123, "b": [ true, "foo" ], "a" : "bar" }');
                           json
    --------------------------------------------------
     { "a" : 123, "b": [ true, "foo" ], "a" : "bar" }
    (1 row)

    SELECT JSON('{"a": 123, "b": [true, "foo"], "a": "bar"}' RETURNING jsonb);
                   json
    ----------------------------------
     {"a": "bar", "b": [true, "foo"]}
    (1 row)

    SELECT JSON('{"a": 123, "b": [true, "foo"], "a": "bar"}' WITH UNIQUE KEYS);
    ERROR:  duplicate JSON object key value

##### 9.16.3.1.2. `JSON_SCALAR`

    JSON_SCALAR (
      expression
      [ RETURNING json_data_type ]
    )

###### 9.16.3.1.2.1. Description

The `JSON_SCALAR` function generates a JSON scalar value from SQL data.

###### 9.16.3.1.2.2. Parameters

``expression``

  The `expression` provides the data for constructing a JSON value. For null input, SQL null (not a JSON null) value is returned. For any scalar other than a number or a Boolean, the text representation will be used, with escaping as necessary to make it a valid JSON string value. For details, see `to_json()`/`to_jsonb()` in Table 9.47.

`RETURNING `json_data_type``

  The output clause that specifies the type (`json` or `jsonb`) of the generated JSON scalar. The default is `json`.

###### 9.16.3.1.2.3. Notes

Alternatively, you can construct JSON objects by using the PostgreSQL-specific `to_json()`/`to_jsonb()` functions. See Table 9.47 for details.

###### 9.16.3.1.2.4. Examples

Construct JSON scalars from the provided values of various types:

    SELECT JSON_SCALAR(123.45);
     json_scalar
    -------------
     123.45
    (1 row)

    SELECT JSON_SCALAR('123');
     json_scalar
    -------------
     "123"
    (1 row)

    SELECT JSON_SCALAR(true);
     json_scalar
    -------------
     true
    (1 row)

##### 9.16.3.1.3. `JSON_OBJECT`

    JSON_OBJECT (
      [ { key_expression { VALUE | ':' }
          value_expression [ FORMAT JSON [ ENCODING UTF8 ] ] }[, ...] ]
      [ { NULL | ABSENT } ON NULL ]
      [ { WITH | WITHOUT } UNIQUE [ KEYS ] ]
      [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ]
    )

###### 9.16.3.1.3.1. Description

The `JSON_OBJECT` function generates a JSON object from SQL or JSON data.

###### 9.16.3.1.3.2. Parameters

``key_expression` { VALUE | ':' } `value_expression` [ FORMAT JSON [ ENCODING UTF8 ] ]`

  The input clause that provides the data for constructing a JSON object:

  - `key_expression` is a scalar expression defining the JSON key, which is implicitly converted to the `text` type. The provided expression cannot be `NULL` or belong to a type that has a cast to `json`.

  - `value_expression` is an expression that provides the input for the JSON value.

  - The optional `FORMAT` clause is provided to conform to the SQL/JSON standard.

  You must use a colon or the `VALUE` keyword as a separator between the key and the value. Multiple key/value pairs are separated by commas.

`{ NULL | ABSENT } ON NULL`

  Defines whether `NULL` values are allowed in the constructed JSON object:

  `NULL`

    Default. `NULL` values are allowed.

  `ABSENT`

    If the value is `NULL`, the corresponding key/value pair is omitted from the generated JSON object.

`{ WITH | WITHOUT } UNIQUE [ KEYS ]`

  Defines whether duplicate keys are allowed:

  `WITHOUT`

    Default. The constructed JSON object can contain duplicate keys.

  `WITH`

    Duplicate keys are not allowed. If the input data contains duplicate keys, an error is returned. This check is performed before removing JSON items with NULL values.

  Optionally, you can add the `KEYS` keyword for semantic clarity.

`RETURNING `data_type` [ FORMAT JSON [ ENCODING UTF8 ] ]`

  The output clause that specifies the type of the generated JSON object. For details, see Section 9.16.3.4.2.

###### 9.16.3.1.3.3. Notes

Alternatively, you can construct JSON objects by using the PostgreSQL-specific `json_build_object()`/ `jsonb_build_object()` functions. See Table 9.47 for details.

###### 9.16.3.1.3.4. Examples

Construct a JSON object from the provided key/value pairs of various types:

    SELECT JSON_OBJECT(
    -- scalar JSON types
     'key1': 'string',
     'key2': '[1, 2]',
     'key3' VALUE 123, -- alternative syntax for key-value delimiter
     'key4': NULL,
    -- other types
     'key5': ARRAY[1, 2, 3], -- postgres array
     'key6': jsonb '{"a": ["b", 1]}', -- composite json/jsonb
     'key7': date '2017-09-30', -- datetime type
     'key8': row(1, 'a'), -- row type
     'key9': '[1, 2]' FORMAT JSON, -- same value as for key2, but with FORMAT
    -- key can be an expression
      'key' || 'last' : TRUE
    ABSENT ON NULL) AS json;
                           json
    ----------------------------------------------------
    {"key1" : "string", "key2" : "[1, 2]", "key3" : 123,
     "key5" : [1,2,3], "key6" : {"a": ["b", 1]},
     "key7" : "2017-09-30", "key8" : {"f1":1,"f2":"a"},
     "key9" : [1, 2], "keylast" : true}
    (1 row)

From the `films` table, select some data about the films distributed by Paramount Pictures (`did` = 103) and return JSON objects:

    SELECT
    JSON_OBJECT(
     'code' VALUE f.code,
     'title' VALUE f.title,
     'did' VALUE f.did
    ) AS paramount
    FROM films AS f
    WHERE f.did = 103;
                        paramount
    ----------------------------------------------------
    {"code" : "P_301", "title" : "Vertigo", "did" : 103}
    {"code" : "P_302", "title" : "Becket", "did" : 103}
    {"code" : "P_303", "title" : "48 Hrs", "did" : 103}
    (3 rows)

##### 9.16.3.1.4. `JSON_OBJECTAGG`

    JSON_OBJECTAGG (
      [ { key_expression { VALUE | ':' } value_expression } ]
      [ { NULL | ABSENT } ON NULL ]
      [ { WITH | WITHOUT } UNIQUE [ KEYS ] ]
      [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ]
    )

###### 9.16.3.1.4.1. Description

The `JSON_OBJECTAGG` function aggregates the provided data into a JSON object. You can use this function to combine values stored in different table columns into pairs.

###### 9.16.3.1.4.2. Parameters

``key_expression` { VALUE | ':' } `value_expression``

  The input clause that provides the data to be aggregated as a JSON object:

  - `key_expression` is a scalar expression defining the JSON key, which is implicitly converted to the `text` type. The provided expression cannot be `NULL` or belong to a type that has a cast to `json`.

  - `value_expression` is an expression that provides the input for the JSON value preceded by its type. For JSON scalar types, you can omit the type.

    ### Note

    The input value of the `bytea` type must be stored in `UTF8` and contain a valid `UTF8` string. Otherwise, an error occurs. PostgreSQL currently supports only `UTF8`.

  You must use a colon or the `VALUE` keyword as a separator between keys and values. Multiple key/value pairs are separated by commas.

`{ NULL | ABSENT } ON NULL`

  Defines whether `NULL` values are allowed in the constructed JSON object:

  `NULL`

    Default. `NULL` values are allowed.

  `ABSENT`

    If the value is `NULL`, the corresponding key/value pair is omitted from the generated JSON object.

`{ WITH | WITHOUT } UNIQUE [ KEYS ]`

  Defines whether duplicate keys are allowed:

  `WITHOUT`

    Default. The constructed JSON object can contain duplicate keys.

  `WITH`

    Duplicate keys are not allowed. If the input data contains duplicate keys, an error is returned. This check is performed before removing JSON items with NULL values.

  Optionally, you can add the `KEYS` keyword for semantic clarity.

`RETURNING `data_type` [ FORMAT JSON [ ENCODING UTF8 ] ]`

  The output clause that specifies the type of the generated JSON object. For details, see Section 9.16.3.4.2.

###### 9.16.3.1.4.3. Notes

Alternatively, you can create JSON objects by using PostgreSQL-specific `json_object_agg()`/ `jsonb_object_agg()` aggregate functions. See Section 9.21 for details.

###### 9.16.3.1.4.4. Examples

For films with `did` = 103, aggregate key/value pairs of film genre (`f.kind`) and title (`f.title`) into a single object:

    SELECT
    JSON_OBJECTAGG(
     f.kind VALUE f.title)
     AS films_list
    FROM films AS f
    where f.did = 103;
                     films_list
    ----------------------------------------------------
    { "Action" : "Vertigo", "Drama" : "Becket", "Action" : "48 Hrs" }

Return the same object as `jsonb`. Note that only a single film of the action genre is included as the `jsonb` type does not allow duplicate keys.

    SELECT
    JSON_OBJECTAGG(
      f.kind VALUE f.title
      RETURNING jsonb)
    AS films_list
    FROM films AS f
    where f.did = 103;
                     films_list
    ----------------------------------------------------
    {"Drama": "Becket", "Action": "48 Hrs"}

Return objects of film titles and length, grouped by the film genre:

    SELECT
      f.kind,
      JSON_OBJECTAGG(
        f.title VALUE f.len
    ) AS films_list
    FROM films AS f
    GROUP BY f.kind;

         kind    |        films_list
    -------------+----------------------------------
    Musical      | { "West Side Story" : "02:32:00", "The King and I" : "02:13:00", "Bed Knobs and Broomsticks" : "01:57:00" }
    Romantic     | { "The African Queen" : "01:43:00", "Une Femme est une Femme" : "01:25:00", "Storia di una donna" : "01:30:00" }
    Comedy       | { "Bananas" : "01:22:00", "There's a Girl in my Soup" : "01:36:00" }
    Drama        | { "The Third Man" : "01:44:00", "Becket" : "02:28:00", "War and Peace" : "05:57:00", "Yojimbo" : "01:50:00", "Das Boot" : "02:29:00" }
    Action       | { "Vertigo" : "02:08:00", "48 Hrs" : "01:37:00", "Taxi Driver" : "01:54:00", "Absence of Malice" : "01:55:00" }
    (5 rows)

##### 9.16.3.1.5. `JSON_ARRAY`

    JSON_ARRAY (
      [ { value_expression [ FORMAT JSON ] } [, ...] ]
      [ { NULL | ABSENT } ON NULL ]
      [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ]
    )

    JSON_ARRAY (
      [ query_expression ]
      [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ]
    )

###### 9.16.3.1.5.1. Description

The `JSON_ARRAY` function constructs a JSON array from the provided SQL or JSON data.

###### 9.16.3.1.5.2. Parameters

``value_expression``

  The input clause that provides the data for constructing a JSON array. The `value_expression` is an expression that provides the input for the JSON value preceded by its type. For JSON scalar types, you can omit the type.

  ### Note

  The input value of the `bytea` type must be stored in `UTF8` and contain a valid `UTF8` string. Otherwise, an error occurs. PostgreSQL currently supports only `UTF8`.

``query_expression``

  An SQL query that provides the data for constructing a JSON array. The query must return a single column that holds the values to be used in the array.

`{ NULL | ABSENT } ON NULL`

  Defines whether `NULL` values are allowed in the generated JSON array:

  `NULL`

    `NULL` values are allowed.

  `ABSENT`

    Default. If the value is `NULL`, the corresponding key/value pair is omitted from the generated JSON object.

  This clause is only supported for arrays built from an explicit list of values. If you are using an SQL query to generate an array, NULL values are always omitted.

`RETURNING `data_type` [ FORMAT JSON [ ENCODING UTF8 ] ]`

  The output clause that specifies the return type of the constructed JSON array. For details, see Section 9.16.3.4.2.

###### 9.16.3.1.5.3. Notes

Alternatively, you can create JSON arrays by using PostgreSQL-specific `json_build_array()`/ `jsonb_build_array()` functions. See Section 9.16 for details.

###### 9.16.3.1.5.4. Examples

From the `films` table, select some data about the films distributed by Paramount Pictures (`did` = 103) and return JSON arrays:

    SELECT
    JSON_ARRAY(
      f.code,
      f.title,
      f.did
    ) AS films
    FROM films AS f
    WHERE f.did = 103;
                           films
    ----------------------------------------------------
    ["P_301", "Vertigo", 103]
    ["P_302", "Becket", 103]
    ["P_303", "48 Hrs", 103]
    (3 rows)

Construct a JSON array from the list of film titles returned from the `films` table by a subquery:

    SELECT
    JSON_ARRAY(
      SELECT
      f.title
    FROM films AS f
    where f.did = 103)
    AS film_titles;
                        film_titles
    ----------------------------------------------------
    ["Vertigo", "Becket", "48 Hrs"]
    (1 row)

##### 9.16.3.1.6. `JSON_ARRAYAGG`

    JSON_ARRAYAGG (
      [ value_expression ]
      [ ORDER BY sort_expression ]
      [ { NULL | ABSENT } ON NULL ]
      [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ]
    )

###### 9.16.3.1.6.1. Description

The `JSON_ARRAYAGG` function aggregates the provided SQL or JSON data into a JSON array.

###### 9.16.3.1.6.2. Parameters

``value_expression``

  The input clause that provides the input data to be aggregated as a JSON array. The `value_expression` can be a value or a query returning the values to be used as input in array construction. You can provide multiple input values separated by commas.

`ORDER BY`

  Sorts the input data to be aggregated as a JSON array. For details on the exact syntax of the `ORDER BY` clause, see ORDER BY Clause.

`{ NULL | ABSENT } ON NULL`

  Defines whether `NULL` values are allowed in the constructed array:

  - `NULL` — `NULL` values are allowed.

  - `ABSENT` (default) — `NULL` values are omitted from the generated array.

`RETURNING `data_type` [ FORMAT JSON [ ENCODING UTF8 ] ]`

  The output clause that specifies the return type of the constructed JSON array. For details, see Section 9.16.3.4.2.

###### 9.16.3.1.6.3. Notes

Alternatively, you can create JSON arrays by using PostgreSQL-specific `json_agg()`/ `jsonb_agg()` functions. See Section 9.21 for details.

###### 9.16.3.1.6.4. Examples

Construct an array of film titles sorted in alphabetical order:

    SELECT
    JSON_ARRAYAGG(
      f.title
    ORDER BY f.title ASC) AS film_titles
    FROM films AS f;
                        film_titles
    ----------------------------------------------------
    ["48 Hrs", "Absence of Malice", "Bananas", "Becket", "Bed Knobs and Broomsticks", "Das Boot", "Storia di una donna", "Taxi Driver", "The African Queen", "The King and I", "There's a Girl in my Soup", "The Third Man", "Une Femme est une Femme", "Vertigo", "War and Peace", "West Side Story", "Yojimbo"]
    (1 row)

#### 9.16.3.2. Querying JSON

SQL/JSON query functions evaluate SQL/JSON path language expressions against JSON values, producing values of SQL/JSON types, which are converted to SQL types. All SQL/JSON query functions accept several common clauses described in Section 9.16.3.4. For details on the SQL/JSON path language, see Section 9.16.2.

- `IS JSON`

- `JSON_EXISTS`

- `JSON_VALUE`

- `JSON_QUERY`

- `JSON_TABLE`

In some usage examples for these functions, the following small table storing some JSON data will be used:

    CREATE TABLE my_films (
      js       text );

    INSERT INTO my_films VALUES (
    '{ "favorites" : [
       { "kind" : "comedy", "films" : [
         { "title" : "Bananas",
           "director" : "Woody Allen"},
         { "title" : "The Dinner Game",
           "director" : "Francis Veber" } ] },
       { "kind" : "horror", "films" : [
         { "title" : "Psycho",
           "director" : "Alfred Hitchcock" } ] },
       { "kind" : "thriller", "films" : [
         { "title" : "Vertigo",
           "director" : "Alfred Hitchcock" } ] },
       { "kind" : "drama", "films" : [
         { "title" : "Yojimbo",
           "director" : "Akira Kurosawa" } ] }
      ] }');

##### 9.16.3.2.1. `JSON_EXISTS`

    JSON_EXISTS (
      context_item, path_expression [ PASSING { value AS varname } [, ...]]
      [ RETURNING data_type ]
      [ { TRUE | FALSE | UNKNOWN | ERROR } ON ERROR ]
    )

###### 9.16.3.2.1.1. Description

The `JSON_EXISTS` function checks whether the provided JSON path expression can return any SQL/JSON items.

###### 9.16.3.2.1.2. Parameters

``context_item`, `path_expression` [ PASSING { `value` AS `varname` } [, ...]]`

  The input data to query, the JSON path expression defining the query, and an optional `PASSING` clause. See Section 9.16.3.4.1 for details.

`RETURNING `data_type``

  The output clause that specifies the data type of the returned value. The specified data type should have a cast from a `boolean` type, which is returned by default.

`{ TRUE | FALSE | UNKNOWN | ERROR } ON ERROR`

  Defines the return value if an error occurs. The default value is `FALSE`.

###### 9.16.3.2.1.3. Examples

Check whether the provided `jsonb` data contains a key/value pair with the `key1` key, and its value contains an array with one or more elements bigger than 2:

    SELECT JSON_EXISTS(jsonb '{"key1": [1,2,3]}', 'strict $.key1[*] ? (@ > 2)');
     json_exists
    -------------
     t
    (1 row)

Note the difference between strict and lax modes if the required item does not exist:

    -- Strict mode with ERROR on ERROR clause
    SELECT JSON_EXISTS(jsonb '{"a": [1,2,3]}', 'strict $.a[5]' ERROR ON ERROR);
    ERROR: Invalid SQL/JSON subscript
    (1 row)

    -- Lax mode
    SELECT JSON_EXISTS(jsonb '{"a": [1,2,3]}', 'lax $.a[5]' ERROR ON ERROR);
     json_exists
    -------------
     f
    (1 row)

    -- Strict mode using the default value for the ON ERROR clause
    SELECT JSON_EXISTS(jsonb '{"a": [1,2,3]}', 'strict $.a[5]');
     json_exists
    -------------
     f
    (1 row)

##### 9.16.3.2.2. `JSON_VALUE`

    JSON_VALUE (
      context_item, path_expression [ PASSING { value AS varname } [, ...]]
      [ RETURNING data_type ]
      [ { ERROR | NULL | DEFAULT expression } ON EMPTY ]
      [ { ERROR | NULL | DEFAULT expression } ON ERROR ]
    )

###### 9.16.3.2.2.1. Description

The `JSON_VALUE` function extracts a value from the provided JSON data and converts it to an SQL scalar. If the specified JSON path expression returns more than one SQL/JSON item, an error occurs. To extract an SQL/JSON array or object, use Section 9.16.3.2.3.

###### 9.16.3.2.2.2. Parameters

``context_item`, `path_expression` [ PASSING { `value` AS `varname` } [, ...]]`

  The input data to query, the JSON path expression defining the query, and an optional `PASSING` clause. For details, see Section 9.16.2.

`RETURNING `data_type``

  The output clause that specifies the data type of the returned value. Out of the box, PostgreSQL supports the following types: `json`, `jsonb`, `bytea`, and character string types (`text`, `char`, `varchar`, and `nchar`). The extracted value must be a single SQL/JSON scalar item and have a cast to the specified type. Otherwise, an error occurs. By default, `JSON_VALUE` returns a string of the `text` type.

`{ ERROR | NULL | DEFAULT `expression` } ON EMPTY`

  Defines the return value if no JSON value is found. The default is `NULL`. If you use `DEFAULT `expression``, the provided `expression` is evaluated and cast to the type specified in the `RETURNING` clause.

`{ ERROR | NULL | DEFAULT `expression` } ON ERROR`

  Defines the return value if an unhandled error occurs. The default is `NULL`. If you use `DEFAULT `expression``, the provided `expression` is evaluated and cast to the type specified in the `RETURNING` clause.

###### 9.16.3.2.2.3. Examples

Extract an SQL/JSON value and return it as an SQL scalar of the specified type. Note that `JSON_VALUE` can only return a single scalar, and the returned value must have a cast to the specified return type:

    SELECT JSON_VALUE('"123.45"', '$' RETURNING float);
     json_value
    ------------
         123.45
    (1 row)

    SELECT JSON_VALUE('123.45', '$' RETURNING int ERROR ON ERROR);
     json_value
    ------------
            123
    (1 row)

    SELECT JSON_VALUE('"03:04 2015-02-01"', '$.datetime("HH24:MI YYYY-MM-DD")' RETURNING date);
     json_value
    ------------
     2015-02-01
    (1 row)

    SELECT JSON_VALUE('"123.45"', '$' RETURNING int ERROR ON ERROR);
    ERROR:  invalid input syntax for integer: "123.45"

    SELECT JSON_VALUE(jsonb '[1]', 'strict $' ERROR ON ERROR);
    ERROR: SQL/JSON scalar required

    SELECT JSON_VALUE(jsonb '[1,2]', 'strict $[*]' ERROR ON ERROR);
    ERROR: more than one SQL/JSON item

If the path expression returns an array, an object, or multiple SQL/JSON items, an error is returned, as specified in the `ON ERROR` clause:

    SELECT JSON_VALUE(jsonb '[1]', 'strict $' ERROR ON ERROR);
    ERROR: SQL/JSON scalar required

    SELECT JSON_VALUE(jsonb '{"a": 1}', 'strict $' ERROR ON ERROR);
    ERROR: SQL/JSON scalar required

    SELECT JSON_VALUE(jsonb '[1,2]', 'strict $[*]' ERROR ON ERROR);
    ERROR: more than one SQL/JSON item

    SELECT JSON_VALUE(jsonb '[1,2]', 'strict $[*]' DEFAULT 1 ON ERROR);
    1

##### 9.16.3.2.3. `JSON_QUERY`

    JSON_QUERY (
      context_item, path_expression [ PASSING { value AS varname } [, ...]]
      [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ]
      [ { WITHOUT | WITH { CONDITIONAL | [UNCONDITIONAL] } } [ ARRAY ] WRAPPER ]
      [ { KEEP | OMIT } QUOTES [ ON SCALAR STRING ] ]
      [ { ERROR | NULL | EMPTY { [ ARRAY ] | OBJECT } | DEFAULT expression } ON EMPTY ]
      [ { ERROR | NULL | EMPTY { [ ARRAY ] | OBJECT } | DEFAULT expression } ON ERROR ]
    )

###### 9.16.3.2.3.1. Description

The `JSON_QUERY` function extracts an SQL/JSON array or object from JSON data. This function must return a JSON string, so if the path expression returns a scalar or multiple SQL/JSON items, you must wrap the result using the `WITH WRAPPER` clause. To extract a single SQL/JSON value, you can use Section 9.16.3.2.2.

###### 9.16.3.2.3.2. Parameters

``context_item`, `path_expression` [ PASSING { `value` AS `varname` } [, ...]]`

  The input data to query, the JSON path expression defining the query, and an optional `PASSING` clause. For details, see Section 9.16.2.

`RETURNING `data_type` [ FORMAT JSON [ ENCODING UTF8 ] ]`

  The output clause that specifies the data type of the returned value. For details, see Section 9.16.3.4.2.

`{ WITHOUT | WITH { CONDITIONAL | [UNCONDITIONAL] } } [ ARRAY ] WRAPPER`

  Defines whether to wrap a returned sequence of SQL/JSON items into an SQL/JSON array.

  `WITHOUT WRAPPER`

    Do not wrap the result. This is the default behavior if the `WRAPPER` clause is omitted.

  `WITH [UNCONDITIONAL] WRAPPER`

    Always wrap the result.

  `WITH CONDITIONAL WRAPPER`

    Wrap the result if the path expression returns anything other than a single SQL/JSON array or object.

  Optionally, you can add the `ARRAY` keyword for semantic clarity.

  ### Important

  You cannot use this clause together with the `ON EMPTY` clause.

`{ KEEP | OMIT } QUOTES [ ON SCALAR STRING ]`

  Defines whether to keep or omit quotes if a scalar string is returned. By default, scalar strings are returned with quotes. Using this clause together with the `WITH WRAPPER` clause is not allowed.

  Optionally, you can add the `ON SCALAR STRING` keywords for semantic clarity.

`{ ERROR | NULL | EMPTY { [ ARRAY ] | OBJECT } | DEFAULT `expression` } ON EMPTY`

  Defines the return value if no JSON value is found. The default is `NULL`. If you use `EMPTY [ARRAY]` or `EMPTY OBJECT`, an empty JSON array [] or object {} is returned, respectively. If you use `DEFAULT `expression``, the provided `expression` is evaluated and cast to the type specified in the `RETURNING` clause.

  You cannot use this clause together with the `WRAPPER` clause.

`{ ERROR | NULL | EMPTY { [ ARRAY ] | OBJECT } | DEFAULT `expression` } ON ERROR`

  Defines the return value if an unhandled error occurs. The default is `NULL`. If you use `EMPTY [ARRAY]` or `EMPTY OBJECT`, an empty JSON array `[]` or object `{}` are returned, respectively. If you use `DEFAULT `expression``, the provided `expression` is evaluated and cast to the type specified in the `RETURNING` clause.

###### 9.16.3.2.3.3. Examples

Extract all film genres listed in the `my_films` table:

    SELECT
        JSON_QUERY(js, '$.favorites[*].kind' WITH WRAPPER ERROR ON ERROR)
    FROM my_films;
     json_query
    ------------
     ["comedy", "horror", "thriller", "drama"]
    (1 row)

Note that the same query will result in an error if you omit the `WITH WRAPPER` clause, as it returns multiple SQL/JSON items:

    SELECT
        JSON_QUERY(js, '$.favorites[*].kind' ERROR ON ERROR)
    FROM my_films;
    ERROR: more than one SQL/JSON item

Compare the effect of different `WRAPPER` clauses:

    SELECT
        js,
        JSON_QUERY(js, 'lax $[*]') AS "without",
        JSON_QUERY(js, 'lax $[*]' WITH WRAPPER)  AS "with uncond",
        JSON_QUERY(js, 'lax $[*]' WITH CONDITIONAL WRAPPER) AS "with cond"
    FROM
        (VALUES (jsonb '[]'), ('[1]'), ('[[1,2,3]]'),  ('[{"a": 1}]'), ('[1, null, "2"]')) foo(js);
           js       |  without  |  with uncond   |   with cond
    ----------------+-----------+----------------+----------------
     []             | (null)    | (null)         | (null)
     [1]            | 1         | [1]            | [1]
     [[1, 2, 3]]    | [1, 2, 3] | [[1, 2, 3]]    | [1, 2, 3]
     [{"a": 1}]     | {"a": 1}  | [{"a": 1}]     | {"a": 1}
     [1, null, "2"] | (null)    | [1, null, "2"] | [1, null, "2"]
    (5 rows)

Compare quote handling for scalar types with and without the `OMIT QUOTES` clause:

    SELECT JSON_QUERY(jsonb '"aaa"', '$' RETURNING text);
     json_query
    ------------
     "aaa"
    (1 row)

    SELECT JSON_QUERY(jsonb '"aaa"', '$' RETURNING text OMIT QUOTES);
     json_query
    ------------
     aaa
    (1 row)

##### 9.16.3.2.4. `IS JSON`

    expression
      IS [ NOT ] JSON
      [ { VALUE | SCALAR | ARRAY | OBJECT } ]
      [ { WITH | WITHOUT } UNIQUE [ KEYS ] ]

###### 9.16.3.2.4.1. Description

The `IS JSON` predicate tests whether the provided value is valid JSON data. If you provide a specific JSON data type as a parameter, you can check whether the value belongs to this type. You can also use this predicate in the `IS NOT JSON` form. The return values are:

- `t` if the value satisfies the specified condition.

- `f` if the value does not satisfy the specified condition.

###### 9.16.3.2.4.2. Parameters

``expression``

  The input clause defining the value to test. You can provide the values of `json`, `jsonb`, `bytea`, or character string types.

`VALUE | SCALAR | ARRAY | OBJECT`

  Specifies the JSON data type to test for:

  - `VALUE` (default) — any JSON type.

  - `SCALAR` — JSON number, string, or boolean.

  - `ARRAY` — JSON array.

  - `OBJECT` — JSON object.

`{ WITH | WITHOUT } UNIQUE [ KEYS ]`

  Defines whether duplicate keys are allowed:

  - `WITHOUT` (default) — the JSON object can contain duplicate keys.

  - `WITH` — duplicate keys are not allowed. If the input data contains duplicate keys, it is considered to be invalid JSON.

  Optionally, you can add the `KEYS` keyword for semantic clarity.

###### 9.16.3.2.4.3. Examples

Compare the result returned by the `IS JSON` predicate for different data types:

    SELECT
        js,
        js IS JSON "is json",
        js IS NOT JSON "is not json",
        js IS JSON SCALAR "is scalar",
        js IS JSON OBJECT "is object",
        js IS JSON ARRAY "is array"
    FROM
        (VALUES ('123'), ('"abc"'), ('{"a": "b"}'), ('[1,2]'), ('abc')) foo(js);

         js     | is json | is not json | is scalar | is object | is array
    ------------+---------+-------------+-----------+-----------|-------------
     123        | t       | f           | t         | f         | f
     "abc"      | t       | f           | t         | f         | f
     {"a": "b"} | t       | f           | f         | t         | f
     [1,2]      | t       | f           | f         | f         | t
     abc        | f       | t           | f         | f         | f
    (5 rows)

##### 9.16.3.2.5. `JSON_TABLE`

    JSON_TABLE (
      context_item, path_expression [ AS json_path_name ] [ PASSING { value AS varname } [, ...] ]
      COLUMNS ( json_table_column [, ...] )
      [
        PLAN ( json_table_plan ) |
        PLAN DEFAULT ( { INNER | OUTER } [ , { CROSS | UNION } ]
                     | { CROSS | UNION } [ , { INNER | OUTER } ] )
      ]
    )

    where json_table_column is:

        name type [ PATH json_path_specification ]
            [ { WITHOUT | WITH { CONDITIONAL | [UNCONDITIONAL] } } [ ARRAY ] WRAPPER ]
            [ { KEEP | OMIT } QUOTES [ ON SCALAR STRING ] ]
            [ { ERROR | NULL | DEFAULT expression } ON EMPTY ]
            [ { ERROR | NULL | DEFAULT expression } ON ERROR ]
      | name type FORMAT json_representation
            [ PATH json_path_specification ]
            [ { WITHOUT | WITH { CONDITIONAL | [UNCONDITIONAL] } } [ ARRAY ] WRAPPER ]
            [ { KEEP | OMIT } QUOTES [ ON SCALAR STRING ] ]
            [ { ERROR | NULL | EMPTY { ARRAY | OBJECT } | DEFAULT expression } ON EMPTY ]
            [ { ERROR | NULL | EMPTY { ARRAY | OBJECT } | DEFAULT expression } ON ERROR ]
      | name type EXISTS [ PATH json_path_specification ]
            [ { ERROR | TRUE | FALSE | UNKNOWN } ON ERROR ]
      | NESTED PATH json_path_specification [ AS path_name ]
            COLUMNS ( json_table_column [, ...] )
      | name FOR ORDINALITY

    json_table_plan is:

        json_path_name [ { OUTER | INNER } json_table_plan_primary ]
      | json_table_plan_primary { UNION json_table_plan_primary } [...]
      | json_table_plan_primary { CROSS json_table_plan_primary } [...]

    json_table_plan_primary is:

        json_path_name | ( json_table_plan )

###### 9.16.3.2.5.1. Description

The `JSON_TABLE` function queries JSON data and presents the results as a relational view, which can be accessed as a regular SQL table. You can only use `JSON_TABLE` inside the `FROM` clause of the `SELECT` statement for an SQL table.

Taking JSON data as input, `JSON_TABLE` uses a path expression to extract a part of the provided data that will be used as a row pattern for the constructed view. Each SQL/JSON item at the top level of the row pattern serves as the source for a separate row in the constructed relational view.

To split the row pattern into columns, `JSON_TABLE` provides the `COLUMNS` clause that defines the schema of the created view. For each column to be constructed, this clause provides a separate path expression that evaluates the row pattern, extracts a JSON item, and returns it as a separate SQL value for the specified column. If the required value is stored in a nested level of the row pattern, it can be extracted using the `NESTED PATH` subclause. Joining the columns returned by `NESTED PATH` can add multiple new rows to the constructed view. Such rows are called child rows, as opposed to the parent row that generates them.

The rows produced by `JSON_TABLE` are laterally joined to the row that generated them, so you do not have to explicitly join the constructed view with the original table holding JSON data. Optionally, you can specify how to join the columns returned by `NESTED PATH` using the `PLAN` clause.

Each `NESTED PATH` clause can generate one or more columns, which are considered to be siblings to each other. In relation to the columns returned directly from the row expression or by the `NESTED PATH` clause of a higher level, these columns are child columns. Sibling columns are always joined first. Once they are processed, the resulting rows are joined to the parent row.

###### 9.16.3.2.5.2. Parameters

``context_item`, `path_expression` [ AS `json_path_name` ] [ PASSING { `value` AS `varname` } [, ...]]`

  The input data to query, the JSON path expression defining the query, and an optional `PASSING` clause, as described in Section 9.16.3.4.1. The result of the input data evaluation is called the row pattern. The row pattern is used as the source for row values in the constructed view.

`COLUMNS( `json_table_column` [, ...] )`

  The `COLUMNS` clause defining the schema of the constructed view. In this clause, you must specify all the columns to be filled with SQL/JSON items. The `json_table_column` expression has the following syntax variants:

  ``name` `type` [ PATH `json_path_specification` ]`

    Inserts a single SQL/JSON item into each row of the specified column.

    The provided `PATH` expression parses the row pattern defined by `json_api_common_syntax` and fills the column with produced SQL/JSON items, one for each row. If the `PATH` expression is omitted, `JSON_TABLE` uses the `$.`name`` path expression, where `name` is the provided column name. In this case, the column name must correspond to one of the keys within the SQL/JSON item produced by the row pattern.

    Internally, Section 9.16.3.2.2 and Section 9.16.3.2.3 are used to produce resulting values. Section 9.16.3.2.3 is used for JSON, array, and composite column types, Section 9.16.3.2.2 is used for other types.

    Optionally, you can add `ON EMPTY` and `ON ERROR` clauses to define how to handle missing values or structural errors. `WRAPPER` and `QUOTES` clauses can only be used with JSON, array, and composite types. These clauses have the same syntax and semantics as in Section 9.16.3.2.2 and Section 9.16.3.2.3.

  ``name` `type` FORMAT `json_representation` [ PATH `json_path_specification` ]`

    Generates a column and inserts a composite SQL/JSON item into each row of this column.

    The provided `PATH` expression parses the row pattern defined by `json_api_common_syntax` and fills the column with produced SQL/JSON items, one for each row. If the `PATH` expression is omitted, `JSON_TABLE` uses the `$.`name`` path expression, where `name` is the provided column name. In this case, the column name must correspond to one of the keys within the SQL/JSON item produced by the row pattern.

    Internally, Section 9.16.3.2.3 is used to produce resulting values.

    Optionally, you can add `WRAPPER`, `QUOTES`, `ON EMPTY` and `ON ERROR` clauses to define additional settings for the returned SQL/JSON items. These clauses have the same syntax and semantics as in Section 9.16.3.2.3.

  ``name` `type` EXISTS [ PATH `json_path_specification` ]`

    Generates a column and inserts a boolean item into each row of this column.

    The provided `PATH` expression parses the row pattern defined by `json_api_common_syntax`, checks whether any SQL/JSON items were returned, and fills the column with resulting boolean value, one for each row. The specified `type` should have cast from `boolean`. If the `PATH` expression is omitted, `JSON_TABLE` uses the `$.`name`` path expression, where `name` is the provided column name.

    Optionally, you can add `ON ERROR` clause to define error behavior. This clause have the same syntax and semantics as in Section 9.16.3.2.1.

  `NESTED PATH `json_path_specification` [ AS `json_path_name` ] COLUMNS ( `json_table_column` [, ...] )`

    Extracts SQL/JSON items from nested levels of the row pattern, generates one or more columns as defined by the `COLUMNS` subclause, and inserts the extracted SQL/JSON items into each row of these columns. The `json_table_column` expression in the `COLUMNS` subclause uses the same syntax as in the parent `COLUMNS` clause.

    The `NESTED PATH` syntax is recursive, so you can go down multiple nested levels by specifying several `NESTED PATH` subclauses within each other. It allows to unnest the hierarchy of JSON objects and arrays in a single function invocation rather than chaining several `JSON_TABLE` expressions in an SQL statement.

    You can use the `PLAN` clause to define how to join the columns returned by `NESTED PATH` clauses.

  ``name` FOR ORDINALITY`

    Adds an ordinality column that provides sequential row numbering. You can have only one ordinality column per table. Row numbering is 1-based. For child rows that result from the `NESTED PATH` clauses, the parent row number is repeated.

`AS `json_path_name``

  The optional `json_path_name` serves as an identifier of the provided `json_path_specification`. The path name must be unique and distinct from the column names. When using the `PLAN` clause, you must specify the names for all the paths, including the row pattern. Each path name can appear in the `PLAN` clause only once.

`PLAN ( `json_table_plan` )`

  Defines how to join the data returned by `NESTED PATH` clauses to the constructed view.

  To join columns with parent/child relationship, you can use:

  `INNER`

    Use `INNER JOIN`, so that the parent row is omitted from the output if it does not have any child rows after joining the data returned by `NESTED PATH`.

  `OUTER`

    Use `LEFT OUTER JOIN`, so that the parent row is always included into the output even if it does not have any child rows after joining the data returned by `NESTED PATH`, with NULL values inserted into the child columns if the corresponding values are missing.

    This is the default option for joining columns with parent/child relationship.

  To join sibling columns, you can use:

  `UNION`

    Use `FULL OUTER JOIN ON FALSE`, so that both parent and child rows are included into the output, with NULL values inserted into both child and parent columns for all missing values.

    This is the default option for joining sibling columns.

  `CROSS`

    Use `CROSS JOIN`, so that the output includes a row for every possible combination of rows from the left-hand and the right-hand columns.

`PLAN DEFAULT ( `option` [, ... ] )`

  Overrides the default joining plans. The `INNER` and `OUTER` options define the joining plan for parent/child columns, while `UNION` and `CROSS` affect the sibling columns. You can override the default plans for all columns at once. Even though the path names are not included into the `PLAN DEFAULT` clause, they must be provided for all the paths to conform to the SQL/JSON standard.

###### 9.16.3.2.5.3. Examples

Query the `my_films` table holding some JSON data about the films and create a view that distributes the film genre, title, and director between separate columns:

    SELECT jt.* FROM
     my_films,
     JSON_TABLE ( js, '$.favorites[*]' COLUMNS (
       id FOR ORDINALITY,
       kind text PATH '$.kind',
       NESTED PATH '$.films[*]' COLUMNS (
         title text PATH '$.title',
         director text PATH '$.director'))) AS jt;
    ----+----------+------------------+-------------------
     id |   kind   |       title      |    director
    ----+----------+------------------+-------------------
     1  | comedy   | Bananas          | Woody Allen
     1  | comedy   | The Dinner Game  | Francis Veber
     2  | horror   | Psycho           | Alfred Hitchcock
     3  | thriller | Vertigo          | Hitchcock
     4  | drama    | Yojimbo          | Akira Kurosawa
     (5 rows)

Find a director that has done films in two different genres:

    SELECT
      director1 AS director, title1, kind1, title2, kind2
    FROM
      my_films,
      JSON_TABLE ( js, '$.favorites' AS favs COLUMNS (
        NESTED PATH '$[*]' AS films1 COLUMNS (
          kind1 text PATH '$.kind',
          NESTED PATH '$.films[*]' AS film1 COLUMNS (
            title1 text PATH '$.title',
            director1 text PATH '$.director')
        ),
        NESTED PATH '$[*]' AS films2 COLUMNS (
          kind2 text PATH '$.kind',
          NESTED PATH '$.films[*]' AS film2 COLUMNS (
            title2 text PATH '$.title',
            director2 text PATH '$.director'
          )
        )
       )
       PLAN (favs OUTER ((films1 INNER film1) CROSS (films2 INNER film2)))
      ) AS jt
     WHERE kind1 > kind2 AND director1 = director2;

#### 9.16.3.3. Serializing JSON data

- `JSON_SERIALIZE`

##### 9.16.3.3.1. `JSON_SERIALAIZE`

    JSON_SERIALIZE (
      expression [ FORMAT JSON [ ENCODING UTF8 ] ]
      [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ]
    )

###### 9.16.3.3.1.1. Description

The `JSON_SERIALIZE` function transforms an SQL/JSON value into a character or binary string.

###### 9.16.3.3.1.2. Parameters

``expression` [ FORMAT JSON [ ENCODING UTF8 ] ]`

  JSON typed expression that provides a data for serialization. Accepted JSON types (`json` and `jsonb`), any character string types (`text`, `char`, etc.), binary strings (`bytea`) in UTF8 encoding. For null input, null value is returned.

  The optional `FORMAT` clause is provided to conform to the SQL/JSON standard.

`RETURNING `data_type` [ FORMAT JSON [ ENCODING UTF8 ] ]`

  The output clause that specifies the target character or binary string type (`text`, `char`, `bytea`, etc.).

###### 9.16.3.3.1.3. Notes

Alternatively, you can construct JSON values simply using PostgreSQL-specific casts to `json` and `jsonb` types.

###### 9.16.3.3.1.4. Examples

Construct serialized JSON using the provided strings:

    SELECT JSON_SERIALIZE(JSON_SCALAR('foo'));
     json_serialize
    ----------------
     "foo"
    (1 row)

    SELECT JSON_SERIALIZE('{"foo": "bar", "baz": [1, 2]}' RETURNING bytea);
                            json_serialize
    --------------------------------------------------------------
     \x7b22666f6f223a2022626172222c202262617a223a205b312c20325d7d
    (1 row)

#### 9.16.3.4. SQL/JSON Common Clauses

##### 9.16.3.4.1. SQL/JSON Input Clause

``context_item`, `path_expression` [ PASSING { `value` AS `varname` } [, ...]]`

  The input clause specifies the JSON data to query and the exact query path to be passed to SQL/JSON query functions:

  - The `context_item` is the JSON data to query.

    ### Note

    Currently for functions `JSON_VALUE`, `JSON_EXISTS`, and `JSON_QUERY` this must be a value of type `jsonb`.

  - The `path_expression` is an SQL/JSON path expression that specifies the items to be retrieved from the JSON data. For details on path expression syntax, see Section 9.16.2.

  - The optional `PASSING` clause provides the values for the named variables used in the SQL/JSON path expression.

  The input clause is common for all SQL/JSON query functions.

##### 9.16.3.4.2. SQL/JSON Output Clause

`RETURNING `data_type` [ FORMAT JSON [ ENCODING UTF8 ] ]`

  The output clause that specifies the return type of the generated JSON object. Out of the box, PostgreSQL supports the following types: `json`, `jsonb`, `bytea`, and character string types (`text`, `char`, `varchar`, and `nchar`). To use another type, you must create a cast from `json` to that type. By default, the `json` type is returned.

  The optional `FORMAT` clause is provided to conform to the SQL/JSON standard.

  The output clause is common for both constructor and query SQL/JSON functions.

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