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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 -> integerjson

jsonb -> integerjsonb

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 -> textjson

jsonb -> textjsonb

Extracts JSON object field with the given key.

'{"a": {"b":"foo"}}'::json -> 'a'{"b":"foo"}

json ->> integertext

jsonb ->> integertext

Extracts n'th element of JSON array, as text.

'[1,2,3]'::json ->> 23

json ->> texttext

jsonb ->> texttext

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 @> jsonbboolean

Does the first JSON value contain the second? (See Section 8.14.3 for details about containment.)

'{"a":1, "b":2}'::jsonb @> '{"b":2}'::jsonbt

jsonb <@ jsonbboolean

Is the first JSON value contained in the second?

'{"b":2}'::jsonb <@ '{"a":1, "b":2}'::jsonbt

jsonb ? textboolean

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 || jsonbjsonb

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 - textjsonb

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 - integerjsonb

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 @? jsonpathboolean

Does JSON path return any item for the specified JSON value?

'{"a":[1,2,3,4,5]}'::jsonb @? '$.a[*] ? (@ > 2)'t

jsonb @@ jsonpathboolean

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

jsonb_array_length('[]')0

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 values 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 NULLt


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 + numbernumber

Addition

jsonb_path_query('[2]', '$[0] + 3')5

+ numbernumber

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 - numbernumber

Subtraction

jsonb_path_query('[2]', '7 - $[0]')5

- numbernumber

Negation; unlike subtraction, this can iterate over multiple values

jsonb_path_query_array('{"x": [2,3,4]}', '- $.x')[-2, -3, -4]

number * numbernumber

Multiplication

jsonb_path_query('[4]', '2 * $[0]')8

number / numbernumber

Division

jsonb_path_query('[8.5]', '$[0] / 2')4.2500000000000000

number % numbernumber

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 == valueboolean

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 != valueboolean

value <> valueboolean

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 < valueboolean

Less-than comparison

jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ < 2)')[1]

value <= valueboolean

Less-than-or-equal-to comparison

jsonb_path_query_array('["a", "b", "c"]', '$[*] ? (@ <= "b")')["a", "b"]

value > valueboolean

Greater-than comparison

jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ > 2)')[3]

value >= valueboolean

Greater-than-or-equal-to comparison

jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ >= 2)')[2, 3]

trueboolean

JSON constant true

jsonb_path_query('[{"name": "John", "parent": false}, {"name": "Chris", "parent": true}]', '$[*] ? (@.parent == true)'){"name": "Chris", "parent": true}

falseboolean

JSON constant false

jsonb_path_query('[{"name": "John", "parent": false}, {"name": "Chris", "parent": true}]', '$[*] ? (@.parent == false)'){"name": "John", "parent": false}

nullvalue

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 && booleanboolean

Boolean AND

jsonb_path_query('[1, 3, 7]', '$[*] ? (@ > 1 && @ < 5)')3

boolean || booleanboolean

Boolean OR

jsonb_path_query('[1, 3, 7]', '$[*] ? (@ < 1 || @ > 5)')7

! booleanboolean

Boolean NOT

jsonb_path_query('[1, 3, 7]', '$[*] ? (!(@ < 5))')7

boolean is unknownboolean

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 stringboolean

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.

There are two groups of SQL/JSON functions. 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.

Many SQL/JSON functions have an optional FORMAT clause. This is provided to conform with the SQL standard, but has no effect except where noted otherwise.

Table 9.51 lists the SQL/JSON Constructor functions. Each function has a RETURNING clause specifying the data type returned. For the json and json_scalar functions, this needs to be either json or jsonb. For the other constructor functions it must be one of json, jsonb, bytea, a character string type (text, char, varchar, or nchar), or a type for which there is a cast from json to that type. By default, the json type is returned.

Note

Many of the results that can be obtained from the SQL/JSON Constructor functions can also be obtained by calling PostgreSQL-specific functions detailed in Table 9.47 and Table 9.61.

Table 9.51. SQL/JSON Constructor Functions

Function signature

Description

Example(s)

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

The expression can be any text type or a bytea in UTF8 encoding. If the expression is NULL, an SQL null value is returned. If WITH UNIQUE is specified, the expression must not contain any duplicate object keys.

json('{"a":123, "b":[true,"foo"], "a":"bar"}'){"a":123, "b":[true,"foo"], "a":"bar"}

json('{"a":123,"b":[true,"foo"],"a":"bar"}' returning jsonb){"a": "bar", "b": [true, "foo"]}

json_scalar (expression [ RETURNING json_data_type ])

Returns a JSON scalar value representing expression. If the input is NULL, an SQL NULL is returned. If the input is a number or a boolean value, a corresponding JSON number or boolean value is returned. For any other value a JSON string is returned.

json_scalar(123.45)123.45

json_scalar(CURRENT_TIMESTAMP)"2022-05-10T10:51:04.62128-04:00"

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 ] ] ])

Constructs a JSON object of all the key value pairs given, or an empty object if none are given. key_expression is a scalar expression defining the JSON key, which is converted to the text type. It cannot be NULL nor can it belong to a type that has a cast to the json. If WITH UNIQUE is specified, there must not be any duplicate key_expression. If ABSENT ON NULL is specified, the entire pair is omitted if the value_expression is NULL.

json_object('code' VALUE 'P123', 'title': 'Jaws'){"code" : "P123", "title" : "Jaws"}

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

Behaves like json_object above, but as an aggregate function, so it only takes one key_expression and one value_expression parameter.

SELECT json_objectagg(k:v) FROM (VALUES ('a'::text,current_date),('b',current_date + 1)) AS t(k,v){ "a" : "2022-05-10", "b" : "2022-05-11" }

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 ] ] ])

Constructs a JSON array from either a series of value_expression parameters or from the results of query_expression, which must be a SELECT query returning a single column. If ABSENT ON NULL is specified, NULL values are ignored. This is always the case if a query_expression is used.

json_array(1,true,json '{"a":null}')[1, true, {"a":null}]

json_array(SELECT * FROM (VALUES(1),(2)) t)[1, 2]

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

Behaves in the same way as json_array but as an aggregate function so it only takes one value_expression parameter. If ABSENT ON NULL is specified, any NULL values are omitted. If ORDER BY is specified, the elements will appear in the array in that order rather than in the input order.

SELECT json_arrayagg(v) FROM (VALUES(2),(1)) t(v)[2, 1]


Table 9.52 details SQL/JSON facilities for testing and serializing JSON.

Table 9.52. SQL/JSON Testing and Serializing Functions

Function signature

Description

Example(s)

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

This predicate tests whether expression can be parsed as JSON, possibly of a specified type. If SCALAR or ARRAY or OBJECT is specified, the test is whether or not the JSON is of that particular type. If WITH UNIQUE is specified, then an any object in the expression is also tested to see if it has duplicate keys.

SELECT js,
  js IS JSON "json?",
  js IS JSON SCALAR "scalar?",
  js IS JSON OBJECT "object?",
  js IS JSON ARRAY "array?"
FROM
(VALUES ('123'), ('"abc"'), ('{"a": "b"}'),
('[1,2]'),('abc')) foo(js);
     js     | json? | scalar? | object? | array?
------------+-------+---------+---------+--------
 123        | t     | t       | f       | f
 "abc"      | t     | t       | f       | f
 {"a": "b"} | t     | f       | t       | f
 [1,2]      | t     | f       | f       | t
 abc        | f     | f       | f       | f

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

Transforms an SQL/JSON value into a character or binary string. The expression can be of any JSON type, any character string type, or bytea in UTF8 encoding. The returned type can be any character string type or bytea. The default is text.

json_serialize('{ "a" : 1 } ' RETURNING bytea)\x7b20226122203a2031207d20


Table 9.53 details the SQL/JSON functions that can be used to query JSON data, except for json_table.

Note

SQL/JSON paths can only be applied to the jsonb type, so it might be necessary to cast the context_item argument of these functions to jsonb.

Table 9.53. SQL/JSON Query Functions

Function signature

Description

Example(s)

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

Returns true if the SQL/JSON path_expression applied to the context_item using the values yields any items. The ON ERROR clause specifies what is returned if an error occurs. Note that if the path_expression is strict, an error is generated if it yields no items. The default value is UNKNOWN which causes a NULL result.

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

json_exists(jsonb '{"a": [1,2,3]}', 'lax $.a[5]' ERROR ON ERROR)f

json_exists(jsonb '{"a": [1,2,3]}', 'strict $.a[5]' ERROR ON ERROR)ERROR: jsonpath array subscript is out of bounds

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 ])

Returns the result of applying the path_expression to the context_item using the values. The extracted value must be a single SQL/JSON scalar item. For results that are objects or arrays, use the json_query instead. The returned data_type has the same semantics as for constructor functions like json_objectagg. The default returned type is text. The ON EMPTY clause specifies the behavior if the path_expression yields no value at all. The ON ERROR clause specifies the behavior if an error occurs, as a result of either the evaluation or the application of the ON EMPTY clause.

json_value(jsonb '"123.45"', '$' RETURNING float)123.45

json_value(jsonb '"03:04 2015-02-01"', '$.datetime("HH24:MI YYYY-MM-DD")' RETURNING date)2015-02-01

json_value(jsonb '[1,2]', 'strict $[*]' DEFAULT 9 ON ERROR)9

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 ])

Returns the result of applying the path_expression to the context_item using the values. This function must return a JSON string, so if the path expression returns multiple SQL/JSON items, you must wrap the result using the WITH WRAPPER clause. If the wrapper is UNCONDITIONAL, an array wrapper will always be applied, even if the returned value is already a single JSON object or array, but if it is CONDITIONAL it will not be applied to a single array or object. UNCONDITIONAL is the default. If the result is a a scalar string, by default the value returned will have surrounding quotes making it a valid JSON value. However, this behavior is reversed if OMIT QUOTES is specified. The ON ERROR and ON EMPTY clauses have similar semantics to those clauses for json_value. The returned data_type has the same semantics as for constructor functions like json_objectagg. The default returned type is text.

json_query(jsonb '[1,[2,3],null]', 'lax $[*][1]' WITH CONDITIONAL WRAPPER)[3]


9.16.4. JSON_TABLE

json_table is an SQL/JSON function which 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 a SELECT statement.

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. Columns produced by NESTED PATHs at the same level are considered to be siblings, while a column produced by a NESTED PATH is considered to be a child of the column produced by and NESTED PATH or row expression at a higher level. Sibling columns are always joined first. Once they are processed, the resulting rows are joined to the parent row.

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, which can provide data values to the path_expression. 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.

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 for json_value and json_query.

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.

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 for json_query.

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 has the same syntax and semantics as for json_exists.

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

Generate one row for each value produced by each of the sibling columns. The columns from the other siblings are set to null.

This is the default option for joining sibling columns.

CROSS

Generate one row for each combination of values from the sibling columns.

PLAN DEFAULT ( OUTER | INNER [, UNION | CROSS ] )

The terms can also be specified in reverse order. The INNER or OUTER option defines the joining plan for parent/child columns, while UNION or CROSS affects joins of sibling columns. This form of PLAN overrides the default plan for all columns at once. Even though the path names are not included in the PLAN DEFAULT form, to conform to the SQL/JSON standard they must be provided for all the paths if the PLAN clause is used.

PLAN DEFAULT is simpler than specifying a complete PLAN, and is often all that is required to get the desired output.

Examples

In these examples the following small table storing some JSON data will be used:

CREATE TABLE my_films ( js jsonb );

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" } ] }
  ] }');

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          | Alfred 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;

     director     | title1  |  kind1   | title2 | kind2
------------------+---------+----------+--------+--------
 Alfred Hitchcock | Vertigo | thriller | Psycho | horror
(1 row)