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9.16. JSON Functions and Operators #

   9.16.1. Processing and Creating JSON Data
   9.16.2. The SQL/JSON Path Language
   9.16.3. SQL/JSON Query Functions
   9.16.4. JSON_TABLE

   This section describes:
     * functions and operators for processing and creating JSON data
     * the SQL/JSON path language
     * the SQL/JSON query functions

   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.

   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 9.47 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. 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.

   Table 9.47. 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 9.48.
   Section 8.14.4 describes how these operators can be used to effectively
   search indexed jsonb data.

   Table 9.48. 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? (This is
   useful only with SQL-standard JSON path expressions, not predicate
   check expressions, since those always return a 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. (This is useful only with predicate check expressions, not
   SQL-standard JSON path expressions, since it will return NULL if the
   path result is not a single boolean value.)

   '{"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 9.49 shows the functions that are available for constructing json
   and jsonb values. Some functions in this table have a RETURNING clause,
   which specifies the data type returned. It must be one of json, jsonb,
   bytea, a character string type (text, char, or varchar), or a type that
   can be cast to json. By default, the json type is returned.

   Table 9.49. 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]]

   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]

   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 ( [ { 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 type.
   If WITH UNIQUE KEYS is specified, there must not be any duplicate
   key_expression. Any pair for which the value_expression evaluates to
   NULL is omitted from the output if ABSENT ON NULL is specified; if NULL
   ON NULL is specified or the clause omitted, the key is included with
   value NULL.

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

   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"}

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

   Converts a given expression specified as text or bytea string (in UTF8
   encoding) into a JSON value. If 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_scalar ( expression )

   Converts a given SQL scalar value into a JSON scalar value. If the
   input is NULL, an SQL null is returned. If the input is 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_serialize ( expression [ FORMAT JSON [ ENCODING UTF8 ] ] [
   RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ] )

   Converts an SQL/JSON expression 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 used in RETURNING can be any
   character string type or bytea. The default is text.

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

   ^[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 9.50 details SQL/JSON facilities for testing JSON.

   Table 9.50. SQL/JSON Testing 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 KEYS is specified, then 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

SELECT js,
  js IS JSON OBJECT "object?",
  js IS JSON ARRAY "array?",
  js IS JSON ARRAY WITH UNIQUE KEYS "array w. UK?",
  js IS JSON ARRAY WITHOUT UNIQUE KEYS "array w/o UK?"
FROM (VALUES ('[{"a":"1"},
 {"b":"2","b":"3"}]')) foo(js);
-[ RECORD 1 ]-+--------------------
js            | [{"a":"1"},        +
              |  {"b":"2","b":"3"}]
object?       | f
array?        | t
array w. UK?  | f
array w/o UK? | t

   Table 9.51 shows the functions that are available for processing json
   and jsonb values.

   Table 9.51. 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")

   jsonb_populate_record_valid ( base anyelement, from_json json ) →
   boolean

   Function for testing jsonb_populate_record. Returns true if the input
   jsonb_populate_record would finish without an error for the given input
   JSON object; that is, it's valid input, false otherwise.

   create type jsb_char2 as (a char(2));

   select jsonb_populate_record_valid(NULL::jsb_char2, '{"a": "aaa"}'); →
 jsonb_populate_record_valid
-----------------------------
 f
(1 row)

   select * from jsonb_populate_record(NULL::jsb_char2, '{"a": "aaa"}') q;
   →
ERROR:  value too long for type character(2)

   select jsonb_populate_record_valid(NULL::jsb_char2, '{"a": "aa"}'); →
 jsonb_populate_record_valid
-----------------------------
 t
(1 row)

   select * from jsonb_populate_record(NULL::jsb_char2, '{"a": "aa"}') q;
   →
 a
----
 aa
(1 row)

   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 ( target json [,strip_in_arrays boolean ] ) → json

   jsonb_strip_nulls ( target jsonb [,strip_in_arrays boolean ] ) → jsonb

   Deletes all object fields that have null values from the given JSON
   value, recursively. If strip_in_arrays is true (the default is false),
   null array elements are also stripped. Otherwise they are not stripped.
   Bare null values are never stripped.

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

   jsonb_strip_nulls('[1,2,null,3,4]', true); → [1,2,3,4]

   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. (This is useful only with SQL-standard JSON path expressions,
   not predicate check expressions, since those always return a 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 SQL boolean result of a JSON path predicate check for the
   specified JSON value. (This is useful only with predicate check
   expressions, not SQL-standard JSON path expressions, since it will
   either fail or return NULL if the path result is not a single boolean
   value.) 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. For SQL-standard JSON path expressions it returns the JSON
   values selected from target. For predicate check expressions it returns
   the result of the predicate check: true, false, or null. 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 parameters are the same as for
   jsonb_path_query.

   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, or NULL if there are no results. The parameters are the
   same as for jsonb_path_query.

   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

9.16.2. The SQL/JSON Path Language #

   SQL/JSON path expressions specify item(s) to be retrieved from a JSON
   value, similarly to XPath expressions used for access to XML content.
   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.
   If there is no match, the result will be NULL, false, or an error,
   depending on the function. 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. The first element of a path must
   always be $. 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 accessor operator acts on the result(s) of
   the previous evaluation step, producing zero, one, or more output items
   from each input item.

   For example, suppose you have some JSON data from a GPS tracker that
   you would like to parse, such as:
SELECT '{
  "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
      }
    ]
  }
}' AS json \gset

   (The above example can be copied-and-pasted into psql to set things up
   for the following examples. Then psql will expand :'json' into a
   suitably-quoted string constant containing the JSON value.)

   To retrieve the available track segments, you need to use the .key
   accessor operator to descend through surrounding JSON objects, for
   example:
=> select jsonb_path_query(:'json', '$.track.segments');
                                                                         jsonb_p
ath_query
-----------------------------------------------------------​--------------------
---------------------------------------​----------------------------------------
-----
 [{"HR": 73, "location": [47.763, 13.4034], "start time": "2018-10-14 10:05:14"}
, {"HR": 135, "location": [47.706, 13.2635], "start time": "2018-10-14 10:39:21"
}]

   To retrieve the contents of an array, you typically use the [*]
   operator. The following example will return the location coordinates
   for all the available track segments:
=> select jsonb_path_query(:'json', '$.track.segments[*].location');
 jsonb_path_query
-------------------
 [47.763, 13.4034]
 [47.706, 13.2635]

   Here we started with the whole JSON input value ($), then the .track
   accessor selected the JSON object associated with the "track" object
   key, then the .segments accessor selected the JSON array associated
   with the "segments" key within that object, then the [*] accessor
   selected each element of that array (producing a series of items), then
   the .location accessor selected the JSON array associated with the
   "location" key within each of those objects. In this example, each of
   those objects had a "location" key; but if any of them did not, the
   .location accessor would have simply produced no output for that input
   item.

   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:
=> select jsonb_path_query(:'json', '$.track.segments[0].location');
 jsonb_path_query
-------------------
 [47.763, 13.4034]

   The result of each path evaluation step can be processed by one or more
   of the jsonpath operators and methods listed in Section 9.16.2.3. Each
   method name must be preceded by a dot. For example, you can get the
   size of an array:
=> select jsonb_path_query(:'json', '$.track.segments.size()');
 jsonb_path_query
------------------
 2

   More examples of using jsonpath operators and methods within path
   expressions appear below in Section 9.16.2.3.

   A path can also contain 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 produce 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 9.53. Within a filter expression, the @ variable
   denotes the value being considered (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 as follows:
=> select jsonb_path_query(:'json', '$.track.segments[*].HR ? (@ > 130)');
 jsonb_path_query
------------------
 135

   To get the start times of segments with such values, you have to filter
   out irrelevant segments before selecting the start times, so the filter
   expression is applied to the previous step, and the path used in the
   condition is different:
=> select jsonb_path_query(:'json', '$.track.segments[*] ? (@.HR > 130)."start t
ime"');
   jsonb_path_query
-----------------------
 "2018-10-14 10:39:21"

   You can use several filter expressions in sequence, if required. The
   following example selects start times of all segments that contain
   locations with relevant coordinates and high heart rate values:
=> select jsonb_path_query(:'json', '$.track.segments[*] ? (@.location[1] < 13.4
) ? (@.HR > 130)."start time"');
   jsonb_path_query
-----------------------
 "2018-10-14 10:39:21"

   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:
=> select jsonb_path_query(:'json', '$.track.segments[*] ? (@.location[1] < 13.4
).HR ? (@ > 130)');
 jsonb_path_query
------------------
 135

   You can also nest filter expressions within each other. This example
   returns the size of the track if it contains any segments with high
   heart rate values, or an empty sequence otherwise:
=> select jsonb_path_query(:'json', '$.track ? (exists(@.segments[*] ? (@.HR > 1
30))).segments.size()');
 jsonb_path_query
------------------
 2

9.16.2.1. Deviations from the SQL Standard #

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

9.16.2.1.1. Boolean Predicate Check Expressions #

   As an extension to the SQL standard, a PostgreSQL path expression can
   be a Boolean predicate, whereas the SQL standard allows predicates only
   within filters. While SQL-standard path expressions return the relevant
   element(s) of the queried JSON value, predicate check expressions
   return the single three-valued jsonb result of the predicate: true,
   false, or null. For example, we could write this SQL-standard filter
   expression:
=> select jsonb_path_query(:'json', '$.track.segments ?(@[*].HR > 130)');
                                jsonb_path_query
-----------------------------------------------------------​--------------------
--
 {"HR": 135, "location": [47.706, 13.2635], "start time": "2018-10-14 10:39:21"}

   The similar predicate check expression simply returns true, indicating
   that a match exists:
=> select jsonb_path_query(:'json', '$.track.segments[*].HR > 130');
 jsonb_path_query
------------------
 true

Note

   Predicate check expressions are required in the @@ operator (and the
   jsonb_path_match function), and should not be used with the @? operator
   (or the jsonb_path_exists function).

9.16.2.1.2. Regular Expression Interpretation #

   There are minor differences in the interpretation of regular expression
   patterns used in like_regex filters, as described in Section 9.16.2.4.

9.16.2.2. 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 is defined as 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 structural errors that cannot be fixed
       as described below are suppressed, producing no match.
     * strict — if a structural error occurs, an error is raised.

   Lax mode facilitates matching of a JSON document and path expression
   when 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 the
   operation. Also, comparison operators automatically unwrap their
   operands in 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 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 lax mode:
=> select jsonb_path_query(:'json', 'lax $.track.segments.location');
 jsonb_path_query
-------------------
 [47.763, 13.4034]
 [47.706, 13.2635]

   In strict mode, the specified path must exactly match the structure of
   the queried JSON document, so using this path expression will cause an
   error:
=> select jsonb_path_query(:'json', 'strict $.track.segments.location');
ERROR:  jsonpath member accessor can only be applied to an object

   To get the same result as in lax mode, you have to explicitly unwrap
   the segments array:
=> select jsonb_path_query(:'json', 'strict $.track.segments[*].location');
 jsonb_path_query
-------------------
 [47.763, 13.4034]
 [47.706, 13.2635]

   The unwrapping behavior of lax mode can lead to surprising results. For
   instance, the following query using the .** accessor selects every HR
   value twice:
=> select jsonb_path_query(:'json', 'lax $.**.HR');
 jsonb_path_query
------------------
 73
 135
 73
 135

   This happens because the .** accessor selects both the segments array
   and each of its elements, while the .HR accessor automatically unwraps
   arrays when using lax mode. To avoid surprising results, we recommend
   using the .** accessor only in strict mode. The following query selects
   each HR value just once:
=> select jsonb_path_query(:'json', 'strict $.**.HR');
 jsonb_path_query
------------------
 73
 135

   The unwrapping of arrays can also lead to unexpected results. Consider
   this example, which selects all the location arrays:
=> select jsonb_path_query(:'json', 'lax $.track.segments[*].location');
 jsonb_path_query
-------------------
 [47.763, 13.4034]
 [47.706, 13.2635]
(2 rows)

   As expected it returns the full arrays. But applying a filter
   expression causes the arrays to be unwrapped to evaluate each item,
   returning only the items that match the expression:
=> select jsonb_path_query(:'json', 'lax $.track.segments[*].location ?(@[*] > 1
5)');
 jsonb_path_query
------------------
 47.763
 47.706
(2 rows)

   This despite the fact that the full arrays are selected by the path
   expression. Use strict mode to restore selecting the arrays:
=> select jsonb_path_query(:'json', 'strict $.track.segments[*].location ?(@[*]
> 15)');
 jsonb_path_query
-------------------
 [47.763, 13.4034]
 [47.706, 13.2635]
(2 rows)

9.16.2.3. SQL/JSON Path Operators and Methods #

   Table 9.52 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. In lax mode,
   methods applied to an array will be executed for each value in the
   array. The exceptions are .type() and .size(), which apply to the array
   itself.

   Table 9.52. 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 . boolean() → boolean

   Boolean value converted from a JSON boolean, number, or string

   jsonb_path_query_array('[1, "yes", false]', '$[*].boolean()') → [true,
   true, false]

   value . string() → string

   String value converted from a JSON boolean, number, string, or datetime

   jsonb_path_query_array('[1.23, "xyz", false]', '$[*].string()') →
   ["1.23", "xyz", "false"]

   jsonb_path_query('"2023-08-15 12:34:56"', '$.timestamp().string()') →
   "2023-08-15T12:34:56"

   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

   value . bigint() → bigint

   Big integer value converted from a JSON number or string

   jsonb_path_query('{"len": "9876543219"}', '$.len.bigint()') →
   9876543219

   value . decimal( [ precision [ , scale ] ] ) → decimal

   Rounded decimal value converted from a JSON number or string (precision
   and scale must be integer values)

   jsonb_path_query('1234.5678', '$.decimal(6, 2)') → 1234.57

   value . integer() → integer

   Integer value converted from a JSON number or string

   jsonb_path_query('{"len": "12345"}', '$.len.integer()') → 12345

   value . number() → numeric

   Numeric value converted from a JSON number or string

   jsonb_path_query('{"len": "123.45"}', '$.len.number()') → 123.45

   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"]

   string . date() → date

   Date value converted from a string

   jsonb_path_query('"2023-08-15"', '$.date()') → "2023-08-15"

   string . time() → time without time zone

   Time without time zone value converted from a string

   jsonb_path_query('"12:34:56"', '$.time()') → "12:34:56"

   string . time(precision) → time without time zone

   Time without time zone value converted from a string, with fractional
   seconds adjusted to the given precision

   jsonb_path_query('"12:34:56.789"', '$.time(2)') → "12:34:56.79"

   string . time_tz() → time with time zone

   Time with time zone value converted from a string

   jsonb_path_query('"12:34:56 +05:30"', '$.time_tz()') → "12:34:56+05:30"

   string . time_tz(precision) → time with time zone

   Time with time zone value converted from a string, with fractional
   seconds adjusted to the given precision

   jsonb_path_query('"12:34:56.789 +05:30"', '$.time_tz(2)') →
   "12:34:56.79+05:30"

   string . timestamp() → timestamp without time zone

   Timestamp without time zone value converted from a string

   jsonb_path_query('"2023-08-15 12:34:56"', '$.timestamp()') →
   "2023-08-15T12:34:56"

   string . timestamp(precision) → timestamp without time zone

   Timestamp without time zone value converted from a string, with
   fractional seconds adjusted to the given precision

   jsonb_path_query('"2023-08-15 12:34:56.789"', '$.timestamp(2)') →
   "2023-08-15T12:34:56.79"

   string . timestamp_tz() → timestamp with time zone

   Timestamp with time zone value converted from a string

   jsonb_path_query('"2023-08-15 12:34:56 +05:30"', '$.timestamp_tz()') →
   "2023-08-15T12:34:56+05:30"

   string . timestamp_tz(precision) → timestamp with time zone

   Timestamp with time zone value converted from a string, with fractional
   seconds adjusted to the given precision

   jsonb_path_query('"2023-08-15 12:34:56.789 +05:30"',
   '$.timestamp_tz(2)') → "2023-08-15T12:34:56.79+05:30"

   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. Similarly, other date/time-related methods that convert
   strings to date/time types also do this casting, which may involve the
   current TimeZone setting. Therefore, these conversions can also only be
   performed within timezone-aware jsonpath functions.

   Table 9.53 shows the available filter expression elements.

   Table 9.53. 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 9.16.2.4).

   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') → []

9.16.2.4. 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 Query Functions #

   SQL/JSON functions JSON_EXISTS(), JSON_QUERY(), and JSON_VALUE()
   described in Table 9.54 can be used to query JSON documents. Each of
   these functions apply a path_expression (an SQL/JSON path query) to a
   context_item (the document). See Section 9.16.2 for more details on
   what the path_expression can contain. The path_expression can also
   reference variables, whose values are specified with their respective
   names in the PASSING clause that is supported by each function.
   context_item can be a jsonb value or a character string that can be
   successfully cast to jsonb.

   Table 9.54. SQL/JSON Query Functions

   Function signature

   Description

   Example(s)

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

     * Returns true if the SQL/JSON path_expression applied to the
       context_item yields any items, false otherwise.
     * The ON ERROR clause specifies the behavior if an error occurs
       during path_expression evaluation. Specifying ERROR will cause an
       error to be thrown with the appropriate message. Other options
       include returning boolean values FALSE or TRUE or the value UNKNOWN
       which is actually an SQL NULL. The default when no ON ERROR clause
       is specified is to return the boolean value FALSE.

   Examples:

   JSON_EXISTS(jsonb '{"key1": [1,2,3]}', 'strict $.key1[*] ? (@ > $x)'
   PASSING 2 AS x) → 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_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
]) → jsonb

     * Returns the result of applying the SQL/JSON path_expression to the
       context_item.
     * By default, the result is returned as a value of type jsonb, though
       the RETURNING clause can be used to return as some other type to
       which it can be successfully coerced.
     * If the path expression may return multiple values, it might be
       necessary to wrap those values using the WITH WRAPPER clause to
       make it a valid JSON string, because the default behavior is to not
       wrap them, as if WITHOUT WRAPPER were specified. The WITH WRAPPER
       clause is by default taken to mean WITH UNCONDITIONAL WRAPPER,
       which means that even a single result value will be wrapped. To
       apply the wrapper only when multiple values are present, specify
       WITH CONDITIONAL WRAPPER. Getting multiple values in result will be
       treated as an error if WITHOUT WRAPPER is specified.
     * If the result is a scalar string, by default, the returned value
       will be surrounded by quotes, making it a valid JSON value. It can
       be made explicit by specifying KEEP QUOTES. Conversely, quotes can
       be omitted by specifying OMIT QUOTES. To ensure that the result is
       a valid JSON value, OMIT QUOTES cannot be specified when WITH
       WRAPPER is also specified.
     * The ON EMPTY clause specifies the behavior if evaluating
       path_expression yields an empty set. The ON ERROR clause specifies
       the behavior if an error occurs when evaluating path_expression,
       when coercing the result value to the RETURNING type, or when
       evaluating the ON EMPTY expression if the path_expression
       evaluation returns an empty set.
     * For both ON EMPTY and ON ERROR, specifying ERROR will cause an
       error to be thrown with the appropriate message. Other options
       include returning an SQL NULL, an empty array (EMPTY [ARRAY]), an
       empty object (EMPTY OBJECT), or a user-specified expression
       (DEFAULT expression) that can be coerced to jsonb or the type
       specified in RETURNING. The default when ON EMPTY or ON ERROR is
       not specified is to return an SQL NULL value.

   Examples:

   JSON_QUERY(jsonb '[1,[2,3],null]', 'lax $[*][$off]' PASSING 1 AS off
   WITH CONDITIONAL WRAPPER) → 3

   JSON_QUERY(jsonb '{"a": "[1, 2]"}', 'lax $.a' OMIT QUOTES) → [1, 2]

   JSON_QUERY(jsonb '{"a": "[1, 2]"}', 'lax $.a' RETURNING int[] OMIT
   QUOTES ERROR ON ERROR) →
ERROR:  malformed array literal: "[1, 2]"
DETAIL:  Missing "]" after array dimensions.

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 ]) → text

     * Returns the result of applying the SQL/JSON path_expression to the
       context_item.
     * Only use JSON_VALUE() if the extracted value is expected to be a
       single SQL/JSON scalar item; getting multiple values will be
       treated as an error. If you expect that extracted value might be an
       object or an array, use the JSON_QUERY function instead.
     * By default, the result, which must be a single scalar value, is
       returned as a value of type text, though the RETURNING clause can
       be used to return as some other type to which it can be
       successfully coerced.
     * The ON ERROR and ON EMPTY clauses have similar semantics as
       mentioned in the description of JSON_QUERY, except the set of
       values returned in lieu of throwing an error is different.
     * Note that scalar strings returned by JSON_VALUE always have their
       quotes removed, equivalent to specifying OMIT QUOTES in JSON_QUERY.

   Examples:

   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 $[$off]' PASSING 1 as off) → 2

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

Note

   The context_item expression is converted to jsonb by an implicit cast
   if the expression is not already of type jsonb. Note, however, that any
   parsing errors that occur during that conversion are thrown
   unconditionally, that is, are not handled according to the (specified
   or implicit) ON ERROR clause.

Note

   JSON_VALUE() returns an SQL NULL if path_expression returns a JSON
   null, whereas JSON_QUERY() returns the JSON null as is.

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 use JSON_TABLE inside the FROM clause of a SELECT,
   UPDATE, or DELETE and as data source in a MERGE statement.

   Taking JSON data as input, JSON_TABLE uses a JSON path expression to
   extract a part of the provided data to use as a row pattern for the
   constructed view. Each SQL/JSON value given by the row pattern serves
   as source for a separate row in the constructed 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, a
   separate JSON path expression can be specified to be evaluated against
   the row pattern to get an SQL/JSON value that will become the value for
   the specified column in a given output row.

   JSON data stored at a nested level of the row pattern can be extracted
   using the NESTED PATH clause. Each NESTED PATH clause can be used to
   generate one or more columns using the data from a nested level of the
   row pattern. Those columns can be specified using a COLUMNS clause that
   looks similar to the top-level COLUMNS clause. Rows constructed from
   NESTED COLUMNS are called child rows and are joined against the row
   constructed from the columns specified in the parent COLUMNS clause to
   get the row in the final view. Child columns themselves may contain a
   NESTED PATH specification thus allowing to extract data located at
   arbitrary nesting levels. Columns produced by multiple NESTED PATHs at
   the same level are considered to be siblings of each other and their
   rows after joining with the parent row are combined using UNION.

   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.

   The syntax is:
JSON_TABLE (
    context_item, path_expression [ AS json_path_name ] [ PASSING { value AS var
name } [, ...] ]
    COLUMNS ( json_table_column [, ...] )
    [ { ERROR | EMPTY [ARRAY]} ON ERROR ]
)


where json_table_column is:

  name FOR ORDINALITY
  | name type
        [ FORMAT JSON [ENCODING UTF8]]
        [ PATH path_expression ]
        [ { 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 path_expression ]
        [ { ERROR | TRUE | FALSE | UNKNOWN } ON ERROR ]
  | NESTED [ PATH ] path_expression [ AS json_path_name ] COLUMNS ( json_table_c
olumn [, ...] )

   Each syntax element is described below in more detail.

   context_item, path_expression [ AS json_path_name ] [ PASSING { value
          AS varname } [, ...]]
          The context_item specifies the input document to query, the
          path_expression is an SQL/JSON path expression defining the
          query, and json_path_name is an optional name for the
          path_expression. The optional PASSING clause provides data
          values for the variables mentioned in the path_expression. The
          result of the input data evaluation using the aforementioned
          elements is called the row pattern, which 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 can specify each column to be filled with an
          SQL/JSON value obtained by applying a JSON path expression
          against the row pattern. json_table_column has the following
          variants:

        name FOR ORDINALITY
                Adds an ordinality column that provides sequential row
                numbering starting from 1. Each NESTED PATH (see below)
                gets its own counter for any nested ordinality columns.

        name type [FORMAT JSON [ENCODING UTF8]] [ PATH path_expression ]
                Inserts an SQL/JSON value obtained by applying
                path_expression against the row pattern into the view's
                output row after coercing it to specified type.

                Specifying FORMAT JSON makes it explicit that you expect
                the value to be a valid json object. It only makes sense
                to specify FORMAT JSON if type is one of bpchar, bytea,
                character varying, name, json, jsonb, text, or a domain
                over these types.

                Optionally, you can specify WRAPPER and QUOTES clauses to
                format the output. Note that specifying OMIT QUOTES
                overrides FORMAT JSON if also specified, because unquoted
                literals do not constitute valid json values.

                Optionally, you can use ON EMPTY and ON ERROR clauses to
                specify whether to throw the error or return the specified
                value when the result of JSON path evaluation is empty and
                when an error occurs during JSON path evaluation or when
                coercing the SQL/JSON value to the specified type,
                respectively. The default for both is to return a NULL
                value.

Note

                This clause is internally turned into and has the same
                semantics as JSON_VALUE or JSON_QUERY. The latter if the
                specified type is not a scalar type or if either of FORMAT
                JSON, WRAPPER, or QUOTES clause is present.

        name type EXISTS [ PATH path_expression ]
                Inserts a boolean value obtained by applying
                path_expression against the row pattern into the view's
                output row after coercing it to specified type.

                The value corresponds to whether applying the PATH
                expression to the row pattern yields any values.

                The specified type should have a cast from the boolean
                type.

                Optionally, you can use ON ERROR to specify whether to
                throw the error or return the specified value when an
                error occurs during JSON path evaluation or when coercing
                SQL/JSON value to the specified type. The default is to
                return a boolean value FALSE.

Note

                This clause is internally turned into and has the same
                semantics as JSON_EXISTS.

        NESTED [ PATH ] path_expression [ AS json_path_name ] COLUMNS (
                json_table_column [, ...] )
                Extracts SQL/JSON values from nested levels of the row
                pattern, generates one or more columns as defined by the
                COLUMNS subclause, and inserts the extracted SQL/JSON
                values into those 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.

Note

          In each variant of json_table_column described above, if the
          PATH clause is omitted, path expression $.name is used, where
          name is the provided column name.

   AS json_path_name
          The optional json_path_name serves as an identifier of the
          provided path_expression. The name must be unique and distinct
          from the column names.

   { ERROR | EMPTY } ON ERROR
          The optional ON ERROR can be used to specify how to handle
          errors when evaluating the top-level path_expression. Use ERROR
          if you want the errors to be thrown and EMPTY to return an empty
          table, that is, a table containing 0 rows. Note that this clause
          does not affect the errors that occur when evaluating columns,
          for which the behavior depends on whether the ON ERROR clause is
          specified against a given column.

   Examples

   In the examples that follow, the following table containing 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" } ] }
  ] }');

   The following query shows how to use JSON_TABLE to turn the JSON
   objects in the my_films table to a view containing columns for the keys
   kind, title, and director contained in the original JSON along with an
   ordinality column:
SELECT jt.* FROM
 my_films,
 JSON_TABLE (js, '$.favorites[*]' COLUMNS (
   id FOR ORDINALITY,
   kind text PATH '$.kind',
   title text PATH '$.films[*].title' WITH WRAPPER,
   director text PATH '$.films[*].director' WITH WRAPPER)) AS jt;

 id |   kind   |             title              |             director
----+----------+--------------------------------+-------------------------------
---
  1 | comedy   | ["Bananas", "The Dinner Game"] | ["Woody Allen", "Francis Veber
"]
  2 | horror   | ["Psycho"]                     | ["Alfred Hitchcock"]
  3 | thriller | ["Vertigo"]                    | ["Alfred Hitchcock"]
  4 | drama    | ["Yojimbo"]                    | ["Akira Kurosawa"]
(4 rows)

   The following is a modified version of the above query to show the
   usage of PASSING arguments in the filter specified in the top-level
   JSON path expression and the various options for the individual
   columns:
SELECT jt.* FROM
 my_films,
 JSON_TABLE (js, '$.favorites[*] ? (@.films[*].director == $filter)'
   PASSING 'Alfred Hitchcock' AS filter
     COLUMNS (
     id FOR ORDINALITY,
     kind text PATH '$.kind',
     title text FORMAT JSON PATH '$.films[*].title' OMIT QUOTES,
     director text PATH '$.films[*].director' KEEP QUOTES)) AS jt;

 id |   kind   |  title  |      director
----+----------+---------+--------------------
  1 | horror   | Psycho  | "Alfred Hitchcock"
  2 | thriller | Vertigo | "Alfred Hitchcock"
(2 rows)

   The following is a modified version of the above query to show the
   usage of NESTED PATH for populating title and director columns,
   illustrating how they are joined to the parent columns id and kind:
SELECT jt.* FROM
 my_films,
 JSON_TABLE ( js, '$.favorites[*] ? (@.films[*].director == $filter)'
   PASSING 'Alfred Hitchcock' AS filter
   COLUMNS (
    id FOR ORDINALITY,
    kind text PATH '$.kind',
    NESTED PATH '$.films[*]' COLUMNS (
      title text FORMAT JSON PATH '$.title' OMIT QUOTES,
      director text PATH '$.director' KEEP QUOTES))) AS jt;

 id |   kind   |  title  |      director
----+----------+---------+--------------------
  1 | horror   | Psycho  | "Alfred Hitchcock"
  2 | thriller | Vertigo | "Alfred Hitchcock"
(2 rows)

   The following is the same query but without the filter in the root
   path:
SELECT jt.* FROM
 my_films,
 JSON_TABLE ( js, '$.favorites[*]'
   COLUMNS (
    id FOR ORDINALITY,
    kind text PATH '$.kind',
    NESTED PATH '$.films[*]' COLUMNS (
      title text FORMAT JSON PATH '$.title' OMIT QUOTES,
      director text PATH '$.director' KEEP QUOTES))) 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)

   The following shows another query using a different JSON object as
   input. It shows the UNION "sibling join" between NESTED paths
   $.movies[*] and $.books[*] and also the usage of FOR ORDINALITY column
   at NESTED levels (columns movie_id, book_id, and author_id):
SELECT * FROM JSON_TABLE (
'{"favorites":
    [{"movies":
      [{"name": "One", "director": "John Doe"},
       {"name": "Two", "director": "Don Joe"}],
     "books":
      [{"name": "Mystery", "authors": [{"name": "Brown Dan"}]},
       {"name": "Wonder", "authors": [{"name": "Jun Murakami"}, {"name":"Craig D
oe"}]}]
}]}'::json, '$.favorites[*]'
COLUMNS (
  user_id FOR ORDINALITY,
  NESTED '$.movies[*]'
    COLUMNS (
    movie_id FOR ORDINALITY,
    mname text PATH '$.name',
    director text),
  NESTED '$.books[*]'
    COLUMNS (
      book_id FOR ORDINALITY,
      bname text PATH '$.name',
      NESTED '$.authors[*]'
        COLUMNS (
          author_id FOR ORDINALITY,
          author_name text PATH '$.name'))));

 user_id | movie_id | mname | director | book_id |  bname  | author_id | author_
name
---------+----------+-------+----------+---------+---------+-----------+--------
------
       1 |        1 | One   | John Doe |         |         |           |
       1 |        2 | Two   | Don Joe  |         |         |           |
       1 |          |       |          |       1 | Mystery |         1 | Brown D
an
       1 |          |       |          |       2 | Wonder  |         1 | Jun Mur
akami
       1 |          |       |          |       2 | Wonder  |         2 | Craig D
oe
(5 rows)