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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

  • 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 9.45 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. 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 9.45. 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? (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 @@ jsonpathboolean

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 9.47 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 for which there is a cast from json to that type. By default, the json type is returned.

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]]

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 9.48 details SQL/JSON facilities for testing JSON.

Table 9.48. 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 9.49 shows the functions that are available for processing json and jsonb values.

Table 9.49. 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 ( 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. (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 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 NULLt


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_path_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 9.51. 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 time"');
   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 > 130))).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 result of the predicate: true, false, or unknown. 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 ?(@[*] > 15)');
 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 9.50 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 9.50. 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 . 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"', '$.datetime().string()')"2023-08-15"

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 9.51 shows the available filter expression elements.

Table 9.51. 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 9.16.2.4).

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 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')[]


9.16.2.4. 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 9.52 can be used to query JSON documents. Each of these functions apply a path_expression (the query) to a context_item (the document); see Section 9.16.2 for more details on what path_expression can contain.

Table 9.52. 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 ])

Returns true if the SQL/JSON path_expression applied to the context_item using the PASSING values yields any items.

The ON ERROR clause specifies the behavior if an error occurs; the default is to return the boolean FALSE value. Note that if the path_expression is strict and ON ERROR behavior is ERROR, an error is generated if it yields no items.

Examples:

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

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

select 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 ])

Returns the result of applying the SQL/JSON path_expression to the context_item using the PASSING values.

If the path expression returns multiple SQL/JSON items, it might be necessary to wrap the result using the WITH WRAPPER clause to make it a valid JSON string. If the wrapper is UNCONDITIONAL, an array wrapper will always be applied, even if the returned value is already a single JSON object or an array. If it is CONDITIONAL, it will not be applied to a single JSON object or an array. UNCONDITIONAL is the default.

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. Note that OMIT QUOTES cannot be specified when WITH WRAPPER is also specified.

The RETURNING clause can be used to specify the data_type of the result value. By default, the returned value will be of type jsonb.

The ON EMPTY clause specifies the behavior if evaluating path_expression yields no value at all. The default when ON EMPTY is not specified is to return a null value.

The ON ERROR clause specifies the behavior if an error occurs when evaluating path_expression, including the operation to coerce the result value to the output type, or during the execution of ON EMPTY behavior (that is caused by empty result of path_expression evaluation). The default when ON ERROR is not specified is to return a null value.

Examples:

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

select json_query(jsonb '{"a": "[1, 2]"}', 'lax $.a' OMIT QUOTES);[1, 2]

select 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 ])

Returns the result of applying the SQL/JSON path_expression to the context_item using the PASSING values.

The extracted value must be a single SQL/JSON scalar item; an error is thrown if that's not the case. If you expect that extracted value might be an object or an array, use the json_query function instead.

The RETURNING clause can be used to specify the data_type of the result value. By default, the returned value will be of type text.

The ON ERROR and ON EMPTY clauses have similar semantics as mentioned in the description of json_query.

Note that scalar strings returned by json_value always have their quotes removed, equivalent to specifying OMIT QUOTES in json_query.

Examples:

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

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

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


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 varname } [, ...] ]
    COLUMNS ( json_table_column [, ...] )
    [ { ERROR | EMPTY } 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_column [, ...] )

Each syntax element is described below in more detail.

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

The input data to query (context_item), the JSON path expression defining the query (path_expression) with an optional name (json_path_name), and an optional PASSING clause, which can provide data values to 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 == $filter)'
   PASSING 'Alfred Hitchcock' AS filter, 'Vertigo' AS filter2
     COLUMNS (
     id FOR ORDINALITY,
     kind text PATH '$.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 Doe"}]}]
}}'::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 Dan
       1 |          |       |          |       2 | Wonder  |         1 | Jun Murakami
       1 |          |       |          |       2 | Wonder  |         2 | Craig Doe
(5 rows)