9.16. JSON Functions and Operators #
This section describes:
functions and operators for processing and creating JSON data
the SQL/JSON path language
To provide native support for JSON data types within the SQL environment, Postgres Pro 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 Postgres Pro, see Section 8.14.
9.16.1. Processing and Creating JSON Data #
Note
Functions manipulating JSONB do not accept the '\u0000'
character. To handle this, you can specify a unicode character in the unicode_nul_character_replacement_in_jsonb configuration parameter to replace this character on the fly.
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) |
---|
Extracts
|
Extracts JSON object field with the given key.
|
Extracts
|
Extracts JSON object field with the given key, as
|
Extracts JSON sub-object at the specified path, where path elements can be either field keys or array indexes.
|
Extracts JSON sub-object at the specified path as
|
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) |
---|
Does the first JSON value contain the second? (See Section 8.14.3 for details about containment.)
|
Is the first JSON value contained in the second?
|
Does the text string exist as a top-level key or array element within the JSON value?
|
Do any of the strings in the text array exist as top-level keys or array elements?
|
Do all of the strings in the text array exist as top-level keys or array elements?
|
Concatenates two
To append an array to another array as a single entry, wrap it in an additional layer of array, for example:
|
Deletes a key (and its value) from a JSON object, or matching string value(s) from a JSON array.
|
Deletes all matching keys or array elements from the left operand.
|
Deletes the array element with specified index (negative integers count from the end). Throws an error if JSON value is not an array.
|
Deletes the field or array element at the specified path, where path elements can be either field keys or array indexes.
|
Does JSON path return any item for the specified JSON value?
|
Returns the result of a JSON path predicate check for the specified JSON value. Only the first item of the result is taken into account. If the result is not Boolean, then
|
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) |
---|
Converts any SQL value to
|
Converts an SQL array to a JSON array. The behavior is the same as
|
Constructs a JSON array from either a series of
|
Converts an SQL composite value to a JSON object. The behavior is the same as
|
Builds a possibly-heterogeneously-typed JSON array out of a variadic argument list. Each argument is converted as per
|
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
|
Constructs a JSON object of all the key/value pairs given, or an empty object if none are given.
|
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.
|
This form of
|
Converts a given expression specified as
|
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.
|
Converts an SQL/JSON expression into a character or binary string. The
|
Table 9.48 details SQL/JSON facilities for testing JSON.
Table 9.48. SQL/JSON Testing Functions
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) |
---|
Expands the top-level JSON array into a set of JSON values.
value ----------- 1 true [2,false] |
Expands the top-level JSON array into a set of
value ----------- foo bar |
Returns the number of elements in the top-level JSON array.
|
Expands the top-level JSON object into a set of key/value pairs.
key | value -----+------- a | "foo" b | "bar" |
Expands the top-level JSON object into a set of key/value pairs. The returned
key | value -----+------- a | foo b | bar |
Extracts JSON sub-object at the specified path. (This is functionally equivalent to the
|
Extracts JSON sub-object at the specified path as
|
Returns the set of keys in the top-level JSON object.
json_object_keys ------------------ f1 f2 |
Expands the top-level JSON object to a row having the composite type of the To convert a JSON value to the SQL type of an output column, the following rules are applied in sequence:
While the example below uses a constant JSON value, typical use would be to reference a
a | b | c ---+-----------+------------- 1 | {2,"a b"} | (4,"a b c") |
Expands the top-level JSON array of objects to a set of rows having the composite type of the
a | b ---+--- 1 | 2 3 | 4 |
Expands the top-level JSON object to a row having the composite type defined by an
a | b | c | d | r ---+---------+---------+---+--------------- 1 | [1,2,3] | {1,2,3} | | (123,"a b c") |
Expands the top-level JSON array of objects to a set of rows having the composite type defined by an
a | b ---+----- 1 | foo 2 | |
Returns
|
If
|
Returns
|
Deletes all object fields that have null values from the given JSON value, recursively. Null values that are not object fields are untouched.
|
Checks whether the JSON path returns any item for the specified JSON value. If the
|
Returns the result of a JSON path predicate check for the specified JSON value. Only the first item of the result is taken into account. If the result is not Boolean, then
|
Returns all JSON items returned by the JSON path for the specified JSON value. The optional
jsonb_path_query ------------------ 2 3 4 |
Returns all JSON items returned by the JSON path for the specified JSON value, as a JSON array. The optional
|
Returns the first JSON item returned by the JSON path for the specified JSON value. Returns
|
These functions act like their counterparts described above without the
|
Converts the given JSON value to pretty-printed, indented text.
[ { "f1": 1, "f2": null }, 2 ] |
Returns the type of the top-level JSON value as a text string. Possible types are
|
Table 9.50 details the SQL/JSON functions that can be used to query JSON data.
Note
SQL/JSON paths can only be applied to the jsonb
type, so it might be necessary to cast the context_item
argument of these functions to jsonb
.
Table 9.50. SQL/JSON Query Functions
9.16.2. JSON_TABLE #
json_table
is an SQL/JSON function which queries JSON data and presents the results as a relational view, which can be accessed as a regular SQL table. You can only use json_table
inside the FROM
clause of a SELECT
statement.
Taking JSON data as input, json_table
uses a path expression to extract a part of the provided data that will be used as a row pattern for the constructed view. Each SQL/JSON item at the top level of the row pattern serves as the source for a separate row in the constructed relational view.
To split the row pattern into columns, json_table
provides the COLUMNS
clause that defines the schema of the created view. For each column to be constructed, this clause provides a separate path expression that evaluates the row pattern, extracts a JSON item, and returns it as a separate SQL value for the specified column. If the required value is stored in a nested level of the row pattern, it can be extracted using the NESTED PATH
subclause. Joining the columns returned by NESTED PATH
can add multiple new rows to the constructed view. Such rows are called child rows, as opposed to the parent row that generates them.
The rows produced by JSON_TABLE
are laterally joined to the row that generated them, so you do not have to explicitly join the constructed view with the original table holding JSON data. Optionally, you can specify how to join the columns returned by NESTED PATH
using the PLAN
clause.
Each NESTED PATH
clause can generate one or more columns. Columns produced by NESTED PATH
s at the same level are considered to be siblings, while a column produced by a NESTED PATH
is considered to be a child of the column produced by a NESTED PATH
or row expression at a higher level. Sibling columns are always joined first. Once they are processed, the resulting rows are joined to the parent row.
The syntax is:
JSON_TABLE (context_item
,path_expression
[ ASjson_path_name
] [ PASSING {value
ASvarname
} [, ...] ] COLUMNS (json_table_column
[, ...] ) [ {ERROR
|EMPTY
}ON ERROR
] [ PLAN (json_table_plan
) | PLAN DEFAULT ( { INNER | OUTER } [ , { CROSS | UNION } ] | { CROSS | UNION } [ , { INNER | OUTER } ] ) ] ) wherejson_table_column
is:name
type
[ PATHjson_path_specification
] [ { WITHOUT | WITH { CONDITIONAL | [UNCONDITIONAL] } } [ ARRAY ] WRAPPER ] [ { KEEP | OMIT } QUOTES [ ON SCALAR STRING ] ] [ { ERROR | NULL | DEFAULTexpression
} ON EMPTY ] [ { ERROR | NULL | DEFAULTexpression
} ON ERROR ] |name
type
FORMATjson_representation
[ PATHjson_path_specification
] [ { WITHOUT | WITH { CONDITIONAL | [UNCONDITIONAL] } } [ ARRAY ] WRAPPER ] [ { KEEP | OMIT } QUOTES [ ON SCALAR STRING ] ] [ { ERROR | NULL | EMPTY { ARRAY | OBJECT } | DEFAULTexpression
} ON EMPTY ] [ { ERROR | NULL | EMPTY { ARRAY | OBJECT } | DEFAULTexpression
} ON ERROR ] |name
type
EXISTS [ PATHjson_path_specification
] [ { ERROR | TRUE | FALSE | UNKNOWN } ON ERROR ] | NESTED PATHjson_path_specification
[ ASpath_name
] COLUMNS (json_table_column
[, ...] ) |name
FOR ORDINALITYjson_table_plan
is:json_path_name
[ { OUTER | INNER }json_table_plan_primary
] |json_table_plan_primary
{ UNIONjson_table_plan_primary
} [...] |json_table_plan_primary
{ CROSSjson_table_plan_primary
} [...]json_table_plan_primary
is:json_path_name
| (json_table_plan
)
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, the JSON path expression defining the query, and an optional
PASSING
clause, which can provide data values to thepath_expression
. The result of the input data evaluation is called the row pattern. The row pattern is used as the source for row values in the constructed view.-
COLUMNS
(json_table_column
[, ...] ) The
COLUMNS
clause defining the schema of the constructed view. In this clause, you must specify all the columns to be filled with SQL/JSON items. Thejson_table_column
expression has the following syntax variants:-
name
type
[PATH
json_path_specification
] Inserts a single SQL/JSON item into each row of the specified column.
The provided
PATH
expression is evaluated and the column is filled with the produced SQL/JSON items, one for each row. If thePATH
expression is omitted,JSON_TABLE
uses the$.
path expression, wherename
name
is the provided column name. In this case, the column name must correspond to one of the keys within the SQL/JSON item produced by the row pattern.Optionally, you can add
ON EMPTY
andON ERROR
clauses to define how to handle missing values or structural errors.WRAPPER
andQUOTES
clauses can only be used with JSON, array, and composite types. These clauses have the same syntax and semantics as forjson_value
andjson_query
.-
name
type
FORMAT
json_representation
[PATH
json_path_specification
] Generates a column and inserts a composite SQL/JSON item into each row of this column.
The provided
PATH
expression is evaluated and the column is filled with the produced SQL/JSON items, one for each row. If thePATH
expression is omitted,JSON_TABLE
uses the$.
path expression, wherename
name
is the provided column name. In this case, the column name must correspond to one of the keys within the SQL/JSON item produced by the row pattern.Optionally, you can add
WRAPPER
,QUOTES
,ON EMPTY
andON ERROR
clauses to define additional settings for the returned SQL/JSON items. These clauses have the same syntax and semantics as forjson_query
.-
name
type
EXISTS
[PATH
json_path_specification
] Generates a column and inserts a boolean item into each row of this column.
The provided
PATH
expression is evaluated, a check whether any SQL/JSON items were returned is done, and the column is filled with the resulting boolean value, one for each row. The specifiedtype
should have a cast from theboolean
. If thePATH
expression is omitted,JSON_TABLE
uses the$.
path expression, wherename
name
is the provided column name.Optionally, you can add
ON ERROR
clause to define error behavior. This clause has the same syntax and semantics as forjson_exists
.-
NESTED PATH
json_path_specification
[AS
json_path_name
]COLUMNS
(json_table_column
[, ...] ) Extracts SQL/JSON items from nested levels of the row pattern, generates one or more columns as defined by the
COLUMNS
subclause, and inserts the extracted SQL/JSON items into each row of these columns. Thejson_table_column
expression in theCOLUMNS
subclause uses the same syntax as in the parentCOLUMNS
clause.The
NESTED PATH
syntax is recursive, so you can go down multiple nested levels by specifying severalNESTED PATH
subclauses within each other. It allows to unnest the hierarchy of JSON objects and arrays in a single function invocation rather than chaining severalJSON_TABLE
expressions in an SQL statement.You can use the
PLAN
clause to define how to join the columns returned byNESTED PATH
clauses.-
name
FOR ORDINALITY
Adds an ordinality column that provides sequential row numbering. You can have only one ordinality column per table. Row numbering is 1-based. For child rows that result from the
NESTED PATH
clauses, the parent row number is repeated.
-
-
AS
json_path_name
The optional
json_path_name
serves as an identifier of the providedjson_path_specification
. The path name must be unique and distinct from the column names. When using thePLAN
clause, you must specify the names for all the paths, including the row pattern. Each path name can appear in thePLAN
clause only once.-
PLAN
(json_table_plan
) Defines how to join the data returned by
NESTED PATH
clauses to the constructed view.To join columns with parent/child relationship, you can use:
-
INNER
Use
INNER JOIN
, so that the parent row is omitted from the output if it does not have any child rows after joining the data returned byNESTED PATH
.-
OUTER
Use
LEFT OUTER JOIN
, so that the parent row is always included into the output even if it does not have any child rows after joining the data returned byNESTED PATH
, with NULL values inserted into the child columns if the corresponding values are missing.This is the default option for joining columns with parent/child relationship.
To join sibling columns, you can use:
-
UNION
Generate one row for each value produced by each of the sibling columns. The columns from the other siblings are set to null.
This is the default option for joining sibling columns.
-
CROSS
Generate one row for each combination of values from the sibling columns.
-
-
PLAN DEFAULT
(
)OUTER | INNER
[,UNION | CROSS
] The terms can also be specified in reverse order. The
INNER
orOUTER
option defines the joining plan for parent/child columns, whileUNION
orCROSS
affects joins of sibling columns. This form ofPLAN
overrides the default plan for all columns at once. Even though the path names are not included in thePLAN DEFAULT
form, to conform to the SQL/JSON standard they must be provided for all the paths if thePLAN
clause is used.PLAN DEFAULT
is simpler than specifying a completePLAN
, and is often all that is required to get the desired output.
Examples
In these examples the following small table storing some JSON data will be used:
CREATE TABLE my_films ( js jsonb ); INSERT INTO my_films VALUES ( '{ "favorites" : [ { "kind" : "comedy", "films" : [ { "title" : "Bananas", "director" : "Woody Allen"}, { "title" : "The Dinner Game", "director" : "Francis Veber" } ] }, { "kind" : "horror", "films" : [ { "title" : "Psycho", "director" : "Alfred Hitchcock" } ] }, { "kind" : "thriller", "films" : [ { "title" : "Vertigo", "director" : "Alfred Hitchcock" } ] }, { "kind" : "drama", "films" : [ { "title" : "Yojimbo", "director" : "Akira Kurosawa" } ] } ] }');
Query the my_films
table holding some JSON data about the films and create a view that distributes the film genre, title, and director between separate columns:
SELECT jt.* FROM my_films, JSON_TABLE ( js, '$.favorites[*]' COLUMNS ( id FOR ORDINALITY, kind text PATH '$.kind', NESTED PATH '$.films[*]' COLUMNS ( title text PATH '$.title', director text PATH '$.director'))) AS jt; ----+----------+------------------+------------------- id | kind | title | director ----+----------+------------------+------------------- 1 | comedy | Bananas | Woody Allen 1 | comedy | The Dinner Game | Francis Veber 2 | horror | Psycho | Alfred Hitchcock 3 | thriller | Vertigo | Alfred Hitchcock 4 | drama | Yojimbo | Akira Kurosawa (5 rows)
Find a director that has done films in two different genres:
SELECT director1 AS director, title1, kind1, title2, kind2 FROM my_films, JSON_TABLE ( js, '$.favorites' AS favs COLUMNS ( NESTED PATH '$[*]' AS films1 COLUMNS ( kind1 text PATH '$.kind', NESTED PATH '$.films[*]' AS film1 COLUMNS ( title1 text PATH '$.title', director1 text PATH '$.director') ), NESTED PATH '$[*]' AS films2 COLUMNS ( kind2 text PATH '$.kind', NESTED PATH '$.films[*]' AS film2 COLUMNS ( title2 text PATH '$.title', director2 text PATH '$.director' ) ) ) PLAN (favs OUTER ((films1 INNER film1) CROSS (films2 INNER film2))) ) AS jt WHERE kind1 > kind2 AND director1 = director2; director | title1 | kind1 | title2 | kind2 ------------------+---------+----------+--------+-------- Alfred Hitchcock | Vertigo | thriller | Psycho | horror (1 row)
9.16.3. The SQL/JSON Path Language #
SQL/JSON path expressions specify the items to be retrieved from the JSON data, similar to XPath expressions used for SQL access to XML. In Postgres Pro, path expressions are implemented as the jsonpath
data type and can use any elements described in Section 8.14.7.
JSON query functions and operators pass the provided path expression to the path engine for evaluation. If the expression matches the queried JSON data, the corresponding JSON item, or set of items, is returned. Path expressions are written in the SQL/JSON path language and can include arithmetic expressions and functions.
A path expression consists of a sequence of elements allowed by the jsonpath
data type. The path expression is normally evaluated from left to right, but you can use parentheses to change the order of operations. If the evaluation is successful, a sequence of JSON items is produced, and the evaluation result is returned to the JSON query function that completes the specified computation.
To refer to the JSON value being queried (the context item), use the $
variable in the path expression. It can be followed by one or more accessor operators, which go down the JSON structure level by level to retrieve sub-items of the context item. Each operator that follows deals with the result of the previous evaluation step.
For example, suppose you have some JSON data from a GPS tracker that you would like to parse, such as:
{ "track": { "segments": [ { "location": [ 47.763, 13.4034 ], "start time": "2018-10-14 10:05:14", "HR": 73 }, { "location": [ 47.706, 13.2635 ], "start time": "2018-10-14 10:39:21", "HR": 135 } ] } }
To retrieve the available track segments, you need to use the .
accessor operator to descend through surrounding JSON objects: key
$.track.segments
To retrieve the contents of an array, you typically use the [*]
operator. For example, the following path will return the location coordinates for all the available track segments:
$.track.segments[*].location
To return the coordinates of the first segment only, you can specify the corresponding subscript in the []
accessor operator. Recall that JSON array indexes are 0-relative:
$.track.segments[0].location
The result of each path evaluation step can be processed by one or more jsonpath
operators and methods listed in Section 9.16.3.2. Each method name must be preceded by a dot. For example, you can get the size of an array:
$.track.segments.size()
More examples of using jsonpath
operators and methods within path expressions appear below in Section 9.16.3.2.
When defining a path, you can also use one or more filter expressions that work similarly to the WHERE
clause in SQL. A filter expression begins with a question mark and provides a condition in parentheses:
? (condition
)
Filter expressions must be written just after the path evaluation step to which they should apply. The result of that step is filtered to include only those items that satisfy the provided condition. SQL/JSON defines three-valued logic, so the condition can be true
, false
, or unknown
. The unknown
value plays the same role as SQL NULL
and can be tested for with the is unknown
predicate. Further path evaluation steps use only those items for which the filter expression returned true
.
The functions and operators that can be used in filter expressions are listed in Table 9.52. Within a filter expression, the @
variable denotes the value being filtered (i.e., one result of the preceding path step). You can write accessor operators after @
to retrieve component items.
For example, suppose you would like to retrieve all heart rate values higher than 130. You can achieve this using the following expression:
$.track.segments[*].HR ? (@ > 130)
To get the start times of segments with such values, you have to filter out irrelevant segments before returning the start times, so the filter expression is applied to the previous step, and the path used in the condition is different:
$.track.segments[*] ? (@.HR > 130)."start time"
You can use several filter expressions in sequence, if required. For example, the following expression selects start times of all segments that contain locations with relevant coordinates and high heart rate values:
$.track.segments[*] ? (@.location[1] < 13.4) ? (@.HR > 130)."start time"
Using filter expressions at different nesting levels is also allowed. The following example first filters all segments by location, and then returns high heart rate values for these segments, if available:
$.track.segments[*] ? (@.location[1] < 13.4).HR ? (@ > 130)
You can also nest filter expressions within each other:
$.track ? (exists(@.segments[*] ? (@.HR > 130))).segments.size()
This expression returns the size of the track if it contains any segments with high heart rate values, or an empty sequence otherwise.
Postgres Pro's implementation of the SQL/JSON path language has the following deviations from the SQL/JSON standard:
A path expression can be a Boolean predicate, although the SQL/JSON standard allows predicates only in filters. This is necessary for implementation of the
@@
operator. For example, the followingjsonpath
expression is valid in Postgres Pro:$.track.segments[*].HR < 70
There are minor differences in the interpretation of regular expression patterns used in
like_regex
filters, as described in Section 9.16.3.3.
9.16.3.1. Strict and Lax Modes #
When you query JSON data, the path expression may not match the actual JSON data structure. An attempt to access a non-existent member of an object or element of an array results in a structural error. SQL/JSON path expressions have two modes of handling structural errors:
lax (default) — the path engine implicitly adapts the queried data to the specified path. Any remaining structural errors are suppressed and converted to empty SQL/JSON sequences.
strict — if a structural error occurs, an error is raised.
The lax mode facilitates matching of a JSON document structure and path expression if the JSON data does not conform to the expected schema. If an operand does not match the requirements of a particular operation, it can be automatically wrapped as an SQL/JSON array or unwrapped by converting its elements into an SQL/JSON sequence before performing this operation. Besides, comparison operators automatically unwrap their operands in the lax mode, so you can compare SQL/JSON arrays out-of-the-box. An array of size 1 is considered equal to its sole element. Automatic unwrapping is not performed only when:
The path expression contains
type()
orsize()
methods that return the type and the number of elements in the array, respectively.The queried JSON data contain nested arrays. In this case, only the outermost array is unwrapped, while all the inner arrays remain unchanged. Thus, implicit unwrapping can only go one level down within each path evaluation step.
For example, when querying the GPS data listed above, you can abstract from the fact that it stores an array of segments when using the lax mode:
lax $.track.segments.location
In the strict mode, the specified path must exactly match the structure of the queried JSON document to return an SQL/JSON item, so using this path expression will cause an error. To get the same result as in the lax mode, you have to explicitly unwrap the segments
array:
strict $.track.segments[*].location
The .**
accessor can lead to surprising results when using the lax mode. For instance, the following query selects every HR
value twice:
lax $.**.HR
This happens because the .**
accessor selects both the segments
array and each of its elements, while the .HR
accessor automatically unwraps arrays when using the lax mode. To avoid surprising results, we recommend using the .**
accessor only in the strict mode. The following query selects each HR
value just once:
strict $.**.HR
9.16.3.2. SQL/JSON Path Operators and Methods #
Table 9.51 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 9.51. jsonpath
Operators and Methods
Operator/Method Description Example(s) |
---|
Addition
|
Unary plus (no operation); unlike addition, this can iterate over multiple values
|
Subtraction
|
Negation; unlike subtraction, this can iterate over multiple values
|
Multiplication
|
Division
|
Modulo (remainder)
|
Type of the JSON item (see
|
Size of the JSON item (number of array elements, or 1 if not an array)
|
Approximate floating-point number converted from a JSON number or string
|
Nearest integer greater than or equal to the given number
|
Nearest integer less than or equal to the given number
|
Absolute value of the given number
|
Date/time value converted from a string
|
Date/time value converted from a string using the specified
|
The object's key-value pairs, represented as an array of objects containing three fields:
|
Note
The result type of the datetime()
and datetime(
methods can be template
)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(
method determines the result type according to the fields used in the provided template string. template
)
The datetime()
and datetime(
methods use the same parsing rules as the template
)to_timestamp
SQL function does (see Section 9.8), with three exceptions. First, these methods don't allow unmatched template patterns. Second, only the following separators are allowed in the template string: minus sign, period, solidus (slash), comma, apostrophe, semicolon, colon and space. Third, separators in the template string must exactly match the input string.
If different date/time types need to be compared, an implicit cast is applied. A date
value can be cast to timestamp
or timestamptz
, timestamp
can be cast to timestamptz
, and time
to timetz
. However, all but the first of these conversions depend on the current TimeZone setting, and thus can only be performed within timezone-aware jsonpath
functions.
Table 9.52 shows the available filter expression elements.
Table 9.52. jsonpath
Filter Expression Elements
Predicate/Value Description Example(s) |
---|
Equality comparison (this, and the other comparison operators, work on all JSON scalar values)
|
Non-equality comparison
|
Less-than comparison
|
Less-than-or-equal-to comparison
|
Greater-than comparison
|
Greater-than-or-equal-to comparison
|
JSON constant
|
JSON constant
|
JSON constant
|
Boolean AND
|
Boolean OR
|
Boolean NOT
|
Tests whether a Boolean condition is
|
Tests whether the first operand matches the regular expression given by the second operand, optionally with modifications described by a string of
|
Tests whether the second operand is an initial substring of the first operand.
|
Tests whether a path expression matches at least one SQL/JSON item. Returns
|
9.16.3.3. SQL/JSON Regular Expressions #
SQL/JSON path expressions allow matching text to a regular expression with the like_regex
filter. For example, the following SQL/JSON path query would case-insensitively match all strings in an array that start with an English vowel:
$[*] ? (@ like_regex "^[aeiou]" flag "i")
The optional flag
string may include one or more of the characters i
for case-insensitive match, m
to allow ^
and $
to match at newlines, s
to allow .
to match a newline, and q
to quote the whole pattern (reducing the behavior to a simple substring match).
The SQL/JSON standard borrows its definition for regular expressions from the LIKE_REGEX
operator, which in turn uses the XQuery standard. Postgres Pro 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:
$.* ? (@ like_regex "^\\d+$")