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, 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? (This is useful only with SQL-standard JSON path expressions, not predicate check expressions, since those always return a value.)
|
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
|
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 that can be cast to json
. 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") |
Function for testing
jsonb_populate_record_valid ----------------------------- f (1 row)
ERROR: value too long for type character(2)
jsonb_populate_record_valid ----------------------------- t (1 row)
a ---- aa (1 row) |
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. (This is useful only with SQL-standard JSON path expressions, not predicate check expressions, since those always return a value.) If the
|
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
|
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
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 parameters are the same as for
|
Returns the first JSON item returned by the JSON path for the specified JSON value, or
|
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
|
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 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. 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 .
accessor operator to descend through surrounding JSON objects, for example: key
=>
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', '$.track ? (exists(@.segments[*] ? (@.HR > 130))).segments.size()');
jsonb_path_query ------------------ 2
9.16.2.1. Deviations from the SQL Standard #
Postgres Pro's implementation of the SQL/JSON path language has the following deviations from the SQL/JSON standard.
9.16.2.1.1. Boolean Predicate Check Expressions #
As an extension to the SQL standard, a Postgres Pro path expression can be a Boolean predicate, whereas the SQL standard allows predicates only within filters. While SQL-standard path expressions return the relevant element(s) of the queried JSON value, predicate check expressions return the single three-valued result of the predicate: true
, false
, or unknown
. For example, we could write this SQL-standard filter expression:
=>
select jsonb_path_query(:'json', '$.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()
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 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. In lax mode, methods applied to an array will be executed for each value in the array. The exceptions are .type()
and .size()
, which apply to the array itself.
Table 9.50. 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)
|
Boolean value converted from a JSON boolean, number, or string
|
String value converted from a JSON boolean, number, string, or datetime
|
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
|
Big integer value converted from a JSON number or string
|
Rounded decimal value converted from a JSON number or string (
|
Integer value converted from a JSON number or string
|
Numeric value converted from a JSON number or string
|
Date/time value converted from a string
|
Date/time value converted from a string using the specified
|
Date value converted from a string
|
Time without time zone value converted from a string
|
Time without time zone value converted from a string, with fractional seconds adjusted to the given precision
|
Time with time zone value converted from a string
|
Time with time zone value converted from a string, with fractional seconds adjusted to the given precision
|
Timestamp without time zone value converted from a string
|
Timestamp without time zone value converted from a string, with fractional seconds adjusted to the given precision
|
Timestamp with time zone value converted from a string
|
Timestamp with time zone value converted from a string, with fractional seconds adjusted to the given precision
|
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. 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) |
---|
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
|
9.16.2.4. 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. 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:
$.* ? (@ like_regex "^\\d+$")
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
(an SQL/JSON path query) to a context_item
(the document). See Section 9.16.2 for more details on what the path_expression
can contain. The path_expression
can also reference variables, whose values are specified with their respective names in the PASSING
clause that is supported by each function. context_item
can be a jsonb
value or a character string that can be successfully cast to jsonb
.
Table 9.52. SQL/JSON Query Functions
Function signature Description Example(s) |
---|
Examples:
ERROR: jsonpath array subscript is out of bounds |
Examples:
ERROR: malformed array literal: "[1, 2]" DETAIL: Missing "]" after array dimensions. |
Examples:
|
Note
The context_item
expression is converted to jsonb
by an implicit cast if the expression is not already of type jsonb
. Note, however, that any parsing errors that occur during that conversion are thrown unconditionally, that is, are not handled according to the (specified or implicit) ON ERROR
clause.
Note
JSON_VALUE()
returns an SQL NULL if path_expression
returns a JSON null
, whereas JSON_QUERY()
returns the JSON null
as is.
9.16.4. JSON_TABLE #
JSON_TABLE
is an SQL/JSON function which queries JSON data and presents the results as a relational view, which can be accessed as a regular SQL table. You can use JSON_TABLE
inside the FROM
clause of a SELECT
, UPDATE
, or DELETE
and as data source in a MERGE
statement.
Taking JSON data as input, JSON_TABLE
uses a JSON path expression to extract a part of the provided data to use as a row pattern for the constructed view. Each SQL/JSON value given by the row pattern serves as source for a separate row in the constructed view.
To split the row pattern into columns, JSON_TABLE
provides the COLUMNS
clause that defines the schema of the created view. For each column, a separate JSON path expression can be specified to be evaluated against the row pattern to get an SQL/JSON value that will become the value for the specified column in a given output row.
JSON data stored at a nested level of the row pattern can be extracted using the NESTED PATH
clause. Each NESTED PATH
clause can be used to generate one or more columns using the data from a nested level of the row pattern. Those columns can be specified using a COLUMNS
clause that looks similar to the top-level COLUMNS clause. Rows constructed from NESTED COLUMNS are called child rows and are joined against the row constructed from the columns specified in the parent COLUMNS
clause to get the row in the final view. Child columns themselves may contain a NESTED PATH
specification thus allowing to extract data located at arbitrary nesting levels. Columns produced by multiple NESTED PATH
s 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. 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 | EMPTY { [ARRAY] | OBJECT } | DEFAULTexpression
} ON EMPTY ] [ { ERROR | NULL | EMPTY { [ARRAY] | OBJECT } | DEFAULTexpression
} ON ERROR ] |name
type
EXISTS [ PATHpath_expression
] [ { 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
context_item
specifies the input document to query, thepath_expression
is an SQL/JSON path expression defining the query, andjson_path_name
is an optional name for thepath_expression
. The optionalPASSING
clause provides data values for the variables mentioned in thepath_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
type
[FORMAT JSON
[ENCODINGUTF8
]] [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 specifiedtype
.Specifying
FORMAT JSON
makes it explicit that you expect the value to be a validjson
object. It only makes sense to specifyFORMAT JSON
iftype
is one ofbpchar
,bytea
,character varying
,name
,json
,jsonb
,text
, or a domain over these types.Optionally, you can specify
WRAPPER
,QUOTES
clauses to format the output andON EMPTY
andON ERROR
to handle those scenarios appropriately.Note
This clause is internally turned into and has the same semantics as
JSON_VALUE
orJSON_QUERY
. The latter if the specified type is not a scalar type or if either ofFORMAT JSON
,WRAPPER
, orQUOTES
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 specifiedtype
.The value corresponds to whether applying the
PATH
expression to the row pattern yields any values.The specified
type
should have a cast from theboolean
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 valueFALSE
.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. 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.
Note
In each variant of
json_table_column
described above, if thePATH
clause is omitted, path expression$.
is used, wherename
name
is the provided column name.-
-
AS
json_path_name
The optional
json_path_name
serves as an identifier of the providedpath_expression
. The 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.- {
ERROR
|EMPTY
}ON ERROR
The optional
ON ERROR
can be used to specify how to handle errors when evaluating the top-levelpath_expression
. UseERROR
if you want the errors to be thrown andEMPTY
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 theON 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" } ] } ] }');
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)
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)