Summary of Built-in Functions

Greenplum Database supports built-in functions and operators including analytic functions and window functions that can be used in window expressions. For information about using built-in Greenplum Database functions see, "Using Functions and Operators" in the Greenplum Database Administrator Guide.

Parent topic: Greenplum Database Reference Guide

Greenplum Database Function Types

Greenplum Database evaluates functions and operators used in SQL expressions. Some functions and operators are only allowed to run on the coordinator since they could lead to inconsistencies in Greenplum Database segment instances. This table describes the Greenplum Database Function Types.

Function Type Greenplum Support Description Comments
IMMUTABLE Yes Relies only on information directly in its argument list. Given the same argument values, always returns the same result.
STABLE Yes, in most cases Within a single table scan, returns the same result for same argument values, but results change across SQL statements. Results depend on database lookups or parameter values. current_timestamp family of functions is STABLE; values do not change within an execution.
VOLATILE Restricted Function values can change within a single table scan. For example: random(), timeofday(). Any function with side effects is volatile, even if its result is predictable. For example: setval().

In Greenplum Database, data is divided up across segments — each segment is a distinct PostgreSQL database. To prevent inconsistent or unexpected results, do not run functions classified as VOLATILE at the segment level if they contain SQL commands or modify the database in any way. For example, functions such as setval() are not allowed to run on distributed data in Greenplum Database because they can cause inconsistent data between segment instances.

To ensure data consistency, you can safely use VOLATILE and STABLE functions in statements that are evaluated on and run from the coordinator. For example, the following statements run on the coordinator (statements without a FROM clause):

SELECT setval('myseq', 201);
SELECT foo();

If a statement has a FROM clause containing a distributed table and the function in the FROM clause returns a set of rows, the statement can run on the segments:

SELECT * from foo();

Greenplum Database does not support functions that return a table reference (rangeFuncs) or functions that use the refCursor datatype.

Built-in Functions and Operators

The following table lists the categories of built-in functions and operators supported by PostgreSQL. All functions and operators are supported in Greenplum Database as in PostgreSQL with the exception of STABLE and VOLATILE functions, which are subject to the restrictions noted in Greenplum Database Function Types. See the Functions and Operators section of the PostgreSQL documentation for more information about these built-in functions and operators.

Operator/Function Category VOLATILE Functions STABLE Functions Restrictions
Logical Operators
Comparison Operators
Mathematical Functions and Operators random

setseed
String Functions and Operators All built-in conversion functions convert

pg_client_encoding
Binary String Functions and Operators
Bit String Functions and Operators
Pattern Matching
Data Type Formatting Functions to_char

to_timestamp
Date/Time Functions and Operators timeofday age

current_date

current_time

current_timestamp

localtime

localtimestamp

now
Enum Support Functions
Geometric Functions and Operators
Network Address Functions and Operators
Sequence Manipulation Functions nextval()

setval()
Conditional Expressions
Array Functions and Operators All array functions
Aggregate Functions
Subquery Expressions
Row and Array Comparisons
Set Returning Functions generate_series
System Information Functions All session information functions

All access privilege inquiry functions

All schema visibility inquiry functions

All system catalog information functions

All comment information functions

All transaction ids and snapshots
System Administration Functions set_config

pg_cancel_backend

pg_reload_conf

pg_rotate_logfile

pg_start_backup

pg_stop_backup

pg_size_pretty

pg_ls_dir

pg_read_file

pg_stat_file

current_setting

All database object size functions
> Note The function pg_column_size displays bytes required to store the value, possibly with TOAST compression.
XML Functions and function-like expressions cursor_to_xml(cursor refcursor, count int, nulls boolean, tableforest boolean, targetns text)

cursor_to_xmlschema(cursor refcursor, nulls boolean, tableforest boolean, targetns text)

database_to_xml(nulls boolean, tableforest boolean, targetns text)

database_to_xmlschema(nulls boolean, tableforest boolean, targetns text)

database_to_xml_and_xmlschema(nulls boolean, tableforest boolean, targetns text)

query_to_xml(query text, nulls boolean, tableforest boolean, targetns text)

query_to_xmlschema(query text, nulls boolean, tableforest boolean, targetns text)

query_to_xml_and_xmlschema(query text, nulls boolean, tableforest boolean, targetns text)

schema_to_xml(schema name, nulls boolean, tableforest boolean, targetns text)

schema_to_xmlschema(schema name, nulls boolean, tableforest boolean, targetns text)

schema_to_xml_and_xmlschema(schema name, nulls boolean, tableforest boolean, targetns text)

table_to_xml(tbl regclass, nulls boolean, tableforest boolean, targetns text)

table_to_xmlschema(tbl regclass, nulls boolean, tableforest boolean, targetns text)

table_to_xml_and_xmlschema(tbl regclass, nulls boolean, tableforest boolean, targetns text)

xmlagg(xml)

xmlconcat(xml[, ...])

xmlelement(name name [, xmlattributes(value [AS attname] [, ... ])] [, content, ...])

xmlexists(text, xml)

xmlforest(content [AS name] [, ...])

xml_is_well_formed(text)

xml_is_well_formed_document(text)

xml_is_well_formed_content(text)

xmlparse ( { DOCUMENT | CONTENT } value)

xpath(text, xml)

xpath(text, xml, text[])

xpath_exists(text, xml)

xpath_exists(text, xml, text[])

xmlpi(name target [, content])

xmlroot(xml, version text | no value [, standalone yes|no|no value])

xmlserialize ( { DOCUMENT | CONTENT } value AS type )

xml(text)

text(xml)

xmlcomment(xml)

xmlconcat2(xml, xml)

JSON Functions and Operators

This section describes:

  • functions and operators for processing and creating JSON data
  • the SQL/JSON path language

Processing and Creating JSON Data

Greenplum Database includes built-in functions and operators that create and manipulate JSON data:

JSON Operators

This table describes the operators that are available for use with the json and jsonb data types.

Operator Right Operand Type Return Type Description Example Example Result
-> int json or jsonb Get the JSON array element (indexed from zero, negative integers count from the end). '[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json->2 {"c":"baz"}
-> text json or jsonb Get the JSON object field by key. '{"a": {"b":"foo"}}'::json->'a' {"b":"foo"}
->> int text Get the JSON array element as text. '[1,2,3]'::json->>2 3
->> text text Get the JSON object field as text. '{"a":1,"b":2}'::json->>'b' 2
#> text[] json or jsonb Get the JSON object at the specified path. '{"a": {"b":{"c": "foo"}}}'::json#>'{a,b}' {"c": "foo"}
#>> text[] text Get the JSON object at the specified path as text. '{"a":[1,2,3],"b":[4,5,6]}'::json#>>'{a,2}' 3
Note

There are parallel variants of these operators for both the json and jsonb data types. The field/element/path extraction operators return the same data type as their left-hand input (either json or jsonb), except for those specified as returning text, which coerce the value to text. 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 element exists. The field/element/path extraction operators that accept integer JSON array subscripts all support negative subscripting from the end of arrays.

These standard comparison operators are available for jsonb, but not for json. They follow the ordering rules for B-tree operations outlined at jsonb Indexing.

Operator Description
< less than
> greater than
<= less than or equal to
>= greater than or equal to
= equal
<> or != not equal
Note

The != operator is converted to <> in the parser stage. It is not possible to implement != and <> operators that do different things.

Operators that require the jsonb data type as the left operand are described in the following table. Many of these operators can be indexed by jsonb operator classes. For a full description of jsonb containment and existence semantics, refer to jsonb Containment and Existence. jsonb Indexing describes how these operators can be used to effectively index jsonb data.

Operator Right Operand Type Description Example
@> jsonb Does the left JSON value contain the right JSON path/value entries at the top level? '{"a":1, "b":2}'::jsonb @> '{"b":2}'::jsonb
<@ jsonb Are the left JSON path/value enries contained at the top level within the right JSON value? '{"b":2}'::jsonb <@ '{"a":1, "b":2}'::jsonb
? text Does the string exist as a top-level key within the JSON value? '{"a":1, "b":2}'::jsonb ? 'b'
?| text[] Do any of these array strings exist as a top-level key? '{"a":1, "b":2, "c":3}'::jsonb ?| array['b', 'c']
?& text[] Do all of these array strings exist as top-level keys? '["a", "b"]'::jsonb ?& array['a', 'b']
|| jsonb Concatenate two jsonb values into a new jsonb value. '["a", "b"]'::jsonb || '["c", "d"]'::jsonb
- text Delete key/value pair or string elements from left operand. Key/value pairs are matched based on their key value. '{"a": "b"}'::jsonb - 'a'
- text[] Delete multiple key/value pairs or string elements from left operand. Key/value pairs are matched based on their key value. '{"a": "b", "c": "d"}'::jsonb - '{a,c}'::text[]
- integer Delete the array element with specified index (Negative integers count from the end). Throws an error if top level container is not an array. '["a", "b"]'::jsonb - 1
#- text[] Delete the field or element with specified path (for JSON arrays, negative integers count from the end) '["a", {"b":1}]'::jsonb #- '{1,b}'
@? jsonpath Does JSON path return any item for the specified JSON value? '{"a":[1,2,3,4,5]}'::jsonb @? '$.a[*] ? (@ > 2)'
@@ jsonpath Returns the result of JSON path predicate check for the specified JSON value. Only the first item of the result is taken into account. If the result is not Boolean, then null is returned. '{"a":[1,2,3,4,5]}'::jsonb @@ '$.a[*] > 2'
Note

The || operator concatenates two JSON objects by generating an object containing the union of their keys, taking the second object's value when there are duplicate keys. All other cases produce a JSON array: first, any non-array input is converted into a single-element array, and then the two arrays are concatenated. It does not operate recursively; only the top-level array or object structure is merged.

Note

The @? and @@ operators suppress the following errors: lacking object field or array element, unexpected JSON item type, and numeric errors. This behavior might be helpful while searching over JSON document collections of varying structure.

JSON Creation Functions

This table describes the functions that create json and jsonb data type values. (There are no equivalent functions for jsonb for row_to_json() and array_to_json(). However, the to_jsonb() function supplies much the same functionality as these functions would.)

Function Description Example Example Result
to_json(anyelement)
to_jsonb(anyelement)
Returns the value as a json or jsonb object. Arrays and composites are converted (recursively) to arrays and objects; otherwise, if the input contains a cast from the type to json, the cast function is used to perform the conversion; otherwise, a scalar value is produced. For any scalar type other than a number, a Boolean, or a null value, the text representation will be used, in such a fashion that it is a valid json or jsonb value. to_json('Fred said "Hi."'::text) "Fred said \"Hi.\""
array_to_json(anyarray [, pretty_bool]) Returns the array as a JSON array. A multidimensional array becomes a JSON array of arrays. Line feeds will be added between dimension-1 elements if pretty_bool is true. array_to_json('{{1,5},{99,100}}'::int[]) [[1,5],[99,100]]
row_to_json(record [, pretty_bool]) Returns the row as a JSON object. Line feeds will be added between level-1 elements if pretty_bool is true. row_to_json(row(1,'foo')) {"f1":1,"f2":"foo"}
json_build_array(VARIADIC "any")
jsonb_build_array(VARIADIC "any")
Builds a possibly-heterogeneously-typed JSON array out of a VARIADIC argument list. json_build_array(1,2,'3',4,5) [1, 2, "3", 4, 5]
json_build_object(VARIADIC "any")
jsonb_build_object(VARIADIC "any")
Builds a JSON object out of a VARIADIC argument list. The argument list is taken in order and converted to a set of key/value pairs. json_build_object('foo',1,'bar',2) {"foo": 1, "bar": 2}
json_object(text[])
jsonb_object(text[])
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. json_object('{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[])
jsonb_object(keys text[], values text[])
Builds a JSON object out of a text array. This form of json_object takes keys and values pairwise from two separate arrays. In all other respects it is identical to the one-argument form. json_object('{a, b}', '{1,2}') {"a": "1", "b": "2"}
Note

array_to_json() and row_to_json() have the same behavior as to_json() except for offering a pretty-printing option. The behavior described for to_json() likewise applies to each individual value converted by the other JSON creation functions.

Note

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.

JSON Aggregate Functions

This table shows the functions that aggregate records to an array of JSON objects and pairs of values to a JSON object

Function Argument Types Return Type Description
json_agg(record)
jsonb_agg(record)
record json Aggregates records as a JSON array of objects.
json_object_agg(name, value)
jsonb_object_agg(name, value)
("any", "any") json Aggregates name/value pairs as a JSON object.

JSON Processing Functions

This table shows the functions that are available for processing json and jsonb values.

Many of these processing functions and operators convert Unicode escapes in JSON strings to the appropriate single character. This is a not an issue if the input data type is jsonb, because the conversion was already done. However, for json data type input, this might result in an error being thrown as described in About JSON Data.

Table 8. JSON Processing Functions
Function Return Type Description Example Example Result
json_array_length(json)

jsonb_array_length(jsonb)

int Returns the number of elements in the outermost JSON array. json_array_length('[1,2,3,{"f1":1,"f2":[5,6]},4]') 5
json_each(json)

jsonb_each(jsonb)

setof key text, value json

setof key text, value jsonb

Expands the outermost 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)

jsonb_each_text(jsonb)

setof key text, value text Expands the outermost 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[])

jsonb_extract_path(from_json jsonb, VARIADIC path_elems text[])

json

jsonb

Returns the JSON value pointed to by path_elems (equivalent to #> operator). json_extract_path('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}','f4') {"f5":99,"f6":"foo"}
json_extract_path_text(from_json json, VARIADIC path_elems text[])

jsonb_extract_path_text(from_json jsonb, VARIADIC path_elems text[])

text Returns the JSON value pointed to by path_elems as text (equivalent to #>> operator). json_extract_path_text('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}','f4', 'f6') foo
json_object_keys(json)

jsonb_object_keys(jsonb)

setof text Returns set of keys in the outermost JSON object. json_object_keys('{"f1":"abc","f2":{"f3":"a", "f4":"b"}}')
 json_object_keys
------------------
 f1
 f2
json_populate_record(base anyelement, from_json json)

jsonb_populate_record(base anyelement, from_json jsonb)

anyelement Expands the object in from_json to a row whose columns match the record type defined by base. See the Notes. select * from json_populate_record(null::myrowtype, '{"a": 1, "b": ["2", "a b"], "c": {"d": 4, "e": "a b c"}}')
 a |   b       |      c
---+-----------+-------------
 1 | {2,"a b"} | (4,"a b c")
json_populate_recordset(base anyelement, from_json json)

jsonb_populate_recordset(base anyelement, from_json jsonb)

setof anyelement Expands the outermost array of objects in from_json to a set of rows whose columns match the record type defined by base. See the Notes. select * from json_populate_recordset(null::myrowtype, '[{"a":1,"b":2},{"a":3,"b":4}]')
 a | b
---+---
 1 | 2
 3 | 4
json_array_elements(json)

jsonb_array_elements(jsonb)

setof json

setof jsonb

Expands a JSON array to a set of JSON values. select * from json_array_elements('[1,true, [2,false]]')
   value
-----------
 1
 true
 [2,false]
json_array_elements_text(json)

jsonb_array_elements_text(jsonb)

setof text Expands a JSON array to a set of text values. select * from json_array_elements_text('["foo", "bar"]')
   value
-----------
 foo
 bar
json_typeof(json)

jsonb_typeof(jsonb)

text Returns the type of the outermost JSON value as a text string. Possible types are object, array, string, number, boolean, and null. json_typeof('-123.4') number
json_to_record(json)

jsonb_to_record(jsonb)

record Builds an arbitrary record from a JSON object. See the Notes.

As with all functions returning record, the caller must explicitly define the structure of the record with an AS clause.

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)

jsonb_to_recordset(jsonb)

setof record Builds an arbitrary set of records from a JSON array of objects See the Notes.

As with all functions returning record, the caller must explicitly define the structure of the record with an AS clause.

select * from json_to_recordset('[{"a":1,"b":"foo"},{"a":"2","c":"bar"}]') as x(a int, b text);
 a |  b
---+-----
 1 | foo
 2 |

json_strip_nulls(from_json json)

jsonb_strip_nulls(from_json jsonb)

json

jsonb

Returns from_json with all object fields that have null values omitted. Other null values are untouched. json_strip_nulls('[{"f1":1,"f2":null},2,null,3]') [{"f1":1},2,null,3]

jsonb_set(target jsonb, path text[], new_value jsonb [, create_missing boolean])

jsonb

Returns target with the section designated by path replaced by new_value, or with new_value added if create_missing is true (default is true) and the item designated by path does not exist. As with the path oriented operators, negative integers that appear in path count from the end of JSON arrays.

jsonb_set('[{"f1":1,"f2":null},2,null,3]', '{0,f1}','[2,3,4]', false)

jsonb_set('[{"f1":1,"f2":null},2]', '{0,f3}','[2,3,4]')

[{"f1":[2,3,4],"f2":null},2,null,3]

[{"f1": 1, "f2": null, "f3": [2, 3, 4]}, 2]

jsonb_insert(target jsonb, path text[], new_value jsonb [, insert_after boolean])

jsonb

Returns target with new_value inserted. If target section designated by path is in a JSONB array, new_value will be inserted before target or after if insert_after is true (default is false). If target section designated by path is in JSONB object, new_value will be inserted only if target does not exist. As with the path oriented operators, negative integers that appear in path count from the end of JSON arrays.

jsonb_insert('{"a": [0,1,2]}', '{a, 1}', '"new_value"')

jsonb_insert('{"a": [0,1,2]}', '{a, 1}', '"new_value"', true)

{"a": [0, "new_value", 1, 2]}

{"a": [0, 1, "new_value", 2]}

jsonb_pretty(from_json jsonb)

text

Returns from_json as indented JSON text. jsonb_pretty('[{"f1":1,"f2":null},2,null,3]')
[
    {
        "f1": 1,
        "f2": null
    },
    2,
    null,
    3
]

jsonb_path_exists(target jsonb, path jsonpath [, vars jsonb [, silent bool]])

boolean Checks whether JSON path returns any item for the specified JSON value.

jsonb_path_exists('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2,"max":4}')

true

jsonb_path_match(target jsonb, path jsonpath [, vars jsonb [, silent bool]])

boolean Returns the result of JSON path predicate check for the specified JSON value. Only the first item of the result is taken into account. If the result is not Boolean, then null is returned.

jsonb_path_match('{"a":[1,2,3,4,5]}', 'exists($.a[*] ? (@ >= $min && @ <= $max))', '{"min":2,"max":4}')

true

jsonb_path_query(target jsonb, path jsonpath [, vars jsonb [, silent bool]])

setof jsonb Gets all JSON items returned by JSON path for the specified JSON value.

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 bool]])

jsonb Gets all JSON items returned by JSON path for the specified JSON value and wraps result into an array.

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 bool]])

jsonb Gets the first JSON item returned by JSON path for the specified JSON value. Returns NULL on no results.

jsonb_path_query_first('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2,"max":4}')

2

Notes:

  1. The functions json[b]_populate_record(), json[b]_populate_recordset(), json[b]_to_record() and json[b]_to_recordset() operate on a JSON object, or array of objects, and extract the values associated with keys whose names match column names of the output row type. Object fields that do not correspond to any output column name are ignored, and output columns that do not match any object field will be filled with nulls. 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 a 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 literal, 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 examples for these functions use constants, the typical use would be to reference a table in the FROM clause and use one of its json or jsonb columns as an argument to the function. Extracted key values can then be referenced in other parts of the query, like WHERE clauses and target lists. Extracting multiple values in this way can improve performance over extracting them separately with per-key operators.

  2. All the items of the path parameter of jsonb_set() as well as jsonb_insert() except the last item must be present in the target. If create_missing is false, all items of the path parameter of jsonb_set() must be present. If these conditions are not met the target is returned unchanged.

    If the last path item is an object key, it will be created if it is absent and given the new value. If the last path item is an array index, if it is positive the item to set is found by counting from the left, and if negative by counting from the right - -1 designates the rightmost element, and so on. If the item is out of the range -array_length .. array_length -1, and create_missing is true, the new value is added at the beginning of the array if the item is negative, and at the end of the array if it is positive.

  3. The json_typeof function's null return value should not be confused with a SQL NULL. While calling json_typeof('null'::json) will return null, calling json_typeof(NULL::json) will return a SQL NULL.

  4. If the argument to json_strip_nulls() contains duplicate field names in any object, the result could be semantically somewhat different, depending on the order in which they occur. This is not an issue for jsonb_strip_nulls() since jsonb values never have duplicate object field names.

  5. The jsonb_path_exists(), jsonb_path_match(), jsonb_path_query(), jsonb_path_query_array(), and jsonb_path_query_first() functions have optional vars and silent arguments.

    If the vars argument is specified, it provides an object containing named variables to be substituted into a jsonpath expression.

    If the silent argument is specified and has the true value, these functions suppress the same errors as the @? and @@ operators.

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 Greenplum Database, path expressions are implemented as the jsonpath data type and can use any elements described in jsonpath Type.

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 SQL/JSON item is returned. Path expressions are written in the SQL/JSON path language and can also include arithmetic expressions and functions. Query functions treat the provided expression as a text string, so it must be enclosed in single quotes.

A path expression consists of a sequence of elements allowed by the jsonpath data type. The path expression is evaluated from left to right, but you can use parentheses to change the order of operations. If the evaluation is successful, a sequence of SQL/JSON items (SQL/JSON sequence) is produced, and the evaluation result is returned to the JSON query function that completes the specified computation.

To refer to the JSON data to be queried (the context item), use the $ sign 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 the content of context item. Each operator that follows deals with the result of the previous evaluation step.

For example, suppose you have some JSON data from a GPS tracker that you would like to parse, such as:

{
  "track": {
    "segments": [
      {
        "location":   [ 47.763, 13.4034 ],
        "start time": "2018-10-14 10:05:14",
        "HR": 73
      },
      {
        "location":   [ 47.706, 13.2635 ],
        "start time": "2018-10-14 10:39:21",
        "HR": 135
      }
    ]
  }
}

To retrieve the available track segments, you need to use the .<key> accessor operator for all the preceding JSON objects:

'$.track.segments'

If the item to retrieve is an element of an array, you have to unnest this array using the [*] operator. For example, the following path will return 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. Note that the SQL/JSON arrays 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 SQL/JSON Path Operators and Methods below. Each method name must be preceded by a dot. For example, you can get an array size:

'$.track.segments.size()'

For more examples of using jsonpath operators and methods within path expressions, see SQL/JSON Path Operators and Methods below.

When defining the path, you can also use one or more filter expressions that work similar 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 specified right after the path evaluation step to which they are applied. The result of this 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 filter expressions return true.

Functions and operators that can be used in filter expressions are listed in jsonpath Filter Expression Elements. The path evaluation result to be filtered is denoted by the @ variable. To refer to a JSON element stored at a lower nesting level, add one or more accessor operators after @.

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 time of segments with such values instead, you have to filter out irrelevant segments before returning the start time, 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 on the same nesting level, if required. For example, the following expression selects 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.

Deviations from Standard

Greenplum Database's implementation of SQL/JSON path language has the following deviations from the SQL/JSON standard:

  • .datetime() item method is not implemented yet mainly because immutable jsonpath functions and operators cannot reference session timezone, which is used in some datetime operations. Datetime support will be added to jsonpath in future versions of Greenplum Database.

  • A path expression can be a Boolean predicate, although the SQL/JSON standard allows predicates only in filters. This is necessary for implementation of the @@ operator. For example, the following jsonpath expression is valid in Greenplum Database:

    '$.track.segments[*].HR < 70'
    
  • There are minor differences in the interpretation of regular expression patterns used in like_regex filters as described in Regular Expressions.

Strict And Lax Modes

When you query JSON data, the path expression may not match the actual JSON data structure. An attempt to access a non-existent member of an object or element of an array results in a structural error. SQL/JSON path expressions have two modes of handling structural errors:

  • lax (default) — the path engine implicitly adapts the queried data to the specified path. Any remaining structural errors are suppressed and converted to empty SQL/JSON sequences.

  • strict — if a structural error occurs, an error is raised.

The lax mode facilitates matching of a JSON document structure and path expression if the JSON data does not conform to the expected schema. If an operand does not match the requirements of a particular operation, it can be automatically wrapped as an SQL/JSON array or unwrapped by converting its elements into an SQL/JSON sequence before performing this operation. Besides, comparison operators automatically unwrap their operands in the lax mode, so you can compare SQL/JSON arrays out-of-the-box. An array of size 1 is considered equal to its sole element. Automatic unwrapping is not performed only when:

  • The path expression contains type() or size() methods that return the type and the number of elements in the array, respectively.

  • The queried JSON data contain nested arrays. In this case, only the outermost array is unwrapped, while all the inner arrays remain unchanged. Thus, implicit unwrapping can only go one level down within each path evaluation step.

For example, when querying the GPS data listed above, you can abstract from the fact that it stores an array of segments when using the lax mode:

'lax $.track.segments.location'

In the strict mode, the specified path must exactly match the structure of the queried JSON document to return an SQL/JSON item, so using this path expression will cause an error. To get the same result as in the lax mode, you have to explicitly unwrap the segments array:

'strict $.track.segments[*].location'

The .** accessor can lead to surprising results when using the lax mode. For instance, the following query selects every HR value twice:

lax $.**.HR

This happens because the .** accessor selects both the segments array and each of its elements, while the .HR accessor automatically unwraps arrays when using the lax mode. To avoid surprising results, we recommend using the .** accessor only in the strict mode. The following query selects each HR value just once:

strict $.**.HR

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 starts 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. Greenplum Database does not currently support the LIKE_REGEX operator. Therefore, the like_regex filter is implemented using the POSIX regular expression engine as described in POSIX Regular Expressions. This leads to various minor discrepancies from standard SQL/JSON behavior which are catalogued in Differences From XQuery (LIKE_REGEX). 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 jsonpath Type. 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:

$.* ? (@ like_regex "^\\d+$")

SQL/JSON Path Operators and Methods

The following table describes the operators and methods available in jsonpath:

Operator/Method Description Example JSON Example Query Result
+ (unary) Plus operator that iterates over the SQL/JSON sequence {"x": [2.85, -14.7, -9.4]} + $.x.floor() 2, -15, -10
- (unary) Minus operator that iterates over the SQL/JSON sequence {"x": [2.85, -14.7, -9.4]} - $.x.floor() -2, 15, 10
+ (binary) Addition [2] 2 + $[0] 4
- (binary) Subtraction [2] 4 - $[0] 2
* Multiplication [4] 2 * $[0] 8
/ Division [8] $[0] / 2 4
% Modulus [32] $[0] % 10 2
type() Type of the SQL/JSON item [1, "2", {}] $[*].type() "number", "string", "object"
size() Size of the SQL/JSON item {"m": [11, 15]} $.m.size() 2
double() Approximate floating-point number converted from an SQL/JSON number or a string {"len": "1.9"} $.len.double() * 2 3.8
ceiling() Nearest integer greater than or equal to the SQL/JSON number {"h": 1.3} $.h.ceiling() 2
floor() Nearest integer less than or equal to the SQL/JSON number {"h": 1.3} $.h.floor() 1
abs() Absolute value of the SQL/JSON number {"z": -0.3} $.z.abs() 0.3
keyvalue() Sequence of object's key-value pairs represented as array of items containing three fields ("key", "value", and "id"). "id" is a unique identifier of the object key-value pair belongs to. {"x": "20", "y": 32} $.keyvalue() {"key": "x", "value": "20", "id": 0}, {"key": "y", "value": 32, "id": 0}

SQL/JSON Filter Expression Elements

The following table describes the available filter expressions elements for jsonpath:

Value/Predicate Description Example JSON Example Query Result
== Equality operator [1, 2, 1, 3] $[*] ? (@ == 1) 1, 1
!= Non-equality operator [1, 2, 1, 3] $[*] ? (@ != 1) 2, 3
<> Non-equality operator (same as !=) [1, 2, 1, 3] $[*] ? (@ <> 1) 2, 3
< Less-than operator [1, 2, 3] $[*] ? (@ < 2) 1
<= Less-than-or-equal-to operator [1, 2, 3] $[*] ? (@ <= 2) 1, 2
> Greater-than operator [1, 2, 3] $[*] ? (@ > 2) 3
>= Greater-than-or-equal-to operator [1, 2, 3] $[*] ? (@ >= 2) 2, 3
true Value used to perform comparison with JSON true literal [{"name": "John", "parent": false}, {"name": "Chris", "parent": true}] $[*] ? (@.parent == true) {"name": "Chris", "parent": true}
false Value used to perform comparison with JSON false literal [{"name": "John", "parent": false}, {"name": "Chris", "parent": true}] $[*] ? (@.parent == false) {"name": "John", "parent": false}
null Value used to perform comparison with JSON null value [{"name": "Mary", "job": null}, {"name": "Michael", "job": "driver"}] $[*] ? (@.job == null) .name "Mary"
&& Boolean AND [1, 3, 7] $[*] ? (@ > 1 && @ < 5) 3
|| Boolean OR [1, 3, 7] $[*] ? (@ < 1 || @ > 5) 7
! Boolean NOT [1, 3, 7] $[*] ? (!(@ < 5)) 7
like_regex Tests whether the first operand matches the regular expression given by the second operand, optionally with modifications described by a string of flag characters. ["abc", "abd", "aBdC", "abdacb", "babc"] $[*] ? (@ like_regex "^ab.*c" flag "i") "abc", "aBdC", "abdacb"
starts with Tests whether the second operand is an initial substring of the first operand ["John Smith", "Mary Stone", "Bob Johnson"] $[*] ? (@ starts with "John") "John Smith"
exists Tests whether a path expression matches at least one SQL/JSON item {"x": [1, 2], "y": [2, 4]} strict $.* ? (exists (@ ? (@[*] > 2))) 2, 4
is unknown Tests whether a Boolean condition is unknown [-1, 2, 7, "infinity"] $[*] ? ((@ > 0) is unknown) "infinity"

Window Functions

The following are Greenplum Database built-in window functions. All window functions are immutable. For more information about window functions, see "Window Expressions" in the Greenplum Database Administrator Guide.

Function Return Type Full Syntax Description
cume_dist() double precision CUME_DIST() OVER ( [PARTITION BY expr ] ORDER BY expr ) Calculates the cumulative distribution of a value in a group of values. Rows with equal values always evaluate to the same cumulative distribution value.
dense_rank() bigint DENSE_RANK () OVER ( [PARTITION BY expr ] ORDER BY expr ) Computes the rank of a row in an ordered group of rows without skipping rank values. Rows with equal values are given the same rank value.
first_value(*expr*) same as input expr type FIRST_VALUE( expr ) OVER ( [PARTITION BY expr ] ORDER BY expr [ROWS|RANGE frame_expr ] ) Returns the first value in an ordered set of values.
lag(*expr* [,*offset*] [,*default*]) same as input expr type LAG( expr [, offset ] [, default ]) OVER ( [PARTITION BY expr ] ORDER BY expr ) Provides access to more than one row of the same table without doing a self join. Given a series of rows returned from a query and a position of the cursor, LAG provides access to a row at a given physical offset prior to that position. The default offset is 1. default sets the value that is returned if the offset goes beyond the scope of the window. If default is not specified, the default value is null.
last_value(*expr*) same as input expr type LAST_VALUE(*expr*) OVER ( [PARTITION BY *expr*] ORDER BY *expr* [ROWS|RANGE *frame\_expr*] ) Returns the last value in an ordered set of values.
lead(*expr* [,*offset*] [,*default*]) same as input expr type LEAD(*expr*[,*offset*] [,*expr**default*]) OVER ( [PARTITION BY *expr*] ORDER BY *expr* ) Provides access to more than one row of the same table without doing a self join. Given a series of rows returned from a query and a position of the cursor, lead provides access to a row at a given physical offset after that position. If offset is not specified, the default offset is 1. default sets the value that is returned if the offset goes beyond the scope of the window. If default is not specified, the default value is null.
ntile(*expr*) bigint NTILE(*expr*) OVER ( [PARTITION BY *expr*] ORDER BY *expr* ) Divides an ordered data set into a number of buckets (as defined by expr) and assigns a bucket number to each row.
percent_rank() double precision PERCENT_RANK () OVER ( [PARTITION BY *expr*] ORDER BY *expr*) Calculates the rank of a hypothetical row R minus 1, divided by 1 less than the number of rows being evaluated (within a window partition).
rank() bigint RANK () OVER ( [PARTITION BY *expr*] ORDER BY *expr*) Calculates the rank of a row in an ordered group of values. Rows with equal values for the ranking criteria receive the same rank. The number of tied rows are added to the rank number to calculate the next rank value. Ranks may not be consecutive numbers in this case.
row_number() bigint ROW_NUMBER () OVER ( [PARTITION BY *expr*] ORDER BY *expr*) Assigns a unique number to each row to which it is applied (either each row in a window partition or each row of the query).

Advanced Aggregate Functions

The following built-in advanced analytic functions are Greenplum extensions of the PostgreSQL database. Analytic functions are immutable.

Note

The Greenplum MADlib Extension for Analytics provides additional advanced functions to perform statistical analysis and machine learning with Greenplum Database data. See MADlib Extension for Analytics.

Table 10. Advanced Aggregate Functions
Function Return Type Full Syntax Description
gp_array_agg (anyarray) same as the argument data type gp_array_agg (anyarray)

Example:

CREATE TABLE intarr_tbl (a int, arr int[]);
INSERT INTO intarr_tbl SELECT i, array[i, i] FROM generate_series(1, 5)i;
INSERT INTO intarr_tbl SELECT 6, '{6, NULL}'::int[];
INSERT INTO intarr_tbl SELECT 8, '{NULL, 7}'::int[];
SELECT gp_array_agg(arr ORDER BY arr) FROM intarr_tbl; 
A parallel version of array_agg(anyarray). Concatenates input arrays to create an array of one higher dimension. The inputs must all have the same dimensions, and they cannot be empty or null.
gp_array_agg (anynonarray) array of the argument type gp_array_agg (anynonarray)

Example:

CREATE TABLE table1(a int4, b int4);
INSERT INTO table1 VALUES (4,5), (2,1), (1,3), (3,null), (3,7);
SELECT gp_array_agg(a ORDER BY b NULLS FIRST) FROM table1; 
An parallel version of array_agg(anynonarray). Creates an array by concatenating input values, including nulls.
MEDIAN (expr) timestamp, timestamptz, interval, float MEDIAN (expression)

Example:

SELECT department_id, MEDIAN(salary) 
FROM employees 
GROUP BY department_id; 
Can take a two-dimensional array as input. Treats such arrays as matrices.
PERCENTILE_CONT (expr) WITHIN GROUP (ORDER BY expr [DESC/ASC]) timestamp, timestamptz, interval, float PERCENTILE_CONT(percentage) WITHIN GROUP (ORDER BY expression)

Example:

SELECT department_id,
PERCENTILE_CONT (0.5) WITHIN GROUP (ORDER BY salary DESC)
"Median_cont"; 
FROM employees GROUP BY department_id;
Performs an inverse distribution function that assumes a continuous distribution model. It takes a percentile value and a sort specification and returns the same datatype as the numeric datatype of the argument. This returned value is a computed result after performing linear interpolation. Null are ignored in this calculation.
PERCENTILE_DISC (expr) WITHIN GROUP (ORDER BY expr [DESC/ASC]) timestamp, timestamptz, interval, float PERCENTILE_DISC(percentage) WITHIN GROUP (ORDER BY expression)

Example:

SELECT department_id, 
PERCENTILE_DISC (0.5) WITHIN GROUP (ORDER BY salary DESC)
"Median_desc"; 
FROM employees GROUP BY department_id;
Performs an inverse distribution function that assumes a discrete distribution model. It takes a percentile value and a sort specification. This returned value is an element from the set. Null are ignored in this calculation.
sum(array[]) smallint[]int[], bigint[], float[] sum(array[[1,2],[3,4]])

Example:

CREATE TABLE mymatrix (myvalue int[]);
INSERT INTO mymatrix VALUES (array[[1,2],[3,4]]);
INSERT INTO mymatrix VALUES (array[[0,1],[1,0]]);
SELECT sum(myvalue) FROM mymatrix;
 sum 
---------------
 {{1,3},{4,4}}
Performs matrix summation. Can take as input a two-dimensional array that is treated as a matrix.
pivot_sum (label[], label, expr) int[], bigint[], float[] pivot_sum( array['A1','A2'], attr, value) A pivot aggregation using sum to resolve duplicate entries.
unnest (array[]) set of anyelement unnest( array['one', 'row', 'per', 'item']) Transforms a one dimensional array into rows. Returns a set of anyelement, a polymorphic pseudotype in PostgreSQL.

Text Search Functions and Operators

The following tables summarize the functions and operators that are provided for full text searching. See Using Full Text Search for a detailed explanation of Greenplum Database's text search facility.

Operator Description Example Result
@@ tsvector matches tsquery ? to_tsvector('fat cats ate rats') @@ to_tsquery('cat & rat') t
@@@ deprecated synonym for @@ to_tsvector('fat cats ate rats') @@@ to_tsquery('cat & rat') t
|| concatenatetsvectors 'a:1 b:2'::tsvector || 'c:1 d:2 b:3'::tsvector 'a':1 'b':2,5 'c':3 'd':4
&& AND tsquerys together 'fat | rat'::tsquery && 'cat'::tsquery ( 'fat' | 'rat' ) & 'cat'
|| OR tsquerys together 'fat | rat'::tsquery || 'cat'::tsquery ( 'fat' | 'rat' ) | 'cat'
!! negate atsquery !! 'cat'::tsquery !'cat'
@> tsquery contains another ? 'cat'::tsquery @> 'cat & rat'::tsquery f
<@ tsquery is contained in ? 'cat'::tsquery <@ 'cat & rat'::tsquery t
Note

The tsquery containment operators consider only the lexemes listed in the two queries, ignoring the combining operators.

In addition to the operators shown in the table, the ordinary B-tree comparison operators (=, <, etc) are defined for types tsvector and tsquery. These are not very useful for text searching but allow, for example, unique indexes to be built on columns of these types.

Function Return Type Description Example Result
get_current_ts_config() regconfig get default text search configuration get_current_ts_config() english
length(tsvector) integer number of lexemes in tsvector length('fat:2,4 cat:3 rat:5A'::tsvector) 3
numnode(tsquery) integer number of lexemes plus operators in tsquery numnode('(fat & rat) | cat'::tsquery) 5
plainto_tsquery([ config regconfig , ] querytext) tsquery produce tsquery ignoring punctuation plainto_tsquery('english', 'The Fat Rats') 'fat' & 'rat'
querytree(query tsquery) text get indexable part of a tsquery querytree('foo & ! bar'::tsquery) 'foo'
setweight(tsvector, "char") tsvector assign weight to each element of tsvector setweight('fat:2,4 cat:3 rat:5B'::tsvector, 'A') 'cat':3A 'fat':2A,4A 'rat':5A
strip(tsvector) tsvector remove positions and weights from tsvector strip('fat:2,4 cat:3 rat:5A'::tsvector) 'cat' 'fat' 'rat'
to_tsquery([ config regconfig , ] query text) tsquery normalize words and convert to tsquery to_tsquery('english', 'The & Fat & Rats') 'fat' & 'rat'
to_tsvector([ config regconfig , ] documenttext) tsvector reduce document text to tsvector to_tsvector('english', 'The Fat Rats') 'fat':2 'rat':3
ts_headline([ config regconfig, ] documenttext, query tsquery [, options text ]) text display a query match ts_headline('x y z', 'z'::tsquery) x y <b>z</b>
ts_rank([ weights float4[], ] vector tsvector,query tsquery [, normalization integer ]) float4 rank document for query ts_rank(textsearch, query) 0.818
ts_rank_cd([ weights float4[], ] vectortsvector, query tsquery [, normalizationinteger ]) float4 rank document for query using cover density ts_rank_cd('{0.1, 0.2, 0.4, 1.0}', textsearch, query) 2.01317
ts_rewrite(query tsquery, target tsquery,substitute tsquery) tsquery replace target with substitute within query ts_rewrite('a & b'::tsquery, 'a'::tsquery, 'foo|bar'::tsquery) 'b' & ( 'foo' | 'bar' )
ts_rewrite(query tsquery, select text) tsquery replace using targets and substitutes from a SELECTcommand SELECT ts_rewrite('a & b'::tsquery, 'SELECT t,s FROM aliases') 'b' & ( 'foo' | 'bar' )
tsvector_update_trigger() trigger trigger function for automatic tsvector column update CREATE TRIGGER ... tsvector_update_trigger(tsvcol, 'pg_catalog.swedish', title, body)
tsvector_update_trigger_column() trigger trigger function for automatic tsvector column update CREATE TRIGGER ... tsvector_update_trigger_column(tsvcol, configcol, title, body)
Note

All the text search functions that accept an optional regconfig argument will use the configuration specified by default_text_search_config when that argument is omitted.

The functions in the following table are listed separately because they are not usually used in everyday text searching operations. They are helpful for development and debugging of new text search configurations.

Function Return Type Description Example Result
ts_debug([ *config* regconfig, ] *document* text, OUT *alias* text, OUT *description* text, OUT *token* text, OUT *dictionaries* regdictionary[], OUT *dictionary* regdictionary, OUT *lexemes* text[]) setof record test a configuration ts_debug('english', 'The Brightest supernovaes') (asciiword,"Word, all ASCII",The,{english_stem},english_stem,{}) ...
ts_lexize(*dict* regdictionary, *token* text) text[] test a dictionary ts_lexize('english_stem', 'stars') {star}
ts_parse(*parser\_name* text, *document* text, OUT *tokid* integer, OUT *token* text) setof record test a parser ts_parse('default', 'foo - bar') (1,foo) ...
ts_parse(*parser\_oid* oid, *document* text, OUT *tokid* integer, OUT *token* text) setof record test a parser ts_parse(3722, 'foo - bar') (1,foo) ...
ts_token_type(*parser\_name* text, OUT *tokid* integer, OUT *alias* text, OUT description text) setof record get token types defined by parser ts_token_type('default') (1,asciiword,"Word, all ASCII") ...
ts_token_type(*parser\_oid* oid, OUT *tokid* integer, OUT *alias* text, OUT *description* text) setof record get token types defined by parser ts_token_type(3722) (1,asciiword,"Word, all ASCII") ...
ts_stat(*sqlquery* text, [ *weights* text, ] OUT *word* text, OUT *ndocinteger*, OUT *nentry* integer) setof record get statistics of a tsvectorcolumn ts_stat('SELECT vector from apod') (foo,10,15) ...

Range Functions and Operators

See Range Types for an overview of range types.

The following table shows the operators available for range types.

Operator Description Example Result
= equal int4range(1,5) = '[1,4]'::int4range t
<> not equal numrange(1.1,2.2) <> numrange(1.1,2.3) t
< less than int4range(1,10) < int4range(2,3) t
> greater than int4range(1,10) > int4range(1,5) t
<= less than or equal numrange(1.1,2.2) <= numrange(1.1,2.2) t
>= greater than or equal numrange(1.1,2.2) >= numrange(1.1,2.0) t
@> contains range int4range(2,4) @> int4range(2,3) t
@> contains element '[2011-01-01,2011-03-01)'::tsrange @> '2011-01-10'::timestamp t
<@ range is contained by int4range(2,4) <@ int4range(1,7) t
<@ element is contained by 42 <@ int4range(1,7) f
&& overlap (have points in common) int8range(3,7) && int8range(4,12) t
<< strictly left of int8range(1,10) << int8range(100,110) t
>> strictly right of int8range(50,60) >> int8range(20,30) t
&< does not extend to the right of int8range(1,20) &< int8range(18,20) t
&> does not extend to the left of int8range(7,20) &> int8range(5,10) t
-|- is adjacent to numrange(1.1,2.2) -|- numrange(2.2,3.3) t
+ union numrange(5,15) + numrange(10,20) [5,20)
* intersection int8range(5,15) * int8range(10,20) [10,15)
- difference int8range(5,15) - int8range(10,20) [5,10)

The simple comparison operators <, >, <=, and >= compare the lower bounds first, and only if those are equal, compare the upper bounds. These comparisons are not usually very useful for ranges, but are provided to allow B-tree indexes to be constructed on ranges.

The left-of/right-of/adjacent operators always return false when an empty range is involved; that is, an empty range is not considered to be either before or after any other range.

The union and difference operators will fail if the resulting range would need to contain two disjoint sub-ranges, as such a range cannot be represented.

The following table shows the functions available for use with range types.

Function Return Type Description Example Result
lower(anyrange) range's element type lower bound of range lower(numrange(1.1,2.2)) 1.1
upper(anyrange) range's element type upper bound of range upper(numrange(1.1,2.2)) 2.2
isempty(anyrange) boolean is the range empty? isempty(numrange(1.1,2.2)) false
lower_inc(anyrange) boolean is the lower bound inclusive? lower_inc(numrange(1.1,2.2)) true
upper_inc(anyrange) boolean is the upper bound inclusive? upper_inc(numrange(1.1,2.2)) false
lower_inf(anyrange) boolean is the lower bound infinite? lower_inf('(,)'::daterange) true
upper_inf(anyrange) boolean is the upper bound infinite? upper_inf('(,)'::daterange) true
range_merge(anyrange, anyrange) anyrange the smallest range which includes both of the given ranges range_merge('[1,2)'::int4range, '[3,4)'::int4range) [1,4)

The lower and upper functions return null if the range is empty or the requested bound is infinite. The lower_inc, upper_inc, lower_inf, and upper_inf functions all return false for an empty range.

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