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.

Greenplum Database Function Types
Built-in Functions and Operators
JSON Functions and Operators
Window Functions
Advanced Aggregate Functions
Text Search Functions and Operators
Range Functions and Operators

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
JSON Creation Functions
JSON Aggregate Functions
JSON Processing Functions

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:

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.
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.
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.
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.
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] [,exprdefault]) 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`
`\|\|`	concatenate`tsvector`s	`'a:1 b:2'::tsvector \|\| 'c:1 d:2 b:3'::tsvector`	`'a':1 'b':2,5 'c':3 'd':4`
`&&`	AND `tsquery`s together	`'fat \| rat'::tsquery && 'cat'::tsquery`	`( 'fat' \| 'rat' ) & 'cat'`
`\|\|`	OR `tsquery`s together	`'fat \| rat'::tsquery \|\| 'cat'::tsquery`	`( 'fat' \| 'rat' ) \| 'cat'`
`!!`	negate a`tsquery`	`!! '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.