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 master since they could lead to inconsistencies in Greenplum Database segment instances. This table describes the Greenplum Database Function Types.

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 master. For example, the following statements run on the master (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.

JSON Functions and Operators

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

Note

For json data type values, all key/value pairs are kept even if a JSON object contains duplicate keys. For duplicate keys, JSON processing functions consider the last value as the operative one. For the jsonb data type, duplicate object keys are not kept. If the input includes duplicate keys, only the last value is kept. See About JSON Datain the Greenplum Database Administrator Guide.

JSON Operators

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

Note

There are parallel variants of these operators for both the json and jsonb data types. The field, element, and 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, and 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.

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, see jsonb Containment and Existencein the Greenplum Database Administrator Guide. For information about how these operators can be used to effectively index jsonb data, see jsonb Indexingin the Greenplum Database Administrator Guide.

The standard comparison operators in the following table are available only for the jsonb data type, not for the json data type. They follow the ordering rules for B-tree operations described in jsonb Indexingin the Greenplum Database Administrator Guide.

Note

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

JSON Creation Functions

This table describes the functions that create json data type values. (Currently, there are no equivalent functions for jsonb, but you can cast the result of one of these functions to jsonb.)

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.

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 aggregate records to an array of JSON objects and pairs of values to 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. See About JSON Data.

 key | value —–+——- a   | “foo” b   | “bar” 

 key | value —–+——- a   | foo b   | bar 

 json_object_keys
f1 f2 

 a | b —+— 1 | 2 

 a | b —+— 1 | 2 3 | 4 

   value
1 true [2,false] 

   value
foo bar 

 a |    b    | d —+———+— 1 | [1,2,3] | 

 a |  b —+—– 1 | foo 2 | 

Note

The examples for the functions json_populate_record(), json_populate_recordset(), json_to_record() and json_to_recordset() use constants. However, 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. The extracted key values can then be referenced in other parts of the query. For example the value can be referenced in WHERE clauses and target lists. Extracting multiple values in this way can improve performance over extracting them separately with per-key operators.

JSON keys are matched to identical column names in the target row type. JSON type coercion for these functions might not result in desired values for some types. JSON fields that do not appear in the target row type will be omitted from the output, and target columns that do not match any JSON field will be NULL.

The json_typeof function null return value of null 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.

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.

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.

SELECT department_id, MEDIAN(salary) FROM employees GROUP BY department_id; 

SELECT department_id, PERCENTILE_CONT (0.5) WITHIN GROUP (ORDER BY salary DESC) “Median_cont”; FROM employees GROUP BY department_id;

SELECT department_id, PERCENTILE_DISC (0.5) WITHIN GROUP (ORDER BY salary DESC) “Median_desc”; FROM employees GROUP BY department_id;

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}}

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.

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.

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.

Range Functions and Operators

See Range Types for an overview of range types.

The following table shows the operators available for range types.

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.

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.