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

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 execute 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 execute 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 execute 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

Built-in functions and operators that create and manipulate JSON data.

• JSON Operators
• JSON Creation Functions
• JSON Processing Functions

Note: For json values, all key/value pairs are kept even if a JSON object contains duplicate key/value pairs. The processing functions consider the last value as the operative one.

JSON Operators

This table describes the operators that are available for use with the json data type.

JSON Creation Functions

This table describes the functions that create json values.

JSON Processing Functions

This table describes the functions that process json values.

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Note: Many of these functions and operators convert Unicode escapes in JSON strings to regular characters. The functions throw an error for characters that cannot be represented in the database encoding.

For json_populate_record and json_populate_recordset, type coercion from JSON is best effort and might not result in desired values for some types. JSON keys are matched to identical column names in the target row type. JSON fields that do not appear in the target row type are omitted from the output, and target columns that do not match any JSON field return NULL.

Window Functions

The following built-in window functions are Greenplum extensions to the PostgreSQL database. All window functions are immutable. For more information about window functions, see “Window Expressions” in the Greenplum Database Administrator Guide.

Advanced Aggregate Functions

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}}