Using Functions and Operators

• Using Functions in Greenplum Database
• Window Functions

Parent topic: Querying Data

Refer to the PostgreSQL Function Volatility Categories documentation for additional information about the Greenplum Database function volatility classifications.

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):

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:

There is relatively little difference between the STABLE and IMMUTABLE function volatility categories for simple interactive queries that are planned and immediately executed. It does not matter much whether a function is executed once during planning or once during query execution startup. But there is a big difference when you save the plan and reuse it later. If you mislabel a function IMMUTABLE, Greenplum Database may prematurely fold it to a constant during planning, possibly reusing a stale value during subsequent execution of the plan. You may run into this hazard when using PREPAREd statements, or when using languages such as PL/pgSQL that cache plans.

Greenplum Database supports user-defined functions. See in the PostgreSQL documentation for more information.

Use the CREATE FUNCTION statement to register user-defined functions that are used as described in Using Functions in Greenplum Database. By default, user-defined functions are declared as VOLATILE, so if your user-defined function is IMMUTABLE or STABLE, you must specify the correct volatility level when you register your function.

When you create user-defined functions, avoid using fatal errors or destructive calls. Greenplum Database may respond to such errors with a sudden shutdown or restart.

In Greenplum Database, the shared library files for user-created functions must reside in the same library path location on every host in the Greenplum Database array (masters, segments, and mirrors).

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 functions, which are subject to the restrictions noted in Using Functions in Greenplum Database. See the section of the PostgreSQL documentation for more information about these built-in functions and operators.

Greenplum Database includes JSON processing functions that manipulate values the json data type. For information about JSON data, see Working with JSON Data.

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.

The following built-in advanced aggregate functions are Greenplum extensions of the PostgreSQL database. These functions are immutable. Greenplum Database does not support the PostgreSQL ordered-set aggregate functions.

Note: The Greenplum MADlib Extension for Analytics provides additional advanced functions to perform statistical analysis and machine learning with Greenplum Database data. See Greenplum MADlib Extension for Analytics in the Greenplum Database Reference Guide.