Summary of Built-in Functions

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

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.

Table 1. Functions in Greenplum Database
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(), currval(), 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 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.

Table 2. 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

 
Geometric Functions and Operators      
Network Address Functions and Operators      
Sequence Manipulation Functions currval

lastval

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

 
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, perhaps with TOAST compression.
XML Functions   xmlagg(xml)

xmlexists(text, xml)

xml_is_well_formed(text)

xml_is_well_formed_document(text)

xml_is_well_formed_content(text)

xpath(text, xml)

xpath(text, xml, text[])

xpath_exists(text, xml)

xpath_exists(text, xml, text[])

xml(text)

text(xml)

xmlcomment(xml)

xmlconcat2(xml, xml)

 

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.

Table 3. Window functions
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_valueexpr 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 Analytic Functions

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

Table 4. Advanced Analytic Functions
Function Return Type Full Syntax Description
matrix_add(array[], array[]) smallint[], int[], bigint[], float[] matrix_add( array[[1,1],[2,2]], array[[3,4],[5,6]]) Adds two two-dimensional matrices. The matrices must be conformable.
matrix_multiply( array[], array[]) smallint[]int[], bigint[], float[] matrix_multiply( array[[2,0,0],[0,2,0],[0,0,2]], array[[3,0,3],[0,3,0],[0,0,3]] ) Multiplies two, three- dimensional arrays. The matrices must be conformable.
matrix_multiply( array[], expr) int[], float[] matrix_multiply( array[[1,1,1], [2,2,2], [3,3,3]], 2) Multiplies a two-dimensional array and a scalar numeric value.
matrix_transpose( array[]) Same as input array type. matrix_transpose( array [[1,1,1],[2,2,2]]) Transposes a two-dimensional array.
pinv(array []) smallint[]int[], bigint[], float[] pinv(array[[2.5,0,0],[0,1,0],[0,0,.5]]) Calculates the Moore-Penrose pseudoinverse of a matrix.
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.
Table 5. Advanced Aggregate Functions
Function Return Type Full Syntax Description
MEDIAN (expr) timestamp, timestampz, 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, timestampz, 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 distirbution 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, timestampz, 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 distirbution 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.
mregr_coef(expr, array[]) float[] mregr_coef(y, array[1, x1, x2]) The four mregr_* aggregates perform linear regressions using the ordinary-least-squares method. mregr_coef calculates the regression coefficients. The size of the return array for mregr_coef is the same as the size of the input array of independent variables, since the return array contains the coefficient for each independent variable.
mregr_r2 (expr, array[]) float mregr_r2(y, array[1, x1, x2]) The four mregr_* aggregates perform linear regressions using the ordinary-least-squares method. mregr_r2 calculates the r-squared error value for the regression.
mregr_pvalues(expr, array[]) float[] mregr_pvalues(y, array[1, x1, x2]) The four mregr_* aggregates perform linear regressions using the ordinary-least-squares method. mregr_pvalues calculates the p-values for the regression.
mregr_tstats(expr, array[]) float[] mregr_tstats(y, array[1, x1, x2]) The four mregr_* aggregates perform linear regressions using the ordinary-least-squares method. mregr_tstats calculates the t-statistics for the regression.
nb_classify(text[], bigint, bigint[], bigint[]) text nb_classify(classes, attr_count, class_count, class_total) Classify rows using a Naive Bayes Classifier. This aggregate uses a baseline of training data to predict the classification of new rows and returns the class with the largest likelihood of appearing in the new rows.
nb_probabilities(text[], bigint, bigint[], bigint[]) text nb_probabilities(classes, attr_count, class_count, class_total) Determine probability for each class using a Naive Bayes Classifier. This aggregate uses a baseline of training data to predict the classification of new rows and returns the probabilities that each class will appear in new rows.