Analytic functions compute an aggregate value based on a group of rows. They differ from aggregate functions in that they return multiple rows for each group. The group of rows is called a window and is defined by the analytic_clause
. For each row, a sliding window of rows is defined. The window determines the range of rows used to perform the calculations for the current row. Window sizes can be based on either a physical number of rows or a logical interval such as time.
Analytic functions are the last set of operations performed in a query except for the final ORDER
BY
clause. All joins and all WHERE
, GROUP
BY
, and HAVING
clauses are completed before the analytic functions are processed. Therefore, analytic functions can appear only in the select list or ORDER
BY
clause.
Analytic functions are commonly used to compute cumulative, moving, centered, and reporting aggregates.
windowing_clause ::=
The semantics of this syntax are discussed in the sections that follow.
Specify the name of an analytic function (see the listing of analytic functions following this discussion of semantics).
Analytic functions take 0 to 3 arguments. The arguments can be any numeric data type or any nonnumeric data type that can be implicitly converted to a numeric data type. Oracle determines the argument with the highest numeric precedence and implicitly converts the remaining arguments to that data type. The return type is also that data type, unless otherwise noted for an individual function.
See Also:
"Numeric Precedence" for information on numeric precedence and Table 2-10, "Implicit Type Conversion Matrix" for more information on implicit conversionUse OVER
analytic_clause
to indicate that the function operates on a query result set. This clause is computed after the FROM
, WHERE
, GROUP
BY
, and HAVING
clauses. You can specify analytic functions with this clause in the select list or ORDER
BY
clause. To filter the results of a query based on an analytic function, nest these functions within the parent query, and then filter the results of the nested subquery.
Notes on the analytic_clause: The following notes apply to the analytic_clause
:
You cannot nest analytic functions by specifying any analytic function in any part of the analytic_clause
. However, you can specify an analytic function in a subquery and compute another analytic function over it.
You can specify OVER
analytic_clause
with user-defined analytic functions as well as built-in analytic functions. See CREATE FUNCTION.
Use the PARTITION
BY
clause to partition the query result set into groups based on one or more value_expr
. If you omit this clause, then the function treats all rows of the query result set as a single group.
To use the query_partition_clause
in an analytic function, use the upper branch of the syntax (without parentheses). To use this clause in a model query (in the model_column_clauses
) or a partitioned outer join (in the outer_join_clause
), use the lower branch of the syntax (with parentheses).
You can specify multiple analytic functions in the same query, each with the same or different PARTITION
BY
keys.
If the objects being queried have the parallel attribute, and if you specify an analytic function with the query_partition_clause
, then the function computations are parallelized as well.
Valid values of value_expr
are constants, columns, nonanalytic functions, function expressions, or expressions involving any of these.
Use the order_by_clause
to specify how data is ordered within a partition. For all analytic functions you can order the values in a partition on multiple keys, each defined by a value_expr
and each qualified by an ordering sequence.
Within each function, you can specify multiple ordering expressions. Doing so is especially useful when using functions that rank values, because the second expression can resolve ties between identical values for the first expression.
Whenever the order_by_clause
results in identical values for multiple rows, the function behaves as follows:
CUME_DIST
, DENSE_RANK
, NTILE
, PERCENT_RANK
, and RANK
return the same result for each of the rows.
ROW_NUMBER
assigns each row a distinct value even if there is a tie based on the order_by_clause
. The value is based on the order in which the row is processed, which may be nondeterministic if the ORDER
BY
does not guarantee a total ordering.
For all other analytic functions, the result depends on the window specification. If you specify a logical window with the RANGE
keyword, then the function returns the same result for each of the rows. If you specify a physical window with the ROWS
keyword, then the result is nondeterministic.
Restrictions on the ORDER BY Clause The following restrictions apply to the ORDER
BY
clause:
When used in an analytic function, the order_by_clause
must take an expression (expr
). The SIBLINGS
keyword is not valid (it is relevant only in hierarchical queries). Position (position
) and column aliases (c_alias
) are also invalid. Otherwise this order_by_clause
is the same as that used to order the overall query or subquery.
An analytic function that uses the RANGE
keyword can use multiple sort keys in its ORDER
BY
clause if it specifies any of the following windows:
RANGE
BETWEEN
UNBOUNDED
PRECEDING
AND
CURRENT
ROW
. The short form of this is RANGE
UNBOUNDED
PRECEDING
.
RANGE
BETWEEN
CURRENT
ROW
AND
UNBOUNDED
FOLLOWING
RANGE
BETWEEN
CURRENT
ROW
AND
CURRENT
ROW
RANGE
BETWEEN
UNBOUNDED
PRECEDING
AND
UNBOUNDED
FOLLOWING
Window boundaries other than these four can have only one sort key in the ORDER
BY
clause of the analytic function. This restriction does not apply to window boundaries specified by the ROW
keyword.
ASC | DESC Specify the ordering sequence (ascending or descending). ASC
is the default.
NULLS FIRST | NULLS LAST Specify whether returned rows containing nulls should appear first or last in the ordering sequence.
NULLS
LAST
is the default for ascending order, and NULLS
FIRST
is the default for descending order.
Analytic functions always operate on rows in the order specified in the order_by_clause
of the function. However, the order_by_clause
of the function does not guarantee the order of the result. Use the order_by_clause
of the query to guarantee the final result ordering.
Some analytic functions allow the windowing_clause
. In the listing of analytic functions at the end of this section, the functions that allow the windowing_clause
are followed by an asterisk (*).
ROWS | RANGE These keywords define for each row a window (a physical or logical set of rows) used for calculating the function result. The function is then applied to all the rows in the window. The window moves through the query result set or partition from top to bottom.
ROWS
specifies the window in physical units (rows).
RANGE
specifies the window as a logical offset.
You cannot specify this clause unless you have specified the order_by_clause
. Some window boundaries defined by the RANGE
clause let you specify only one expression in the order_by_clause
. Refer to "Restrictions on the ORDER BY Clause".
The value returned by an analytic function with a logical offset is always deterministic. However, the value returned by an analytic function with a physical offset may produce nondeterministic results unless the ordering expression results in a unique ordering. You may have to specify multiple columns in the order_by_clause
to achieve this unique ordering.
BETWEEN ... AND Use the BETWEEN
... AND
clause to specify a start point and end point for the window. The first expression (before AND
) defines the start point and the second expression (after AND
) defines the end point.
If you omit BETWEEN
and specify only one end point, then Oracle considers it the start point, and the end point defaults to the current row.
UNBOUNDED PRECEDING Specify UNBOUNDED
PRECEDING
to indicate that the window starts at the first row of the partition. This is the start point specification and cannot be used as an end point specification.
UNBOUNDED FOLLOWING Specify UNBOUNDED
FOLLOWING
to indicate that the window ends at the last row of the partition. This is the end point specification and cannot be used as a start point specification.
CURRENT ROW As a start point, CURRENT
ROW
specifies that the window begins at the current row or value (depending on whether you have specified ROW
or RANGE
, respectively). In this case the end point cannot be value_expr
PRECEDING
.
As an end point, CURRENT
ROW
specifies that the window ends at the current row or value (depending on whether you have specified ROW
or RANGE
, respectively). In this case the start point cannot be value_expr
FOLLOWING
.
value_expr PRECEDING or value_expr FOLLOWING For RANGE
or ROW
:
If value_expr
FOLLOWING
is the start point, then the end point must be value_expr
FOLLOWING
.
If value_expr
PRECEDING
is the end point, then the start point must be value_expr
PRECEDING
.
If you are defining a logical window defined by an interval of time in numeric format, then you may need to use conversion functions.
See Also:
NUMTOYMINTERVAL and NUMTODSINTERVAL for information on converting numeric times into intervalsIf you specified ROWS
:
value_expr
is a physical offset. It must be a constant or expression and must evaluate to a positive numeric value.
If value_expr
is part of the start point, then it must evaluate to a row before the end point.
If you specified RANGE
:
value_expr
is a logical offset. It must be a constant or expression that evaluates to a positive numeric value or an interval literal. Refer to "Literals" for information on interval literals.
You can specify only one expression in the order_by_clause
.
If value_expr
evaluates to a numeric value, then the ORDER
BY
expr
must be a numeric or DATE
data type.
If value_expr
evaluates to an interval value, then the ORDER
BY
expr
must be a DATE
data type.
If you omit the windowing_clause
entirely, then the default is RANGE
BETWEEN
UNBOUNDED
PRECEDING
AND
CURRENT
ROW
.
Analytic functions are commonly used in data warehousing environments. In the list of analytic functions that follows, functions followed by an asterisk (*) allow the full syntax, including the windowing_clause
.
See Also:
Oracle Database Data Warehousing Guide for more information on these functions and for scenarios illustrating their use