9 Built-In Aggregate Functions

This chapter provides a reference to built-in aggregate functions included in Oracle Continuous Query Language (Oracle CQL). Built-in aggregate functions perform a summary operation on all the values that a query returns.

This chapter includes the following section:

9.1 Introduction to Oracle CQL Built-In Aggregate Functions

Table 9-1 lists the built-in aggregate functions that Oracle CQL provides:

Table 9-1 Oracle CQL Built-in Aggregate Functions

Type Function

Aggregate

Aggregate (incremental computation)

Extended aggregate

Specify distinct if you want Oracle Stream Analytics to return only one copy of each set of duplicate tuples selected. Duplicate tuples are those with matching values for each expression in the select list. For more information, see .

Oracle Stream Analytics does not support nested aggregations.

Note:

Built-in function names are case sensitive and you must use them in the case shown (in lower case).

Note:

In stream input examples, lines beginning with h (such as h 3800) are heartbeat input tuples. These inform Oracle Stream Analytics that no further input will have a timestamp lesser than the heartbeat value.

For more information, see:

9.1.1 Built-In Aggregate Functions and the Where, Group By, and Having Clauses

In Oracle CQL, the where clause is applied before the group by and having clauses. This means the Oracle CQL statement is invalid:

<query id="q1"><![CDATA[ 
    select * from InputChanel[rows 4 slide 4] as ic where count(*) = 4
]]></query>

Instead, you must use the Oracle CQL statement:

<query id="q1"><![CDATA[ 
    select * from InputChanel[rows 4 slide 4] as ic where count(*) = 4
]]></query>

9.2 avg

Syntax

avg

Purpose

avg returns average value of expr.

This function takes as an argument any bigint, float, or int data type. The function returns a float regardless of the numeric data type of the argument.

Input/Output Types

The following tables lists the input types and the corresponding output types:

Input Type

Output Type

INT

FLOAT

BIGINT

FLOAT

FLOAT

FLOAT

DOUBLE

DOUBLE

BIGDECIMAL

BIGDECIMAL

Examples

Consider the query float_avg and the data stream S3. Stream S3 has schema (c1 float). The query returns the relation. Note that the avg function returns a result of NaN if the average value is not a number.

<query id="float_avg"><![CDATA[ 
    select avg(c1) from S3[range 5]
]]></query>

Timestamp   Tuple
 1000 
 2000        5.5
 8000        4.4
 9000
15000       44.2
h 200000000

Timestamp   Tuple Kind  Tuple
 1000:      - 
 1000:      +            0.0
 2000:      -            0.0
 2000:      +            5.5
 6000:      -            5.5
 6000:      +            5.5
 7000:      -            5.5
 8000:      -
 8000:      +            4.4
 9000:      -            4.4
 9000:      +            4.4
13000:      -            4.4
13000:      +           NaN
14000:      -           NaN
14000:      +
15000:      -
15000:      +           44.2
20000:      -           44.2
20000:      +

9.3 count

Syntax

count

Purpose

count returns the number of tuples returned by the query as an int value.

The return value depends on the argument as Table 9-2 shows.

Table 9-2 Return Values for COUNT Aggregate Function

Input Argument Return Value

arith_expr

The number of tuples where arith_expr is not null.

*

The number of all tuples, including duplicates and nulls.

identifier.*

The number of all tuples that match the correlation variable identifier, including duplicates and nulls.

identifier.attr

The number of tuples that match correlation variable identifier, where attr is not null.

count never returns null.

Example

Consider the query q2 and the data stream S2. Stream S2 has schema (c1 integer, c2 integer). The query returns the relation.

<query id="q2"><![CDATA[ 
    SELECT COUNT(c2), COUNT(*) FROM  S2 [RANGE 10]
]]></query>

Timestamp   Tuple
1000        1,2
2000        1,
3000        1,4
6000        1,6

Timestamp                Tuple Kind  Tuple
-9223372036854775808:    +           0,0
1000:                    -           0,0
1000:                    +           1,1
2000:                    -           1,1
2000:                    +           1,2
3000:                    -           1,2
3000:                    +           2,3
6000:                    -           2,3
6000:                    +           3,4

9.4 first

Syntax

first

Purpose

first returns the value of the specified stream element the first time the specified pattern is matched.

The type of the specified stream element may be any of:

  • bigint

  • integer

  • byte

  • char

  • float

  • interval

  • timestamp.

The return type of this function depends on the type of the specified stream element.

This function takes a single argument made up of the following period-separated values:

  • identifier1: the name of a pattern as specified in a DEFINE clause.

  • identifier2: the name of a stream element as specified in a CREATE STREAM statement.

See Also:

Examples

Consider the query q9 and the data stream S0. Stream S0 has schema (c1 integer, c2 float). This example defines pattern C as C.c1 = 7. It defines firstc as first(C.c2). In other words, firstc will equal the value of c2 the first time c1 = 7. The query returns the relation.

<query id="q9"><![CDATA[ 
    select 
        T.firstc,
        T.lastc,
        T.Ac1,
        T.Bc1,
        T.avgCc1, 
        T.Dc1 
    from 
        S0 
    MATCH_RECOGNIZE ( 
        MEASURES 
            first(C.c2) as firstc, 
            last(C.c2) as lastc, 
            avg(C.c1) as avgCc1, 
            A.c1 as Ac1, 
            B.c1 as Bc1, 
            D.c1 as Dc1 
        PATTERN(A B C* D) 
        DEFINE 
            A as A.c1 = 30, 
            B as B.c2 = 10.0, 
            C as C.c1 = 7, 
            D as D.c1 = 40
    ) as T
]]></query>

Timestamp   Tuple
 1000       33,0.9
 3000       44,0.4
 4000       30,0.3
 5000       10,10.0
 6000        7,0.9
 7000        7,2.3
 9000        7,8.7
11000       40,6.6
15000       19,8.8
17000       30,5.5
20000        5,10.0
23000       40,6.6
25000        3,5.5
30000       30,2.2
35000        2,10.0
40000        7,5.5
44000       40,8.9

Timestamp   Tuple Kind  Tuple
11000:      +           0.9,8.7,30,10,7.0,40
23000:      +           ,,30,5,,40
44000:      +           5.5,5.5,30,2,7.0,40

9.5 last

Syntax

last

Purpose

last returns the value of the specified stream element the last time the specified pattern is matched.

The type of the specified stream element may be any of:

  • bigint

  • integer

  • byte

  • char

  • float

  • interval

  • timestamp.

The return type of this function depends on the type of the specified stream element.

This function takes a single argument made up of the following period-separated values:

  • identifier1: the name of a pattern as specified in a DEFINE clause.

  • identifier2: the name of a stream element as specified in a CREATE STREAM statement.

See Also:

Examples

Consider the query q9 and the data stream S0. Stream S1 has schema (c1 integer, c2 float). This example defines pattern C as C.c1 = 7. It defines lastc as last(C.c2). In other words, lastc will equal the value of c2 the last time c1 = 7. The query returns the relation.

<query id="q9"><![CDATA[ 
    select 
        T.firstc,
        T.lastc,
        T.Ac1,
        T.Bc1,
        T.avgCc1, 
        T.Dc1 
    from 
        S0 
    MATCH_RECOGNIZE ( 
        MEASURES 
            first(C.c2) as firstc, 
            last(C.c2) as lastc, 
            avg(C.c1) as avgCc1, 
            A.c1 as Ac1, 
            B.c1 as Bc1, 
            D.c1 as Dc1 
        PATTERN(A B C* D) 
        DEFINE 
            A as A.c1 = 30, 
            B as B.c2 = 10.0, 
            C as C.c1 = 7, 
            D as D.c1 = 40
    ) as T
]]></query>

Timestamp   Tuple
 1000       33,0.9
 3000       44,0.4
 4000       30,0.3
 5000       10,10.0
 6000        7,0.9
 7000        7,2.3
 9000        7,8.7
11000       40,6.6
15000       19,8.8
17000       30,5.5
20000        5,10.0
23000       40,6.6
25000        3,5.5
30000       30,2.2
35000        2,10.0
40000        7,5.5
44000       40,8.9

Timestamp   Tuple Kind  Tuple
11000:      +           0.9,8.7,30,10,7.0,40
23000:      +           ,,30,5,,40
44000:      +           5.5,5.5,30,2,7.0,40

9.6 listagg

Syntax

list agg

Purpose

listagg returns a java.util.List containing the Java equivalent of the function's argument.

Note that when a user-defined class is used as the function argument, the class must implement the equals method.

Examples

<view id="v1"><![CDATA[
    ISTREAM(select c1, listAgg(c3) as l1, 
        java.util.LinkedHashSet(listAgg(c3)) as set1 
    from S1 
    group by c1)
]]></view>

<query id="q1"><![CDATA[
    select v1.l1.size(), v1.set1.size() 
    from v1
]]></query> 

Timestamp     Tuple
1000          orcl, 1, 15, 400
1000          msft, 1, 15, 400
2000          orcl, 2, 20, 300
2000          msft, 2, 20, 300
5000          orcl, 4, 5, 200
5000          msft, 4, 5, 200
7000          orcl, 3, 10, 100
7000          msft, 3, 20, 100
h 20000000

Timestamp   Tuple Kind   Tuple
 1000:          +        1,1
 1000:          +        1,1
 2000:          +        2,2
 2000:          +        2,2
 5000:          +        3,3
 5000:          +        3,3
 7000:          +        4,4
 7000:          +        4,3

9.7 max

Syntax

max

Purpose

max returns maximum value of expr. Its data type depends on the data type of the argument.

Examples

Consider the query test_max_timestampand the data stream S15 . Stream S15 has schema (c1 int, c2 timestamp). The query returns the relation.

<query id="test_max_timestamp"><![CDATA[ 
    select max(c2) from S15[range 2]
]]></query>

Timestamp   Tuple
  10        1,"08/07/2004 11:13:48"
2000         ,"08/07/2005 11:13:48"
3400        3,"08/07/2006 11:13:48"
4700         ,"08/07/2007 11:13:48"
h 8000 
h 200000000

Timestamp   Tuple Kind  Tuple
   0:       + 
  10:       - 
  10:       +           08/07/2004 11:13:48
2000:       -           08/07/2004 11:13:48
2000:       +           08/07/2005 11:13:48
2010:       -           08/07/2005 11:13:48
2010:       +           08/07/2005 11:13:48
3400:       -           08/07/2005 11:13:48
3400:       +           08/07/2006 11:13:48
4000:       -           08/07/2006 11:13:48
4000:       +           08/07/2006 11:13:48
4700:       -           08/07/2006 11:13:48
4700:       +           08/07/2007 11:13:48
5400:       -           08/07/2007 11:13:48
5400:       +           08/07/2007 11:13:48
6700:       -           08/07/2007 11:13:48
6700:       +

9.8 min

Syntax

min

Purpose

min returns minimum value of expr. Its data type depends on the data type of its argument.

Examples

Consider the query test_min_timestamp and the data stream S15. Stream S15 has schema (c1 int, c2 timestamp). The query returns the relation.

<query id="test_min_timestamp"><![CDATA[ 
    select min(c2) from S15[range 2]
]]></query>

Timestamp   Tuple
  10        1,"08/07/2004 11:13:48"
2000         ,"08/07/2005 11:13:48"
3400        3,"08/07/2006 11:13:48"
4700         ,"08/07/2007 11:13:48"
h 8000 
h 200000000

Timestamp   Tuple Kind  Tuple
   0:       + 
  10:       - 
  10:       +           08/07/2004 11:13:48
2000:       -           08/07/2004 11:13:48
2000:       +           08/07/2004 11:13:48
2010:       -           08/07/2004 11:13:48
2010:       +           08/07/2005 11:13:48
3400:       -           08/07/2005 11:13:48
3400:       +           08/07/2005 11:13:48
4000:       -           08/07/2005 11:13:48
4000:       +           08/07/2006 11:13:48
4700:       -           08/07/2006 11:13:48
4700:       +           08/07/2006 11:13:48
5400:       -           08/07/2006 11:13:48
5400:       +           08/07/2007 11:13:48
6700:       -           08/07/2007 11:13:48
6700:       +

9.9 sum

Syntax

sum

Purpose

sum returns the sum of values of expr. This function takes as an argument any bigint, float, or integer expression. The function returns the same data type as the numeric data type of the argument.

Examples

Consider the query q3 and the data stream S1. Stream S1 has schema (c1 integer, c2 bigint). The query returns the relation. For more information on range, see .

<query id="q3"><![CDATA[ 
    select sum(c2) from S1[range 5]
]]></query>

Timestamp   Tuple
1000         5,
1000        10,5
2000          ,4
3000        30,6
5000        45,44
7000        55,3
h 200000000

Timestamp   Tuple Kind  Tuple
 1000:      -
 1000:      +           5
 2000:      -           5
 2000:      +           9
 3000:      -           9
 3000:      +           15
 5000:      -           15
 5000:      +           59
 6000:      -           59
 6000:      +           54
 7000:      -           54
 7000:      +           53
 8000:      -           53
 8000:      +           47
10000:      -           47
10000:      +           3
12000:      -           3
12000:      +

9.10 xmlagg

Syntax

xml agg

Purpose

xmlagg returns a collection of XML fragments as an aggregated XML document. Arguments that return null are dropped from the result.

You can control the order of fragments using an ORDER BY clause.

Examples

xmlagg Function and the xmlelement Function

Consider the query tkdata67_q1 and the input relation. Stream tkdata67_S0 has schema (c1 integer, c2 float). This query uses xmlelement to create XML fragments from stream elements and then uses xmlagg to aggregate these XML fragments into an XML document. The query returns the relation.

<query id="tkdata67_q1"><![CDATA[ 
    select 
        c1, 
        xmlagg(xmlelement("c2",c2)) 
    from 
        tkdata67_S0[rows 10] 
    group by c1
]]></query>

Timestamp           Tuple
 1000               15, 0.1
 1000               20, 0.14
 1000               15, 0.2
 4000               20, 0.3
10000               15, 0.04
h 12000

Timestamp   Tuple Kind  Tuple
1000:       +           15,<c2>0.1</c2>
                           <c2>0.2</c2>
1000:       +           20,<c2>0.14</c2>
4000:       -           20,<c2>0.14</c2>
4000:       +           20,<c2>0.14</c2>
                           <c2>0.3</c2>
10000:      -           15,<c2>0.1</c2>
                           <c2>0.2</c2>
10000:      +           15,<c2>0.1</c2>
                           <c2>0.2</c2>
                           <c2>0.04</c2>

xmlagg Function and the ORDER BY Clause

Consider the query tkxmlAgg_q5 and the input relation. Stream tkxmlAgg_S1 has schema (c1 int, c2 xmltype). These query selects xmltype stream elements and uses XMLAGG to aggregate them into an XML document. This query uses an ORDER BY clause to order XML fragments. The query returns the relation.

<query id="tkxmlAgg_q5"><![CDATA[ 
    select 
        xmlagg(c2), 
        xmlagg(c2 order by c1) 
    from 
        tkxmlAgg_S1[range 2]
]]></query>

Timestamp           Tuple
1000                1, "<a>hello</a>"
2000               10, "<b>hello1</b>"
3000               15, "<PDRecord><PDName>hello</PDName></PDRecord>"
4000                5, "<PDRecord><PDName>hello</PDName><PDName>hello1</PDName></PDRecord>"
5000               51, "<PDRecord><PDId>6</PDId><PDName>hello1</PDName></PDRecord>"
6000               15, "<PDRecord><PDId>46</PDId><PDName>hello2</PDName></PDRecord>"
7000               55, "<PDRecord><PDId>6</PDId><PDName>hello2</PDName><PDName>hello3</PDName></PDRecord>"

Timestamp   Tuple Kind  Tuple
    0:      +
1000:       -
1000:       +           <a>hello</a>
                       ,<a>hello</a>
2000:       -           <a>hello</a>
                       ,<a>hello</a>
2000:       +           <a>hello</a>
                        <b>hello1</b>
                       ,<a>hello</a>
                        <b>hello1</b>
3000:       -           <a>hello</a>
                        <b>hello1</b>
                       ,<a>hello</a>
                        <b>hello1</b>
3000:       +           <b>hello1</b>
                        <PDRecord>
                          <PDName>hello</PDName>
                        </PDRecord>
                       ,<b>hello1</b>
                        <PDRecord>
                          <PDName>hello</PDName>
                        </PDRecord>
4000:       -           <b>hello1</b>
                        <PDRecord>
                          <PDName>hello</PDName>
                        </PDRecord>
                       ,<b>hello1</b>
                        <PDRecord>
                          <PDName>hello</PDName>
                        </PDRecord>
4000:       +           <PDRecord>
                          <PDName>hello</PDName>
                        </PDRecord>
                        <PDRecord>
                          <PDName>hello</PDName>
                          <PDName>hello1</PDName>
                        </PDRecord>
                       ,<PDRecord>
                          <PDName>hello</PDName>
                          <PDName>hello1</PDName>
                        </PDRecord>
                        <PDRecord>
                          <PDName>hello</PDName>
                        </PDRecord>
5000:       -           <PDRecord>
                          <PDName>hello</PDName>
                        </PDRecord>
                        <PDRecord>
                          <PDName>hello</PDName>
                          <PDName>hello1</PDName>
                        </PDRecord>
                       ,<PDRecord>
                          <PDName>hello</PDName>
                          <PDName>hello1</PDName>
                        </PDRecord>
                        <PDRecord>
                          <PDName>hello</PDName>
                        </PDRecord>
5000:       +           <PDRecord>
                          <PDName>hello</PDName>
                          <PDName>hello1</PDName>
                        </PDRecord>
                        <PDRecord>
                          <PDId>6</PDId>
                          <PDName>hello1</PDName>
                        </PDRecord>
                       ,<PDRecord>
                          <PDName>hello</PDName>
                          <PDName>hello1</PDName>
                        </PDRecord>
                        <PDRecord>
                          <PDId>6</PDId>
                          <PDName>hello1</PDName>
                        </PDRecord>
6000:       -           <PDRecord>
                          <PDName>hello</PDName>
                          <PDName>hello1</PDName>
                        </PDRecord>
                        <PDRecord>
                          <PDId>6</PDId>
                          <PDName>hello1</PDName>
                        </PDRecord>
                       ,<PDRecord>
                          <PDName>hello</PDName>
                          <PDName>hello1</PDName>
                        </PDRecord>
                        <PDRecord>
                          <PDId>6</PDId>
                          <PDName>hello1</PDName>
                        </PDRecord>
6000:       +           <PDRecord>
                          <PDId>6</PDId>
                          <PDName>hello1</PDName>
                        </PDRecord>
                        <PDRecord>
                          <PDId>46</PDId>
                          <PDName>hello2</PDName>
                        </PDRecord>
                       ,<PDRecord>
                          <PDId>46</PDId>
                          <PDName>hello2</PDName>
                        </PDRecord>
                        <PDRecord>
                          <PDId>6</PDId>
                          <PDName>hello1</PDName>
                        </PDRecord>
7000:       -           <PDRecord>
                          <PDId>6</PDId>
                          <PDName>hello1</PDName>
                        </PDRecord>
                        <PDRecord>
                          <PDId>46</PDId>
                          <PDName>hello2</PDName>
                        </PDRecord>
                       ,<PDRecord>
                          <PDId>46</PDId>
                          <PDName>hello2</PDName>
                        </PDRecord>
                        <PDRecord>
                          <PDId>6</PDId>
                          <PDName>hello1</PDName>
                        </PDRecord>