An Expanded Example of a Physical Window : Window Functions « Analytical Functions « Oracle PL/SQL Tutorial

Oracle PL/SQL Tutorial
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Oracle PL/SQL Tutorial » Analytical Functions » Window Functions 
16. 29. 10. An Expanded Example of a Physical Window

To expand the moving average window, we can change the clause ROWS BETWEEN x PRECEDING AND y FOLLOWING to have different values for x and y.

SQL> -- create demo table
SQL> create table myTable(
  2    id           NUMBER(2),
  3    value        NUMBER(6,2)
  4  )
  5  /

Table created.

SQL>
SQL> -- prepare data
SQL> insert into myTable(ID,  value)values (1,9);

row created.

SQL> insert into myTable(ID,  value)values (2,2.11);

row created.

SQL> insert into myTable(ID,  value)values (3,3.44);

row created.

SQL> insert into myTable(ID,  value)values (4,-4.21);

row created.

SQL> insert into myTable(ID,  value)values (5,10);

row created.

SQL> insert into myTable(ID,  value)values (6,3);

row created.

SQL> insert into myTable(ID,  value)values (7,-5.88);

row created.

SQL> insert into myTable(ID,  value)values (8,123.45);

row created.

SQL> insert into myTable(ID,  value)values (9,98.23);

row created.

SQL> insert into myTable(ID,  value)values (10,938.23);

row created.

SQL> insert into myTable(ID,  value)values (11,984.23);

row created.

SQL> insert into myTable(ID,  value)values (12,198.23);

row created.

SQL> insert into myTable(ID,  value)values (13,928.87);

row created.

SQL> insert into myTable(ID,  value)values (14,25.37);

row created.

SQL> insert into myTable(ID,  value)values (15,918.3);

row created.

SQL> insert into myTable(ID,  value)values (16,9.23);

row created.

SQL> insert into myTable(ID,  value)values (17,8.23);

row created.

SQL>
SQL> select from myTable
  2  /

        ID      VALUE
---------- ----------
         1          9
         2       2.11
         3       3.44
         4      -4.21
         5         10
         6          3
         7      -5.88
         8     123.45
         9      98.23
        10     938.23
        11     984.23
        12     198.23
        13     928.87
        14      25.37
        15      918.3
        16       9.23
        17       8.23

17 rows selected.

SQL>
SQL>
SQL> COLUMN ma FORMAT 99999.999
SQL> SELECT id, value,
  2    AVG(valueOVER(ORDER BY id
  3    ROWS BETWEEN PRECEDING AND FOLLOWINGma
  4  FROM myTable
  5  ORDER BY id;

        ID      VALUE         MA
---------- ---------- ----------
         1          9      5.555
         2       2.11      4.850
         3       3.44      2.585
         4      -4.21      4.068
         5         10      2.868
         6          3      1.270
         7      -5.88     25.272
         8     123.45     45.760
         9      98.23    231.406
        10     938.23    427.652
        11     984.23    468.474
        12     198.23    629.558
        13     928.87    614.986
        14      25.37    611.000
        15      918.3    416.000
        16       9.23    378.000
        17       8.23    240.283

17 rows selected.

SQL>
SQL>
SQL>
SQL>
SQL> -- clean the table
SQL> drop table myTable
  2  /

Table dropped.
16. 29. Window Functions
16. 29. 1. Using the Window Functions
16. 29. 2. Performing a Cumulative Sum
16. 29. 3. Use ROWS UNBOUNDED PRECEDING to implicitly indicate the end of the window is the current row
16. 29. 4. Performing a Moving Average
16. 29. 5. Performing a Centered Average
16. 29. 6. Moving average
16. 29. 7. Row-ordering is done first and then the moving average
16. 29. 8. Use SUM for windowing
16. 29. 9. Use the COUNT aggregate analytical function to show how many rows are included in each window
16. 29. 10. An Expanded Example of a Physical Window
16. 29. 11. Displaying which rows are used in the moving average calculations with two other analytical functions: FIRST_ VALUE and LAST_VALUE.
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