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CUME_DIST Analytic Function

This article gives an overview of the CUME_DIST analytic function. If you are new to analytic functions you should probably read this introduction to analytic functions first.

Related articles.

Setup

The examples in this article require the following table.

--DROP TABLE emp PURGE;

CREATE TABLE emp (
  empno    NUMBER(4) CONSTRAINT pk_emp PRIMARY KEY,
  ename    VARCHAR2(10),
  job      VARCHAR2(9),
  mgr      NUMBER(4),
  hiredate DATE,
  sal      NUMBER(7,2),
  comm     NUMBER(7,2),
  deptno   NUMBER(2)
);

INSERT INTO emp VALUES (7369,'SMITH','CLERK',7902,to_date('17-12-1980','dd-mm-yyyy'),800,NULL,20);
INSERT INTO emp VALUES (7499,'ALLEN','SALESMAN',7698,to_date('20-2-1981','dd-mm-yyyy'),1600,300,30);
INSERT INTO emp VALUES (7521,'WARD','SALESMAN',7698,to_date('22-2-1981','dd-mm-yyyy'),1250,500,30);
INSERT INTO emp VALUES (7566,'JONES','MANAGER',7839,to_date('2-4-1981','dd-mm-yyyy'),2975,NULL,20);
INSERT INTO emp VALUES (7654,'MARTIN','SALESMAN',7698,to_date('28-9-1981','dd-mm-yyyy'),1250,1400,30);
INSERT INTO emp VALUES (7698,'BLAKE','MANAGER',7839,to_date('1-5-1981','dd-mm-yyyy'),2850,NULL,30);
INSERT INTO emp VALUES (7782,'CLARK','MANAGER',7839,to_date('9-6-1981','dd-mm-yyyy'),2450,NULL,10);
INSERT INTO emp VALUES (7788,'SCOTT','ANALYST',7566,to_date('13-JUL-87','dd-mm-rr')-85,3000,NULL,20);
INSERT INTO emp VALUES (7839,'KING','PRESIDENT',NULL,to_date('17-11-1981','dd-mm-yyyy'),5000,NULL,10);
INSERT INTO emp VALUES (7844,'TURNER','SALESMAN',7698,to_date('8-9-1981','dd-mm-yyyy'),1500,0,30);
INSERT INTO emp VALUES (7876,'ADAMS','CLERK',7788,to_date('13-JUL-87', 'dd-mm-rr')-51,1100,NULL,20);
INSERT INTO emp VALUES (7900,'JAMES','CLERK',7698,to_date('3-12-1981','dd-mm-yyyy'),950,NULL,30);
INSERT INTO emp VALUES (7902,'FORD','ANALYST',7566,to_date('3-12-1981','dd-mm-yyyy'),3000,NULL,20);
INSERT INTO emp VALUES (7934,'MILLER','CLERK',7782,to_date('23-1-1982','dd-mm-yyyy'),1300,NULL,10);
COMMIT;

CUME_DIST as an Aggregate Function

The CUME_DIST aggregate function is predictive, returning the cumulative distribution of the specified value in a set. As an aggregate function it reduces the number of rows, hence the term "aggregate". If the data isn't grouped we turn the 14 rows in the EMP table to a single row with the aggregated value. In the following example we see the cumulative distribution of the specified value relative to the salaries for all employees.

SELECT CUME_DIST(2000) WITHIN GROUP (ORDER BY sal) AS cume_dist_sal
FROM   emp;

CUME_DIST_SAL
-------------
           .6

SQL>

We can get more granularity of information by including a GROUP BY clause. In the following example we see the cumulative distribution of the specified value relative to the salaries on a per-department basis.

SELECT deptno,
       CUME_DIST(2000) WITHIN GROUP (ORDER BY sal) AS cume_dist_sal_by_dept
FROM   emp
GROUP BY deptno
ORDER BY deptno;

    DEPTNO CUME_DIST_SAL_BY_DEPT
---------- ---------------------
        10                    .5
        20                    .5
        30            .857142857

SQL>

More than one column can be specified in the aggregate function.

SELECT deptno,
       CUME_DIST(2000, 0.1) WITHIN GROUP (ORDER BY sal, comm) AS cume_dist_sal_by_dept
FROM   emp
GROUP BY deptno
ORDER BY deptno;

    DEPTNO CUME_DIST_SAL_BY_DEPT
---------- ---------------------
        10                    .5
        20                    .5
        30            .857142857

SQL>

In both cases we have aggregated the data to get the values, returning less rows than we started with. Analytic functions allow us to return these aggregate values while retaining the original row data.

CUME_DIST Analytic Function

The basic description for the CUME_DIST analytic function is shown below. The analytic clause is described in more detail here.

CUME_DIST() OVER ([ query_partition_clause ] order_by_clause)

The CUME_DIST analytic function is order sensitive so the ORDER BY clause is mandatory. Omitting a partitioning clause from the OVER clause means the whole result set is treated as a single partition. In the following example we display the cumulative distribution, or the relative position in the set, of each of all employees, as well as all the original data.

SELECT empno,
       ename,
       deptno,
       sal,
       CUME_DIST() OVER (ORDER BY sal) AS cume_dist_sal
FROM   emp;

     EMPNO ENAME          DEPTNO        SAL CUME_DIST_SAL
---------- ---------- ---------- ---------- -------------
      7369 SMITH              20        800   .0714285714
      7900 JAMES              30        950    .142857143
      7876 ADAMS              20       1100    .214285714
      7521 WARD               30       1250    .357142857
      7654 MARTIN             30       1250    .357142857
      7934 MILLER             10       1300    .428571429
      7844 TURNER             30       1500            .5
      7499 ALLEN              30       1600    .571428571
      7782 CLARK              10       2450    .642857143
      7698 BLAKE              30       2850    .714285714
      7566 JONES              20       2975    .785714286
      7788 SCOTT              20       3000    .928571429
      7902 FORD               20       3000    .928571429
      7839 KING               10       5000             1

SQL>

Adding the partitioning clause allows us to display the cumulative distribution of each employee within a partition.

SELECT empno,
       ename,
       deptno,
       sal,
       CUME_DIST() OVER (PARTITION BY deptno ORDER BY sal) AS cume_dist_sal_by_dept
FROM   emp;

     EMPNO ENAME          DEPTNO        SAL CUME_DIST_SAL_BY_DEPT
---------- ---------- ---------- ---------- ---------------------
      7934 MILLER             10       1300            .333333333
      7782 CLARK              10       2450            .666666667
      7839 KING               10       5000                     1
      7369 SMITH              20        800                    .2
      7876 ADAMS              20       1100                    .4
      7566 JONES              20       2975                    .6
      7788 SCOTT              20       3000                     1
      7902 FORD               20       3000                     1
      7900 JAMES              30        950            .166666667
      7654 MARTIN             30       1250                    .5
      7521 WARD               30       1250                    .5
      7844 TURNER             30       1500            .666666667
      7499 ALLEN              30       1600            .833333333
      7698 BLAKE              30       2850                     1

SQL>

Quick Links

The "*" indicates the function supports the full analytic syntax, including the windowing clause.

AVG * CLUSTER_DETAILS CLUSTER_DISTANCE CLUSTER_ID CLUSTER_PROBABILITY
CLUSTER_SET CORR * COUNT * COVAR_POP * COVAR_SAMP *
CUME_DIST DENSE_RANK FEATURE_DETAILS FEATURE_ID FEATURE_SET
FEATURE_VALUE FIRST FIRST_VALUE * LAG LAST
LAST_VALUE * LEAD LISTAGG MATCH_RECOGNIZE MAX *
MEDIAN MIN * NTH_VALUE * NTILE PERCENT_RANK
PERCENTILE_CONT PERCENTILE_DISC PREDICTION PREDICTION_COST PREDICTION_DETAILS
PREDICTION_PROBABILITY PREDICTION_SET RANK RATIO_TO_REPORT REGR_ (Linear Regression) Functions *
ROW_NUMBER STDDEV * STDDEV_POP * STDDEV_SAMP * SUM *
VAR_POP * VAR_SAMP * VARIANCE * String Aggregation Top-N Queries

For more information see:

Hope this helps. Regards Tim...

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