Analytic Functions in Oracle
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Contents
Overview and Introduction
How Analytic Functions Work
The Syntax
Examples
Calculate a running Total
Top-N Queries
Example 1
Example 2
Windows
Range Windows
Compute average salary for defined range
Row Windows
Accessing Rows Around Your Current Row
LAG
LEAD
Determine the First Value / Last Value of a Group
Crosstab or Pivot Queries
ROLLUP and RANK Examples
CUBE
Grouping Functions:
Grouping_ID
GROUP_ID
Grouping SETS
More Examples with EMP Table
Analytic Functions are designed to address such problems as "Calculate a running total", "Find percentages within a group", "Top-N queries", "Compute a moving average" and many more.
Most of these problems can be solved using standard PL/SQL, however the performance is often not what it should be.
Analytic Functions add extensions to the SQL language that not only make these operations easier to code; they make them faster than could be achieved with pure SQL or PL/SQL.
How Analytic Functions Work ?
Analytic functions compute an aggregate value based on a group of rows. They differ from aggregate functions (like select AVG(sal) from emp) in that they return multiple rows for each group.
Analytic functions also operate on subsets of rows, similar to aggregate functions in GROUP BY queries, but they do not reduce the number of rows returned by the query.
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.
Select MAX() OVER ()
The OVER() statement signals a start of an Analytic function. That is what differentiates an Analytical Function from a regular Oracle SQL functionSelect MAX() OVER(partition by field1).
The portioning clause is used to setup the group of data that the Analytic function would be applied to.Select MAX() OVER(Partition by field order by)
Order by specify the order of the window in the group by statement. The Order by clause is a keyword in the Oracle Analytic syntax that is requirement for using some Analytic functions
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.Example:
SELECT empno, deptno, sal,
AVG(sal) OVER (PARTITION BY deptno) AS avg_dept_sal
FROM emp;
EMPNO DEPTNO SAL AVG_DEPT_SAL
--------- ---------- ---------- ------------
7782 10 2450 2916.66667
7839 10 5000 2916.66667
7934 10 1300 2916.66667
7566 20 2975 2175
7902 20 3000 2175
7876 20 1100 2175
7369 20 800 2175
7788 20 3000 2175
7521 30 1250 1566.66667
7844 30 1500 1566.66667
7499 30 1600 1566.66667
7900 30 950 1566.66667
7698 30 2850 1566.66667
7654 30 1250 1566.66667
This time AVG is an analytic function, operating on the group of rows defined by the contents of the OVER clause. This group of rows is known as a window, which is why analytic functions are sometimes referred to as window[ing] functions. Notice how the AVG function is still reporting the departmental average, like it did in the GROUP BY query, but the result is present in each row, rather than reducing the total number of rows returned. This is because analytic functions are performed on a result set after all join, WHERE, GROUP BY and HAVING clauses are complete, but before the final ORDER BY operation is performed.
The Syntax
There are some variations in the syntax of the individual analytic functions, but the basic syntax for an analytic function is as follows.
analytic_function([ arguments ]) OVER (analytic_clause)The analytic_clause breaks down into the following optional elements.
[ query_partition_clause ] [ order_by_clause [ windowing_clause ] ]The sub-elements of the analytic_clause each have their own syntax diagrams. Rather than repeat the syntax diagrams, the following sections describe what each section of the analytic_clause is used for.
So here is the FULL sentence:
Analytic-Function(<Argument>,<Argument>,...)
OVER (
<Query-Partition-Clause>
<Order-By-Clause>
<Windowing-Clause>
)
Analytic-Functions
Specify the name of an analytic function, Oracle actually provides many analytic functions such as AVG, CORR, COVAR_POP, COVAR_SAMP, COUNT, CUME_DIST,DENSE_RANK, FIRST, FIRST_VALUE, LAG, LAST, LAST_VALUE, LEAD, MAX, MIN, NTILE, PERCENT_RANK, PERCENTILE_CONT, PERCENTILE_DISC, RANK, RATIO_TO_REPORT,STDDEV, STDDEV_POP, STDDEV_SAMP, SUM, VAR_POP, VAR_SAMP, VARIANCE.Arguments
Analytic functions take 0 to 3 arguments.Query-Partition-Clause
The PARTITION BY clause logically breaks a single result set into N groups, according to the criteria set by the partition expressions. The words "partition" and "group" are used synonymously here. The analytic functions are applied to each group independently, they are reset for each group. If the query_partition_clause is omitted, the whole result set is treated as a single partition.
Example: The following query uses an empty OVER clause, so the average presented is based on all the rows of the result set.
SELECT empno, deptno, sal,
AVG(sal) OVER () AS avg_sal
FROM emp;
EMPNO DEPTNO SAL AVG_SAL
---------- ---------- ---------- ----------
7369 20 800 2073.21429
7499 30 1600 2073.21429
7521 30 1250 2073.21429
7566 20 2975 2073.21429
7654 30 1250 2073.21429
7698 30 2850 2073.21429
7782 10 2450 2073.21429
7788 20 3000 2073.21429
7839 10 5000 2073.21429
7844 30 1500 2073.21429
7876 20 1100 2073.21429
7900 30 950 2073.21429
7902 20 3000 2073.21429
7934 10 1300 2073.21429
If we change the OVER clause to include a query_partition_clause based on the department, the averages presented are specifically for the department the employee belongs too.
SELECT empno, deptno, sal,
AVG(sal) OVER (PARTITION BY deptno) AS avg_dept_sal
FROM emp;
EMPNO DEPTNO SAL AVG_DEPT_SAL
--------- ---------- ---------- ------------
7782 10 2450 2916.66667
7839 10 5000 2916.66667
7934 10 1300 2916.66667
7566 20 2975 2175
7902 20 3000 2175
7876 20 1100 2175
7369 20 800 2175
7788 20 3000 2175
7521 30 1250 1566.66667
7844 30 1500 1566.66667
7499 30 1600 1566.66667
7900 30 950 1566.66667
7698 30 2850 1566.66667
7654 30 1250 1566.66667Order-By-Clause
The order_by_clause is used to order rows, or siblings, within a partition. So if an analytic function is sensitive to the order of the siblings in a partition you should include an order_by_clause. The following query uses the FIRST_VALUE function to return the first salary reported in each department. Notice we have partitioned the result set by the department, but there is no order_by_clause.
SELECT empno, deptno, sal,
FIRST_VALUE(sal IGNORE NULLS) OVER (PARTITION BY deptno) AS first_sal_in_dept
FROM emp;
EMPNO DEPTNO SAL FIRST_SAL_IN_DEPT
---------- ---------- ---------- -----------------
7782 10 2450 2450
7839 10 5000 2450
7934 10 1300 2450
7566 20 2975 2975
7902 20 3000 2975
7876 20 1100 2975
7369 20 800 2975
7788 20 3000 2975
7521 30 1250 1250
7844 30 1500 1250
7499 30 1600 1250
7900 30 950 1250
7698 30 2850 1250
7654 30 1250 1250
Now compare the values of the FIRST_SAL_IN_DEPT column when we include an order_by_clause to order the siblings by ascending salary.
SELECT empno, deptno, sal,
FIRST_VALUE(sal IGNORE NULLS) OVER (PARTITION BY deptno ORDER BY sal ASC NULLS LAST) AS first_val_in_dept
FROM emp;
EMPNO DEPTNO SAL FIRST_VAL_IN_DEPT
---------- ---------- ---------- -----------------
7934 10 1300 1300
7782 10 2450 1300
7839 10 5000 1300
7369 20 800 800
7876 20 1100 800
7566 20 2975 800
7788 20 3000 800
7902 20 3000 800
7900 30 950 950
7654 30 1250 950
7521 30 1250 950
7844 30 1500 950
7499 30 1600 950
7698 30 2850 950
In this case the "ASC NULLS LAST" keywords are unnecessary as ASC is the default for an order_by_clause and NULLS LAST is the default for ASC orders. When ordering by DESC, the default is NULLS FIRST.
It is important to understand how the order_by_clause affects display order. The order_by_clause is guaranteed to affect the order of the rows as they are processed by the analytic function, but it may not always affect the display order. As a result, you must always use a conventional ORDER BY clause in the query if display order is important. Do not rely on any implicit ordering done by the analytic function. Remember, the conventional ORDER BY clause is performed after the analytic processing, so it will always take precedence.Windowing-Clause
The windowing_clause gives some analytic functions a further degree of control over this window within the current partition. The windowing_clause is an extension of the order_by_clause and as such, it can only be used if an order_by_clause is present. The windowing_clause has two basic forms.
RANGE BETWEEN start_point AND end_point
ROWS BETWEEN start_point AND end_point
Possible values for "start_point" and "end_point" are:
- UNBOUNDED PRECEDING : The window starts at the first row of the partition. Only available for start points.
- UNBOUNDED FOLLOWING : The window ends at the last row of the partition. ONly available for end points.
- CURRENT ROW : The window starts or ends at the current row. Can be used as start or end point.
- value_expr PRECEDING : An physical or logical offset before the current row using a constant or expression that evaluates to a positive numerical value. When used with RANGE, it can also be an interval literal if the order_by_clause uses a DATE column.
- value_expr FOLLOWING : As above, but an offset after the current row.
For analytic functions that support the windowing_clause, the default action is "RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW". The following query is similar to one used previously to report the employee salary and average department salary, but now we have included an order_by_clause so we also get the default windowing_clause. Notice how the average salary is now calculated using only the employees from the same department up to and including the current row.
SELECT empno, deptno, sal,
AVG(sal) OVER (PARTITION BY deptno ORDER BY sal) AS avg_dept_sal_sofar
FROM emp;
EMPNO DEPTNO SAL AVG_DEPT_SAL_SOFAR
-------- ---------- ---------- ------------------
7934 10 1300 1300
7782 10 2450 1875
7839 10 5000 2916.66667
7369 20 800 800
7876 20 1100 950
7566 20 2975 1625
7788 20 3000 2175
7902 20 3000 2175
7900 30 950 950
7654 30 1250 1150
7521 30 1250 1150
7844 30 1500 1237.5
7499 30 1600 1310
7698 30 2850 1566.66667
The following query shows one method for accessing data from previous and following rows within the current row using the windowing_clause. This can also be accomplished with LAG and LEAD.
SELECT empno, deptno, sal,
FIRST_VALUE(sal) OVER (ORDER BY sal ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS previous_sal,
LAST_VALUE(sal) OVER (ORDER BY sal ROWS BETWEEN CURRENT ROW AND 1 FOLLOWING) AS next_sal
FROM emp;
EMPNO DEPTNO SAL PREVIOUS_SAL NEXT_SAL
--------- ---------- ---------- ------------ ----------
7369 20 800 800 950
7900 30 950 800 1100
7876 20 1100 950 1250
7521 30 1250 1100 1250
7654 30 1250 1250 1300
7934 10 1300 1250 1500
7844 30 1500 1300 1600
7499 30 1600 1500 2450
7782 10 2450 1600 2850
7698 30 2850 2450 2975
7566 20 2975 2850 3000
7788 20 3000 2975 3000
7902 20 3000 3000 5000
7839 10 5000 3000 5000
More information on windows can be found here.
Analytic Function Examples
Simple Example
Select * from ( Select cust_name, sum(clm_amt)clm_amt
from customer
group by cust_name
order by clm_amt desc desc ) v
)
and rownum < 11;
Simple Example result
CUST_NAME CLM_AMT
------------------------------ -----------
XYZ 100,000,000
Lexus Corp 80,000,000
First America 60,000,000
Yelp 78,000,000
ABC 75,000,000
Omega Int. 74,000,000
S Corp 70,000,000
Acme 25,000,000
Sun Enterprise 23,000,000
Film studio 17,000,000
Analytic Version
select cust_name, SUM(clm_amt) OVER (partition by cust_name) clm_amt;
In the following example we‘ll show GROUPING SETS (Listing 1), GROUP BY ROLLUP (Listing 2), and GROUP BY CUBE (Listing 3) to see what we get with each. We‘ll use the standard SCOTT.EMP table to do this. The first query will show us the sum of salaries by DEPTNO and by JOB. We need to use the GROUPING_ID function to determine what aggregation each row represents. It might not be obvious why we would need this in general from the example, but consider what would happen if DEPTNO or JOB were NULLABLE. There would be no way to distinguish the detail row from the aggregated row.
The GROUPING_ID function returns a 0 or 1 when given a single column. (In this case, it works just like the GROUPING function.) If the return value is 0, indicating a detail record value for that particular column, then the column in question was not aggregated over (was not collapsed). If the function returns 1, then the column in question was aggregated over—any aggregates in the SELECT list would have been computed over that entire column‘s set of values. GROUPING_ID differs from GROUPING, in that you can send a list of columns and the GROUPING_ID function will treat the list as bits and return a decimal number. That means that the call to GROUPING_ID(a,b,c)might return any number between 0 and 7, because different 0/1 combinations are returned. Given that fact, we can use a CASE statement in the query to see if the row is a detail row for DEPTNO, for JOB, for neither, or for both.
Using GROUPING SETS in Listing 1, we asked for GROUP BY only on DEPTNO and then only on JOB. So, that one query was like running the following query
select deptno, null, sum(sal)
from emp group by deptno
union all
select null, job, sum(sal)
from emp group by job;
Code Listing 1: Using GROUPING_ID with GROUPING SETS
select deptno, job, sum(sal),
grouping_id(deptno) gid_d,
grouping_id(job) gid_j,
grouping_id(deptno,job) gid_dj,
bin_to_num(grouping_id(deptno),grouping_id(job)) b2n,
case when grouping_id(deptno,job) = 0
then ‘Dtl both‘
when grouping_id(deptno,job) = 1
then ‘Agg over job‘
when grouping_id(deptno,job) = 2
then ‘Agg over deptno‘
when grouping_id(deptno,job) = 3
then ‘Agg over both‘
end what
from emp
group by grouping sets( (deptno), (job) );
DEPTNO JOB SUM(SAL) GID_D GID_J GID_DJ B2N WHAT
________ ____________ ___________ _______ ________ ________ ____ __________________
10 8750 0 1 1 1 Agg over job
20 0875 0 1 1 1 Agg over job
30 9400 0 1 1 1 Agg over job
ANALYST 6000 1 0 2 2 Agg over deptno
CLERK 4150 1 0 2 2 Agg over deptno
MANAGER 8275 1 0 2 2 Agg over deptno
PRESIDENT 5000 1 0 2 2 Agg over deptno
SALESMAN 5600 1 0 2 2 Agg over deptno
. . . but without having to make two passes on the EMP table, as would be the case with the UNION ALL.
In looking at the columns involved in the query in Listing 1, we can see that the function GROUPING(column_name) shows us when a column is aggregated over or preserved as a detail record. When GROUPING(deptno) = 0, DEPTNO is preserved in the output. When it is 1, it is aggregated over. However, we have two columns in this set we are aggregating by, for a total of four possible 0/1 combinations. (In this query, only two are possible.) Using the GROUPING_ID function on this vector of columns, we can easily see what each row represents. I‘ve also included the alternative, more verbose way to accomplish this—the BIN_TO_NUM() function, to which we can send a list of 0s and 1s and get back a decimal number as well. I‘m pretty sure you‘ll agree that GROUPING_ID(c1,c2,c3) is easier than the corresponding BIN_TO_NUM call with three GROUPING calls.
In Listing 2, we take a look at GROUP BY ROLLUP. A rollup by the two columns DEPTNO and JOB will produce
- Detail records by DEPTNO, JOB (sum of SAL for each DEPTNO/JOB combination).
- A summary record for each DEPTNO over JOB (like a subtotal).
- A summary record over DEPTNO and JOB—a single aggregate for the entire result. Listing 2 shows the query and the results.
Code Listing 2: Using GROUPING_ID with GROUP BY ROLLUP
select deptno, job, sum(sal),
grouping_id(deptno) gid_d,
grouping_id(job) gid_j,
case when grouping_id(deptno,job) = 0
then ‘Dtl both‘
when grouping_id(deptno,job) = 1
then ‘Agg over job‘
when grouping_id(deptno,job) = 2
then ‘Agg over deptno‘
when grouping_id(deptno,job) = 3
then ‘Agg over both‘
end what
from emp
group by rollup( deptno, job );
DEPTNO JOB SUM(SAL) GID_D GID_J WHAT
________ _____________ ___________ _______ _______ ________________
10 CLERK 1300 0 0 Dtl both
10 MANAGER 2450 0 0 Dtl both
10 PRESIDENT 5000 0 0 Dtl both
10 8750 0 1 Agg over job
20 CLERK 1900 0 0 Dtl both
20 ANALYST 6000 0 0 Dtl both
20 MANAGER 2975 0 0 Dtl both
20 10875 0 1 Agg over job
30 CLERK 950 0 0 Dtl both
30 MANAGER 2850 0 0 Dtl both
30 SALESMAN 5600 0 0 Dtl both
30 9400 0 1 Agg over job
29025 1 1 Agg over both
A rollup is sort of like a running total report, and GROUPING_ID tells us when the rollups happened. So the data is sorted by DEPTNO, JOB, and we have subtotals by DEPTNO (aggregated over JOB) and by DEPTNO, JOB (aggregated over both) along with the details by DEPTNO/JOB.
As you can see in Listing 2, the GROUPING_ID function was useful in telling us when we were dealing with a rolled-up record and the level of detail we could expect in that record.
Last, we‘ll look at GROUP BY CUBE. CUBE is similar to ROLLUP, in that you get the same three record types as shown in Listing 2 but also get all possible aggregations. CUBE grouping by DEPTNO and JOB will give you records by all of the following:
- DEPTNO and JOB
- DEPTNO over JOB
- JOB over DEPTNO
- A single total aggregate
You get every possible aggregate. Listing 3 shows the syntax and output and how to use the GROUPING_ID function to see what the level of detail is for each row. It is interesting to note that GROUP BY CUBE produces a superset of the rows we observed in the first query (in Listing 1). You could use GROUPING_ID with CUBE to generate the same result set as the original grouping sets query. That is, adding
having (grouping_id(deptno,job)=2
or (grouping_id(deptno,job)=1
. . . to the GROUP BY CUBE query would cause it to be the logical equivalent of the GROUPING SETS query. But you shouldn‘t do that! If you need only some of the aggregates, use GROUPING SETS to get just the ones you need computed and avoid computing the others altogether. It would be fair to say that GROUPING_ID doesn‘t avoid multiple grouping functions but GROUPING SETS does. However, GROUPING_ID plays an important role in seeing what data is what.
Code Listing 3: Using GROUPING_ID with GROUP BY CUBE
select deptno, job, sum(sal),
grouping_id(deptno) gid_d,
grouping_id(job) gid_j,
case when grouping_id(deptno,job) = 0
then ‘Dtl both‘
when grouping_id(deptno,job) = 1
then ‘Agg over job‘
when grouping_id(deptno,job) = 2
then ‘Agg over deptno‘
when grouping_id(deptno,job) = 3
then ‘Agg over both‘
end what
from emp
group by cube( deptno, job )
order by grouping_id(deptno,job) ;
DEPTNO JOB SUM(SAL) GID_D GID_J WHAT
_________ _____________ ___________ _______ _______ ___________________
10 CLERK 1300 0 0 Dtl both
10 MANAGER 2450 0 0 Dtl both
10 PRESIDENT 5000 0 0 Dtl both
20 CLERK 1900 0 0 Dtl both
30 CLERK 950 0 0 Dtl both
30 SALESMAN 5600 0 0 Dtl both
30 MANAGER 2850 0 0 Dtl both
20 MANAGER 2975 0 0 Dtl both
20 ANALYST 6000 0 0 Dtl both
10 8750 0 1 Agg over job
20 10875 0 1 Agg over job
30 9400 0 1 Agg over job
CLERK 4150 1 0 Agg over deptno
ANALYST 6000 1 0 Agg over deptno
MANAGER 8275 1 0 Agg over deptno
PRESIDENT 5000 1 0 Agg over deptno
SALESMAN 5600 1 0 Agg over deptno
29025 1 1 Agg over both
Example: Calculate a running Total
This example shows the cumulative salary within a departement row by row, with each row including a summation of the prior rows salary.
set autotrace traceonly explain
break on deptno skip 1
column ename format A6
column deptno format 999
column sal format 99999
column seq format 999SELECT ename "Ename", deptno "Deptno", sal "Sal",
SUM(sal) OVER (ORDER BY deptno, ename) "Running Total",
SUM(SAL) OVER (PARTITION BY deptno
ORDER BY ename) "Dept Total",
ROW_NUMBER()
OVER (PARTITION BY deptno ORDER BY ENAME) "Seq"
FROM emp
ORDER BY deptno, ename
/Ename Deptno Sal Running Total Dept Total Seq
------ ------ ------ ------------- ---------- ----
CLARK 10 2450 2450 2450 1
KING 5000 7450 7450 2
MILLER 1300 8750 8750 3
ADAMS 20 1100 9850 1100 1
FORD 3000 12850 4100 2
JONES 2975 15825 7075 3
SCOTT 3000 18825 10075 4
SMITH 800 19625 10875 5
ALLEN 30 1600 21225 1600 1
BLAKE 2850 24075 4450 2
JAMES 950 25025 5400 3
MARTIN 1250 26275 6650 4
TURNER 1500 27775 8150 5
WARD 1250 29025 9400 6Execution Plan
---------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE
1 0 WINDOW (SORT)
2 1 TABLE ACCESS (FULL) OF ‘EMP‘
Statistics
---------------------------------------------------
0 recursive calls
0 db block gets
3 consistent gets
0 physical reads
0 redo size
1658 bytes sent via SQL*Net to client
503 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
14 rows processedThe example shows how to calculate a "Running Total" for the entire query. This is done using the entire ordered result set, via SUM(sal) OVER (ORDER BY deptno, ename).
Further, we were able to compute a running total within each department, a total that would be reset at the beginning of the next department. The PARTITION BY deptno in that SUM(sal) caused this to happen, a partitioning clause was specified in the query in order to break the data up into groups.
The ROW_NUMBER() function is used to sequentially number the rows returned in each group, according to our ordering criteria (a "Seq" column was added to in order to display this position).
The execution plan shows, that the whole query is very well performed with only 3 consistent gets, this can never be accomplished with standard SQL or even PL/SQL.
Top-N Queries
How can we get the Top-N records by some set of fields ?
Prior to having access to these analytic functions, questions of this nature were extremely difficult to answer. There are some problems with Top-N queries however; mostly in the way people phrase them. It is something to be careful about when designing reports. Consider this seemingly sensible request:
I would like the top three paid sales reps by departmentUsing the "traditional approach you can perform:
select * from
(your_query)
where rownum <= 10;The problem with this question is that it is ambiguous because of repeated values, there might be four people who all make the same salary, what should we do then ?
Let‘s look at three examples, all use the well known table EMP.
Example 1
Let‘s look at what ROW_NUMBER can do. Here is an example query using ROW_NUMBER to assign an increasing number to each row in the EMP table after sorting by SAL DESC:
select ename, sal,
row_number() over (order by sal desc) rn
from emp
order by sal desc;
ENAME SAL RN
----- ---- --
KING 5000 1
FORD 3000 2
SCOTT 3000 3
JONES 2975 4
.
.
.
JAMES 950 13
SMITH 800 14I can apply a predicate to ROW_NUMBER after it is assigned. For example
select * from (select ename, sal,
row_number() over (order by sal desc) rn
from emp)
where rn <= 3
order by sal desc;
ENAME SAL RN
----- ---- --
KING 5000 1
SCOTT 3000 2
FORD 3000 3So, that demonstrates how to perform a top-n query by using ROW_NUMBER and also points out a general issue with top-n queries. If you look at that result, you see two rows with the value 3000. What if, in the EMP table, three people, instead of just two, had a salary of 3000? The result obtained by the above query would be ambiguous—I would get three records, but the records I retrieved would be somewhat random. We will analyze that on the example 2:
Another Example:
Sort the sales people by salary from greatest to least. Give the first three rows. If there are less then three people in a department, this will return less than three records.set autotrace on explain
break on deptno skip 1SELECT * FROM (SELECT deptno, ename, sal, ROW_NUMBER()
OVER (PARTITION BY deptno
ORDER BY sal DESC)
Top3 FROM emp)
WHERE Top3 <= 3;DEPTNO ENAME SAL TOP3
---------- ---------- ---------- ----------
10 KING 5000 1
CLARK 2450 2
MILLER 1300 3
20 SCOTT 3000 1
FORD 3000 2
JONES 2975 3
30 BLAKE 2850 1
ALLEN 1600 2
TURNER 1500 3This query works by sorting each partition (or group, which is the deptno), in a descending order, based on the salary column and then assigning a sequential row number to each row in the group as it is processed. The use of a WHERE clause after doing this to get just the first three rows in each partition.
Example 2
Bearing this in mind, I can use other analytic functions to remove the ambiguity from example 1. They will do so, but the analytic functions might return more than n rows. In my opinion, when the attribute I order by is not unique, I want my query to return all of the relevant records—not just the first narbitrary ones. To that end, I can use the RANK and DENSE_RANK analytic functions. Let‘s take a look at what they do:
select ename,sal,
row_number() over (order by sal desc)rn,
rank() over (order by sal desc)rnk,
dense_rank() over (order by sal desc)drnk
from emp
order by sal desc;
ENAME SAL RN RNK DRNK
----- ---- -- --- ----
KING 5000 1 1 1
FORD 3000 2 2 2
SCOTT 3000 3 2 2
JONES 2975 4 4 3
BLAKE 2850 5 5 4
CLARK 2450 6 6 5
.
.
.The main things to note here are the following:
- ROW_NUMBER assigns contiguous, unique numbers from 1..N to a result set.
RANK does not assign unique numbers—FORD and SCOTT tied for second place—nor does it assign contiguous numbers. No record was assigned the value of 3, because two people tied for second place, and no one came in third, according to RANK.
DENSE_RANK, like RANK, does not assign unique numbers, but it does assign contiguous numbers. Even though two records tied for second place, there is a third-place record.
You can use RANK and DENSE_RANK in the same way you would use ROW_NUMBER to restrict the number of rows returned, but obviously you‘ll get subtly different results. For example
select * from (select ename,sal,
dense_rank() over (order by sal desc)drnk
from emp)
where drnk <= 3
order by sal desc;
ENAME SAL DRNK
----- ---- ----
KING 5000 1
SCOTT 3000 2
FORD 3000 2
JONES 2975 3That query returns "the set of people who make the top three salaries," which is likely the desired result. Getting the first three records from EMP after sorting by SAL is rather arbitrary, because using exactly the same set of data, simply inserted in different orders, you could observe different result sets with ROW_NUMBER (because SAL is not unique). Using DENSE_RANK, however, I don‘t get precisely three records but, instead, a repeatable (deterministic) result set. And I suspect that I retrieve the set the end user really meant to retrieve—the set of people making the top three salaries.
Another Example:
Give me the set of sales people who make the top 3 salaries - that is, find the set of distinct salary amounts, sort them, take the largest three, and give me everyone who makes one of those values.SELECT * FROM (SELECT deptno, ename, sal,
DENSE_RANK() OVER (PARTITION BY deptno ORDER BY sal desc)
TopN FROM emp)
WHERE TopN <= 3
ORDER BY deptno, sal DESC;
DEPTNO ENAME SAL TOPN
---------- ---------- ---------- ----------
10 KING 5000 1
CLARK 2450 2
MILLER 1300 3
20 SCOTT 3000 1 <--- !
FORD 3000 1 <--- !
JONES 2975 2
ADAMS 1100 3
30 BLAKE 2850 1
ALLEN 1600 2
30 TURNER 1500 3Here the DENSE_RANK function was used to get the top three salaries. We assigned the dense rank to the salary column and sorted it in a descending order.
The DENSE_RANK function computes the rank of a row in an ordered group of rows. The ranks are consecutive integers beginning with 1. The largest rank value is the number of unique values returned by the query. Rank values are not skipped in the event of ties. Rows with equal values for the ranking criteria receive the same rank.
The DENSE_RANK function does not skip numbers and will assign the same number to those rows with the same value. Hence, after the result set is built in the inline view, we can simply select all of the rows with a dense rank of three or less, this gives us everyone who makes the top three salaries by department number.
The windowing clause gives us a way to define a sliding or anchored window of data, on which the analytic function will operate, within a group. The default window is an anchored window that simply starts at the first row of a group an continues to the current row.
We can set up windows based on two criteria: RANGES of data values or ROWS offset from the current row. It can be said, that the existance of an ORDER BY in an analytic function will add a default window clause of RANGE UNBOUNDED PRECEDING. That says to get all rows in our partition that came before us as specified by the ORDER BY clause.
Let‘s look at an example with a sliding window within a group and compute the sum of the current row‘s SAL column plus the previous 2 rows in that group. If we need a report that shows the sum of the current employee‘s salary with the preceding two salaries within a departement, it would look like this.break on deptno skip 1
column ename format A6
column deptno format 999
column sal format 99999
SELECT deptno "Deptno", ename "Ename", sal "Sal",
SUM(SAL) OVER (PARTITION BY deptno
ORDER BY ename
ROWS 2 PRECEDING) "Sliding Total"
FROM emp
ORDER BY deptno, ename;Deptno Ename Sal Sliding Total
------ ------ ------ -------------
10 CLARK 2450 2450
KING 5000 7450
MILLER 1300 8750
20 ADAMS 1100 1100
FORD 3000 4100
JONES 2975 7075 ^
SCOTT 3000 8975 |
SMITH 800 6775 -- Sliding Window
30 ALLEN 1600 1600
BLAKE 2850 4450
JAMES 950 5400
MARTIN 1250 5050
TURNER 1500 3700
WARD 1250 4000The partition clause makes the SUM (sal) be computed within each department, independent of the other groups. Tthe SUM (sal) is ‘ reset ‘ as the department changes. The ORDER BY ENAME clause sorts the data within each department by ENAME; this allows the window clause: ROWS 2 PRECEDING, to access the 2 rows prior to the current row in a group in order to sum the salaries.
For example, if you note the SLIDING TOTAL value for SMITH is 6 7 7 5, which is the sum of 800, 3000, and 2975. That was simply SMITH‘s row plus the salary from the preceding two rows in the window.
Range Windows
Range windows collect rows together based on a WHERE clause. If I say ‘ range 5 preceding ‘ for example, this will generate a sliding window that has the set of all preceding rows in the group such that they are within 5 units of the current row. These units may either be numeric comparisons or date comparisons and it is not valid to use RANGE with datatypes other than numbers and dates.
Example
Count the employees which where hired within the last 100 days preceding the own hiredate. The range window goes back 100 days from the current row‘s hiredate and then counts the rows within this range. The solution ist to use the following window specification:COUNT(*) OVER (ORDER BY hiredate ASC RANGE 100 PRECEDING)
column ename heading "Name" format a8
column hiredate heading "Hired" format a10
column hiredate_pre heading "Hired-100" format a10
column cnt heading "Cnt" format 99SELECT ename, hiredate, hiredate-100 hiredate_pre,
COUNT(*) OVER (ORDER BY hiredate ASC
RANGE 100 PRECEDING) cnt
FROM emp
ORDER BY hiredate ASC;Name Hired Hired-100 Cnt
-------- ---------- ---------- ---
SMITH 17-DEC-80 08-SEP-80 1
ALLEN 20-FEB-81 12-NOV-80 2
WARD 22-FEB-81 14-NOV-80 3
JONES 02-APR-81 23-DEC-80 3
BLAKE 01-MAY-81 21-JAN-81 4
CLARK 09-JUN-81 01-MAR-81 3
TURNER 08-SEP-81 31-MAY-81 2
MARTIN 28-SEP-81 20-JUN-81 2
KING 17-NOV-81 09-AUG-81 3
JAMES 03-DEC-81 25-AUG-81 5
FORD 03-DEC-81 25-AUG-81 5
MILLER 23-JAN-82 15-OCT-81 4
SCOTT 09-DEC-82 31-AUG-82 1
ADAMS 12-JAN-83 04-OCT-82 2We ordered the single partition by hiredate ASC. If we look for example at the row for CLARK we can see that his hiredate was 09-JUN-81, and 100 days prior to that is the date 01-MAR-81. If we look who was hired between 01-MAR-81 and 09-JUN-81, we find JONES (hired: 02-APR-81) and BLAKE (hired: 01-MAY-81). This are 3 rows including the current row, this is what we see in the column "Cnt" of CLARK‘s row.
Compute average salary for defined range
As an example, compute the average salary of people hired within 100 days before for each employee. The query looks like this:
column ename heading "Name" format a8
column hiredate heading "Hired" format a10
column hiredate_pre heading "Hired-100" format a10
column avg_sal heading "Avg-100" format 999999SELECT ename, hiredate, sal,
AVG(sal) OVER (ORDER BY hiredate ASC
RANGE 100 PRECEDING) avg_sal
FROM emp
ORDER BY hiredate ASC;Name Hired SAL Avg-100
-------- ---------- ---------- -------
SMITH 17-DEC-80 800 800
ALLEN 20-FEB-81 1600 1200
WARD 22-FEB-81 1250 1217
JONES 02-APR-81 2975 1942
BLAKE 01-MAY-81 2850 2169
CLARK 09-JUN-81 2450 2758
TURNER 08-SEP-81 1500 1975
MARTIN 28-SEP-81 1250 1375
KING 17-NOV-81 5000 2583
JAMES 03-DEC-81 950 2340
FORD 03-DEC-81 3000 2340
MILLER 23-JAN-82 1300 2563
SCOTT 09-DEC-82 3000 3000
ADAMS 12-JAN-83 1100 2050Look at CLARK again, since we understand his range window within the group. We can see that the average salary of 2758 is equal to (2975+2850+2450)/3. This is the average of the salaries for CLARK and the rows preceding CLARK, those of JONES and BLAKE. The data must be sorted in ascending order.
Row Windows
Row Windows are physical units; physical number of rows, to include in the window. For example you can calculate the average salary of a given record with the (up to 5) employees hired before them or after them as follows:
set numformat 9999
SELECT ename, hiredate, sal,
AVG(sal)
OVER (ORDER BY hiredate ASC ROWS 5 PRECEDING) AvgAsc,
COUNT(*)
OVER (ORDER BY hiredate ASC ROWS 5 PRECEDING) CntAsc,
AVG(sal)
OVER (ORDER BY hiredate DESC ROWS 5 PRECEDING) AvgDes,
COUNT(*)
OVER (ORDER BY hiredate DESC ROWS 5 PRECEDING) CntDes
FROM emp
ORDER BY hiredate;ENAME HIREDATE SAL AVGASC CNTASC AVGDES CNTDES
---------- --------- ----- ------ ------ ------ ------
SMITH 17-DEC-80 800 800 1 1988 6
ALLEN 20-FEB-81 1600 1200 2 2104 6
WARD 22-FEB-81 1250 1217 3 2046 6
JONES 02-APR-81 2975 1656 4 2671 6
BLAKE 01-MAY-81 2850 1895 5 2675 6
CLARK 09-JUN-81 2450 1988 6 2358 6
TURNER 08-SEP-81 1500 2104 6 2167 6
MARTIN 28-SEP-81 1250 2046 6 2417 6
KING 17-NOV-81 5000 2671 6 2392 6
JAMES 03-DEC-81 950 2333 6 1588 4
FORD 03-DEC-81 3000 2358 6 1870 5
MILLER 23-JAN-82 1300 2167 6 1800 3
SCOTT 09-DEC-82 3000 2417 6 2050 2
ADAMS 12-JAN-83 1100 2392 6 1100 1The window consist of up to 6 rows, the current row and five rows " in front of " this row, where " in front of " is defined by the ORDER BY clause. With ROW partitions, we do not have the limitation of RANGE partition - the data may be of any type and the order by may include many columns. Notice, that we selected out a COUNT(*) as well. This is useful just to demonstrate how many rows went into making up a given average. We can see clearly that for ALLEN‘s record, the average salary computation for people hired before him used only 2 records whereas the computation for salaries of people hired after him used 6.
Accessing Rows Around Your Current Row
Frequently you want to access data not only from the current row but the current row " in front of " or " behind " them. For example, let‘s say you need a report that shows, by department all of the employees; their hire date; how many days before was the last hire; how many days after was the next hire.
Using straight SQL this query would be difficult to write. Not only that but its performance would once again definitely be questionable. The approach I typically took in the past was either to " select a select " or write a PL/SQL function that would take some data from the current row and " find " the previous and next rows data. This worked, but introduce large overhead into both the development of the query and the run-time execution of the query.
Using analytic functions, this is easy and efficient to do.set echo on
column deptno format 99 heading Dep
column ename format a6 heading Ename
column hiredate heading Hired
column last_hire heading LastHired
column days_last heading DaysLast
column next_hire heading NextHire
column days_next heading NextDays
break on deptno skip 1
SELECT deptno, ename, hiredate,
LAG(hiredate,1,NULL)
OVER (PARTITION BY deptno
ORDER BY hiredate, ename) last_hire,
hiredate - LAG(hiredate,1,NULL)
OVER (PARTITION BY deptno
ORDER BY hiredate, ename) days_last,
LEAD(hiredate,1,NULL)
OVER (PARTITION BY deptno
ORDER BY hiredate, ename) next_hire,
LEAD(hiredate,1,NULL)
OVER (PARTITION BY deptno
ORDER BY hiredate, ename) - hiredate days_next
FROM emp
ORDER BY deptno, hiredate;Dep Ename Hired LastHired DaysLast NextHire NextDays
--- ------ --------- --------- -------- --------- --------
10 CLARK 09-JUN-81 17-NOV-81 161
KING 17-NOV-81 09-JUN-81 161 23-JAN-82 67
MILLER 23-JAN-82 17-NOV-81 67
20 SMITH 17-DEC-80 02-APR-81 106
JONES 02-APR-81 17-DEC-80 106 03-DEC-81 245
FORD 03-DEC-81 02-APR-81 245 09-DEC-82 371
SCOTT 09-DEC-82 03-DEC-81 371 12-JAN-83 34
ADAMS 12-JAN-83 09-DEC-82 34
30 ALLEN 20-FEB-81 22-FEB-81 2
WARD 22-FEB-81 20-FEB-81 2 01-MAY-81 68
BLAKE 01-MAY-81 22-FEB-81 68 08-SEP-81 130
TURNER 08-SEP-81 01-MAY-81 130 28-SEP-81 20
MARTIN 28-SEP-81 08-SEP-81 20 03-DEC-81 66
JAMES 03-DEC-81 28-SEP-81 66The LEAD and LAG routines could be considered a way to " index into your partitioned group ". Using these functions you can access any individual row. Notice for example in the above printout, it shows that the record for KING includes the data (in bold red font) from the prior row (LAST HIRE) and the next row (NEXT-HIRE). We can access the fields in records preceding or following the current record in an ordered partition easily.
LAG ( value_expr [, offset] [, default] )
OVER ( [query_partition_clause] order_by_clause )LAG provides access to more than one row of a table at the same time without 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. If you do not specify offset, then its default is 1. The optional default value is returned if the offset goes beyond the scope of the window. If you do not specify default, then its default value is null. The following example provides, for each person in the EMP table, the salary of the employee hired just before:
SELECT ename,hiredate,sal,
LAG(sal, 1, 0) OVER (ORDER BY hiredate) AS PrevSal
FROM emp
WHERE job = ‘CLERK‘;Ename Hired SAL PREVSAL
------ --------- ----- -------
SMITH 17-DEC-80 800 0
JAMES 03-DEC-81 950 800
MILLER 23-JAN-82 1300 950
ADAMS 12-JAN-83 1100 1300
LEAD ( value_expr [, offset] [, default] )
OVER ( [query_partition_clause] order_by_clause )
LEAD provides access to more than one row of a table at the same time without 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 beyond that position. If you do not specify offset, then its default is 1. The optional default value is returned if the offset goes beyond the scope of the table. If you do not specify default, then its default value is null. The following example provides, for each employee in the EMP table, the hire date of the employee hired just after:
SELECT ename, hiredate,
LEAD(hiredate, 1) OVER (ORDER BY hiredate) AS NextHired
FROM emp WHERE deptno = 30;Ename Hired NEXTHIRED
------ --------- ---------
ALLEN 20-FEB-81 22-FEB-81
WARD 22-FEB-81 01-MAY-81
BLAKE 01-MAY-81 08-SEP-81
TURNER 08-SEP-81 28-SEP-81
MARTIN 28-SEP-81 03-DEC-81
JAMES 03-DEC-81
Determine the First Value / Last Value of a Group
The FIRST_VALUE and LAST_VALUE functions allow you to select the first and last rows from a group. These rows are especially valuable because they are often used as the baselines in calculations.
Example
The following example selects, for each employee in each department, the name of the employee with the lowest salary.break on deptno skip 1
SELECT deptno, ename, sal,
FIRST_VALUE(ename)
OVER (PARTITION BY deptno
ORDER BY sal ASC) AS MIN_SAL_HAS
FROM emp
ORDER BY deptno, ename;DEPTNO ENAME SAL MIN_SAL_HAS
---------- ---------- ---------- -----------
10 CLARK 2450 MILLER
KING 5000 MILLER
MILLER 1300 MILLER
20 ADAMS 1100 SMITH
FORD 3000 SMITH
JONES 2975 SMITH
SCOTT 3000 SMITH
SMITH 800 SMITH
30 ALLEN 1600 JAMES
BLAKE 2850 JAMES
JAMES 950 JAMES
MARTIN 1250 JAMES
TURNER 1500 JAMES
WARD 1250 JAMESThe following example selects, for each employee in each department, the name of the employee with the highest salary.
SELECT deptno, ename, sal,
FIRST_VALUE(ename)
OVER (PARTITION BY deptno
ORDER BY sal DESC) AS MAX_SAL_HAS
FROM emp
ORDER BY deptno, ename;DEPTNO ENAME SAL MAX_SAL_HAS
---------- ---------- ---------- -----_-----
10 CLARK 2450 KING
KING 5000 KING
MILLER 1300 KING
20 ADAMS 1100 FORD
FORD 3000 FORD
JONES 2975 FORD
SCOTT 3000 FORD
SMITH 800 FORD
30 ALLEN 1600 BLAKE
BLAKE 2850 BLAKE
JAMES 950 BLAKE
MARTIN 1250 BLAKE
TURNER 1500 BLAKE
WARD 1250 BLAKEThe following example selects, for each employee in department 30 the name of the employee with the lowest salary using an inline view
SELECT deptno, ename, sal,
FIRST_VALUE(ename)
OVER (ORDER BY sal ASC) AS MIN_SAL_HAS
FROM (SELECT * FROM emp WHERE deptno = 30)DEPTNO ENAME SAL MIN_SAL_HAS
---------- ---------- ---------- -----------
30 JAMES 950 JAMES
MARTIN 1250 JAMES
WARD 1250 JAMES
TURNER 1500 JAMES
ALLEN 1600 JAMES
BLAKE 2850 JAMES
Crosstab or Pivot Queries
A crosstab query, sometimes known as a pivot query, groups your data in a slightly different way from those we have seen hitherto. A crosstab query can be used to get a result with three rows (one for each project), with each row having three columns (the first listing the projects and then one column for each year) -- like this:
Project 2001 2002
ID CHF CHF
-------------------------------
100 123.00 234.50
200 543.00 230.00
300 238.00 120.50Example:
Let‘s say you want to show the top 3 salary earners in each department as columns. The query needs to return exactly 1 row per department and the row would have 4 columns. The DEPTNO, the name of the highest paid employee in the department, the name of the next highest paid, and so on. Using analytic functions this almost easy, without analytic functions this was virtually impossible.SELECT deptno,
MAX(DECODE(seq,1,ename,null)) first,
MAX(DECODE(seq,2,ename,null)) second,
MAX(DECODE(seq,3,ename,null)) third
FROM (SELECT deptno, ename,
row_number()
OVER (PARTITION BY deptno
ORDER BY sal desc NULLS LAST) seq
FROM emp)
WHERE seq <= 3
GROUP BY deptno;DEPTNO FIRST SECOND THIRD
---------- ---------- ---------- ----------
10 KING CLARK MILLER
20 SCOTT FORD JONES
30 BLAKE ALLEN TURNERNote the inner query, that assigned a sequence (RowNr) to each employee by department number in order of salary.
SELECT deptno, ename, sal,
row_number()
OVER (PARTITION BY deptno
ORDER BY sal desc NULLS LAST) RowNr
FROM emp;DEPTNO ENAME SAL ROWNR
---------- ---------- ---------- ----------
10 KING 5000 1
10 CLARK 2450 2
10 MILLER 1300 3
20 SCOTT 3000 1
20 FORD 3000 2
20 JONES 2975 3
20 ADAMS 1100 4
20 SMITH 800 5
30 BLAKE 2850 1
30 ALLEN 1600 2
30 TURNER 1500 3
30 WARD 1250 4
30 MARTIN 1250 5
30 JAMES 950 6The DECODE in the outer query keeps only rows with sequences 1, 2 or 3 and assigns them to the correct "column". The GROUP BY gets rid of the redundant rows and we are left with our collapsed result. It may be easier to understand if you see the resultset without the aggregate function MAX grouped by deptno.
SELECT deptno,
DECODE(seq,1,ename,null) first,
DECODE(seq,2,ename,null) second,
DECODE(seq,3,ename,null) third
FROM (SELECT deptno, ename,
row_number()
OVER (PARTITION BY deptno
ORDER BY sal desc NULLS LAST) seq
FROM emp)
WHERE seq <= 3;DEPTNO FIRST SECOND THIRD
---------- ---------- ---------- ----------
10 KING
10 CLARK
10 MILLER
20 SCOTT
20 FORD
20 JONES
30 BLAKE
30 ALLEN
30 TURNERThe MAX aggregate function will be applied by the GROUP BY column DEPTNO. In any given DEPTNO above only one row will have a non-null value for FIRST, the remaining rows in that group will always be NULL. The MAX function will pick out the non-null row and keep that for us. Hence, the group by and MAX will collapse our resultset, removing the NULL values from it and giving us what we want.
Another example:
Return data from EMP table in Horizontal mode (Pivot Table)
With the addition of analytic functions in Oracle8i and the SYS_CONNECT_BY_PATH() function in Oracle9i, this became something rather easily in SQL.
Take the following approach:
1. Partition the data by DEPTNO and, for each DEPTNO, sort the data by ENAME, and assign a sequential number by using the ROW_NUMBER() analytic function.
2. Use a CONNECT BY query, starting with ROW_NUMBER() equal to 1 and connecting that record to the same DEPTNO value with ROW_NUMBER() equal to 2, and so on. So, eventually end up with a record that is the result of connecting 1 to 2 to 3 to 4, and so on, for each DEPTNO value.
3. Select just the "longest connect by path" for each DEPTNO value - the longest connect by path for each DEPTNO value will have all of the ENAME values gathered together.
The SYS_CONNECT_BY_PATH() function will return the list of concatenated ENAME values.
The query looks like this:
select deptno,
max(sys_connect_by_path
(ename, ‘ ‘ )) scbp
from (select deptno, ename,
row_number() over
(partition by deptno
order by ename) rn
from emp
)
start with rn = 1
connect by prior rn = rn-1
and prior deptno = deptno
group by deptno
order by deptno
/
DEPTNO SCBP
--------- ----------------------------------
10 CLARK KING MILLER
20 ADAMS FORD JONES SCOTT ...
30 ALLEN BLAKE JAMES MARTIN ...
ROLLUP and RANK Examples
Looking for a quick, efficient way to summarize the data stored in your database? The SQL ROLLUP and CUBE commands offer a valuable tool for gaining some quick and dirty insight into your data. ROLLUP and CUBE are SQL extensions.
Ver http://orafaq.com/node/56
The ROLLUP operation works on a set of columns you want to group. Just like the GROUP BY operation but aggregates a summary row for each group of columns supplied in its clause. Rollup gives the sum on the aggregate; it is used as an add-on to the GROUP BY clause. From the most detailed to a grand total and has the following basic syntax format of:
GROUP BY ROLLUP ([columns of interest separated by commas])
DDL to use with the examples:
create table Employee(
ID VARCHAR2(4 BYTE) NOT NULL,
First_Name VARCHAR2(10 BYTE),
Last_Name VARCHAR2(10 BYTE),
Start_Date DATE,
End_Date DATE,
Salary Number(8,2),
City VARCHAR2(10 BYTE),
Description VARCHAR2(15 BYTE)
)
/
-- prepare data
insert into Employee(ID, First_Name, Last_Name, Start_Date,End_Date,Salary, City,Description) values (‘01‘,‘Jason‘, ‘Martin‘, to_date(‘19960725‘,‘YYYYMMDD‘), to_date(‘20060725‘,‘YYYYMMDD‘), 1234.56, ‘Toronto‘, ‘Programme
insert into Employee(ID, First_Name, Last_Name, Start_Date,End_Date,Salary, City,Description) values(‘02‘,‘Alison‘, ‘Mathews‘,to_date(‘19760321‘,‘YYYYMMDD‘), to_date(‘19860221‘,‘YYYYMMDD‘), 6661.78, ‘Vancouver‘,‘Tester‘);
insert into Employee(ID, First_Name, Last_Name, Start_Date,End_Date,Salary, City,Description) values(‘03‘,‘James‘, ‘Smith‘, to_date(‘19781212‘,‘YYYYMMDD‘), to_date(‘19900315‘,‘YYYYMMDD‘), 6544.78, ‘Vancouver‘,‘Tester‘);
insert into Employee(ID, First_Name, Last_Name, Start_Date,End_Date,Salary, City,Description) values(‘04‘,‘Celia‘, ‘Rice‘, to_date(‘19821024‘,‘YYYYMMDD‘), to_date(‘19990421‘,‘YYYYMMDD‘), 2344.78, ‘Vancouver‘,‘Manager‘)
insert into Employee(ID, First_Name, Last_Name, Start_Date,End_Date,Salary, City,Description) values(‘05‘,‘Robert‘, ‘Black‘, to_date(‘19840115‘,‘YYYYMMDD‘), to_date(‘19980808‘,‘YYYYMMDD‘), 2334.78, ‘Vancouver‘,‘Tester‘);
insert into Employee(ID, First_Name, Last_Name, Start_Date,End_Date,Salary, City,Description) values(‘06‘,‘Linda‘, ‘Green‘, to_date(‘19870730‘,‘YYYYMMDD‘), to_date(‘19960104‘,‘YYYYMMDD‘), 4322.78,‘New York‘, ‘Tester‘);
insert into Employee(ID, First_Name, Last_Name, Start_Date,End_Date,Salary, City,Description) values(‘07‘,‘David‘, ‘Larry‘, to_date(‘19901231‘,‘YYYYMMDD‘), to_date(‘19980212‘,‘YYYYMMDD‘), 7897.78,‘New York‘, ‘Manager‘)
insert into Employee(ID, First_Name, Last_Name, Start_Date,End_Date,Salary, City,Description) values(‘08‘,‘James‘, ‘Cat‘, to_date(‘19960917‘,‘YYYYMMDD‘), to_date(‘20020415‘,‘YYYYMMDD‘), 1232.78,‘Vancouver‘, ‘Tester‘);
-- display data in the table
select * from Employee;
ID FIRST_NAME LAST_NAME START_DAT END_DATE SALARY CITY DESCRIPTION
---- ---------- ---------- --------- --------- ---------- ---------- ---------------
01 Jason Martin 25-JUL-96 25-JUL-06 1234.56 Toronto Programmer
02 Alison Mathews 21-MAR-76 21-FEB-86 6661.78 Vancouver Tester
03 James Smith 12-DEC-78 15-MAR-90 6544.78 Vancouver Tester
04 Celia Rice 24-OCT-82 21-APR-99 2344.78 Vancouver Manager
05 Robert Black 15-JAN-84 08-AUG-98 2334.78 Vancouver Tester
06 Linda Green 30-JUL-87 04-JAN-96 4322.78 New York Tester
07 David Larry 31-DEC-90 12-FEB-98 7897.78 New York Manager
08 James Cat 17-SEP-96 15-APR-02 1232.78 Vancouver Tester
-- Rollup: give the sum on the aggregate; it is used as an add-on to the GROUP BY clause.
SELECT count(*), city
FROM employee
GROUP BY ROLLUP(city);
COUNT(*) CITY
---------- ----------
2 New York
1 Toronto
5 Vancouver
8
With ROLLUP and ROW_NUMBER added
SELECT ROW_NUMBER() OVER(ORDER BY city, description) rn,
count(*), city, description
FROM employee
GROUP BY ROLLUP(city, description);
RN COUNT(*) CITY DESCRIPTION
----- ---------- ---------- ---------------
1 1 New York Manager
2 1 New York Tester
3 2 New York
4 1 Toronto Programmer
5 1 Toronto
6 1 Vancouver Manager
7 4 Vancouver Tester
8 5 Vancouver
9 8
The ROLLUP clause extends GROUP BY to return a row containing a subtotal for each group along with a total for all groups
--Passing a Single Column to ROLLUP
--The ROLLUP clause extends GROUP BY to return a row containing a subtotal for each group along with a total for all groups.
SELECT city, SUM(salary)
FROM employee
GROUP BY city;
CITY SUM(SALARY)
---------- -----------
New York 12220.56
Toronto 1234.56
Vancouver 19118.9
--The following query rewrites the previous example to use ROLLUP.
SELECT city, SUM(salary)
FROM employee
GROUP BY ROLLUP(city);
CITY SUM(SALARY)
---------- -----------
New York 12220.56
Toronto 1234.56
Vancouver 19118.9
32574.02
Changing the Position of Columns Passed to ROLLUP
SELECT city, description, SUM(salary)
FROM employee
GROUP BY ROLLUP(city, description);
CITY DESCRIPTION SUM(SALARY)
---------- --------------- -----------
Toronto Programmer 1234.56
Toronto 1234.56
New York Tester 4322.78
New York Manager 7897.78
New York 12220.56
Vancouver Tester 16774.12
Vancouver Manager 2344.78
Vancouver 19118.9
32574.02
SELECT city, description, SUM(salary)
FROM employee
GROUP BY ROLLUP(description, city);
CITY DESCRIPTION SUM(SALARY)
---------- --------------- -----------
New York Tester 4322.78
Vancouver Tester 16774.12
Tester 21096.9
New York Manager 7897.78
Vancouver Manager 2344.78
Manager 10242.56
Toronto Programmer 1234.56
Programmer 1234.56
32574.02
Passing Multiple Columns to ROLLUP: groups the rows into blocks with the same column values
SELECT city, description, SUM(salary)
FROM employee
GROUP BY ROLLUP(city, description);
CITY DESCRIPTION SUM(SALARY)
---------- --------------- -----------
Toronto Programmer 1234.56
Toronto 1234.56
New York Tester 4322.78
New York Manager 7897.78
New York 12220.56
Vancouver Tester 16774.12
Vancouver Manager 2344.78
Vancouver 19118.9
32574.02
Using AVG with ROLLUP
SELECT city, description, AVG(salary)
FROM employee
GROUP BY ROLLUP(city, description);
CITY DESCRIPTION AVG(SALARY)
---------- --------------- -----------
Toronto Programmer 1234.56
Toronto 1234.56
New York Tester 4322.78
New York Manager 7897.78
New York 6110.28
Vancouver Tester 4193.53
Vancouver Manager 2344.78
Vancouver 3823.78
4071.7525
Rollup function in group by clause
SELECT city, SUM(salary)
FROM employee
GROUP BY ROLLUP(city);
CITY SUM(SALARY)
---------- -----------
New York 12220.56
Toronto 1234.56
Vancouver 19118.9
32574.02
ROLLUP and RANK() to get the sales rankings by product type ID
CREATE TABLE all_sales (
year INTEGER,
month INTEGER,
prd_type_id INTEGER,
emp_id INTEGER ,
amount NUMBER(8, 2)
);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2006,1 ,1 ,21 ,16034.84);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2006,2 ,1 ,21 ,15644.65);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2006,3 ,2 ,21 ,20167.83);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2006,4 ,2 ,21 ,25056.45);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2006,5 ,2 ,21 ,NULL);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2006,6 ,1 ,21 ,15564.66);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2006,7 ,1 ,21 ,15644.65);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2006,8 ,1 ,21 ,16434.82);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2006,9 ,1 ,21 ,19654.57);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2006,10 ,1 ,21 ,21764.19);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2006,11 ,1 ,21 ,13026.73);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2006,12 ,2 ,21 ,10034.64);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2005,1 ,2 ,22 ,16634.84);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2005,1 ,2 ,21 ,26034.84);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2005,2 ,1 ,21 ,12644.65);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2005,3 ,1 ,21 ,NULL);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2005,4 ,1 ,21 ,25026.45);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2005,5 ,1 ,21 ,17212.66);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2005,6 ,1 ,21 ,15564.26);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2005,7 ,2 ,21 ,62654.82);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2005,8 ,2 ,21 ,26434.82);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2005,9 ,2 ,21 ,15644.65);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2005,10 ,2 ,21 ,21264.19);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2005,11 ,1 ,21 ,13026.73);
insert into all_sales (YEAR,MONTH,PRD_TYPE_ID,EMP_ID,AMOUNT) values(2005,12 ,1 ,21 ,10032.64);
select * from all_sales;
YEAR MONTH PRD_TYPE_ID EMP_ID AMOUNT
------ ---------- ----------- ---------- ----------
2006 1 1 21 16034.84
2006 2 1 21 15644.65
2006 3 2 21 20167.83
2006 4 2 21 25056.45
2006 5 2 21
2006 6 1 21 15564.66
2006 7 1 21 15644.65
2006 8 1 21 16434.82
2006 9 1 21 19654.57
2006 10 1 21 21764.19
2006 11 1 21 13026.73
2006 12 2 21 10034.64
2005 1 2 22 16634.84
2005 1 2 21 26034.84
2005 2 1 21 12644.65
2005 3 1 21
2005 4 1 21 25026.45
2005 5 1 21 17212.66
2005 6 1 21 15564.26
2005 7 2 21 62654.82
2005 8 2 21 26434.82
2005 9 2 21 15644.65
2005 10 2 21 21264.19
2005 11 1 21 13026.73
2005 12 1 21 10032.64
--ROLLUP and RANK() to get the sales rankings by product type ID
SELECT
prd_type_id, SUM(amount),
RANK() OVER (ORDER BY SUM(amount) DESC) AS rank
FROM all_sales
GROUP BY ROLLUP(prd_type_id)
ORDER BY prd_type_id;
PRD_TYPE_ID SUM(AMOUNT) RANK
----------- ----------- ----------
1 227276.5 2
2 223927.08 3
451203.58 1
CUBE
In addition to the subtotals generated by the ROLLUP extension, the CUBE extension will generate subtotals for all combinations of the dimensions specified.
If "n" is the number of columns listed in the CUBE, there will be 2n subtotal combinations.
Setting Test Table
DROP TABLE dimension_tab;
CREATE TABLE dimension_tab (
fact_1_id NUMBER NOT NULL,
fact_2_id NUMBER NOT NULL,
fact_3_id NUMBER NOT NULL,
fact_4_id NUMBER NOT NULL,
sales_value NUMBER(10,2) NOT NULL
);
INSERT INTO dimension_tab
SELECT TRUNC(DBMS_RANDOM.value(low => 1, high => 3)) AS fact_1_id,
TRUNC(DBMS_RANDOM.value(low => 1, high => 6)) AS fact_2_id,
TRUNC(DBMS_RANDOM.value(low => 1, high => 11)) AS fact_3_id,
TRUNC(DBMS_RANDOM.value(low => 1, high => 11)) AS fact_4_id,
ROUND(DBMS_RANDOM.value(low => 1, high => 100), 2) AS sales_value
FROM dual
CONNECT BY level <= 1000;
COMMIT;
SELECT fact_1_id, fact_2_id,
SUM(sales_value) AS sales_value
FROM dimension_tab
GROUP BY CUBE (fact_1_id, fact_2_id)
ORDER BY fact_1_id, fact_2_id;
FACT_1_ID FACT_2_ID SALES_VALUE
--------- ---------- -----------
1 1 5806.42
1 2 4724.82
1 3 4358.52
1 4 5049.58
1 5 4929.04
1 24868.38
2 1 5181.96
2 2 5008.37
2 3 4856.44
2 4 4342.02
2 5 4619.73
2 24008.52
1 10988.38
2 9733.19
3 9214.96
4 9391.6
5 9548.77
48876.9
As the number of dimensions increase, so do the combinations of subtotals that need to be calculated
SELECT fact_1_id, fact_2_id, fact_3_id,
SUM(sales_value) AS sales_value
FROM dimension_tab
GROUP BY CUBE (fact_1_id, fact_2_id, fact_3_id)
ORDER BY fact_1_id, fact_2_id, fact_3_id;
It is possible to do a partial cube to reduce the number of subtotals calculated.
SELECT fact_1_id, fact_2_id, fact_3_id,
SUM(sales_value) AS sales_value
FROM dimension_tab
GROUP BY fact_1_id, CUBE (fact_2_id, fact_3_id)
ORDER BY fact_1_id, fact_2_id, fact_3_id;
GROUPING Functions
It can be quite easy to visually identify subtotals generated by rollups and cubes, but to do it programatically you really need something more accurate than the presence of null values in the grouping columns. This is where the GROUPING function comes in. It accepts a single column as a parameter and returns "1" if the column contains a null value generated as part of a subtotal by a ROLLUP or CUBE operation or "0" for any other value, including stored null values.
The following query is a repeat of a previous cube, but the GROUPING function has been added for each of the dimensions in the cube.
SELECT fact_1_id, fact_2_id,
SUM(sales_value) AS sales_value,
GROUPING(fact_1_id) AS f1g,
GROUPING(fact_2_id) AS f2g
FROM dimension_tab
GROUP BY CUBE (fact_1_id, fact_2_id)
ORDER BY fact_1_id, fact_2_id;
FACT_1_ID FACT_2_ID SALES_VALUE F1G F2G
--------- ---------- ----------- ---------- ----------
1 1 5806.42 0 0
1 2 4724.82 0 0
1 3 4358.52 0 0
1 4 5049.58 0 0
1 5 4929.04 0 0
1 24868.38 0 1
2 1 5181.96 0 0
2 2 5008.37 0 0
2 3 4856.44 0 0
2 4 4342.02 0 0
2 5 4619.73 0 0
2 24008.52 0 1
1 10988.38 1 0
2 9733.19 1 0
3 9214.96 1 0
4 9391.6 1 0
5 9548.77 1 0
48876.9 1 1
From this we can see:
F1G=0,F2G=0 : Represents a row containing regular subtotal we would expect from a GROUP BY operation.
F1G=0,F2G=1 : Represents a row containing a subtotal for a distinct value of the FACT_1_ID column, as generated by ROLLUP and CUBE operations.
F1G=1,F2G=0 : Represents a row containing a subtotal for a distinct value of the FACT_2_ID column, which we would only see in a CUBE operation.
F1G=1,F2G=1 : Represents a row containing a grand total for the query, as generated by ROLLUP and CUBE operations.
It would now be easy to write a program to accurately process the data.
The GROUPING columns can used for ordering or filtering results.
SELECT fact_1_id, fact_2_id,
SUM(sales_value) AS sales_value,
GROUPING(fact_1_id) AS f1g,
GROUPING(fact_2_id) AS f2g
FROM dimension_tab
GROUP BY CUBE (fact_1_id, fact_2_id)
HAVING GROUPING(fact_1_id) = 1 OR GROUPING(fact_2_id) = 1
ORDER BY GROUPING(fact_1_id), GROUPING(fact_2_id);
FACT_1_ID FACT_2_ID SALES_VALUE F1G F2G
--------- ---------- ----------- ---------- ----------
1 24868.38 0 1
2 24008.52 0 1
5 9548.77 1 0
3 9214.96 1 0
2 9733.19 1 0
1 10988.38 1 0
4 9391.6 1 0
48876.9 1 1
GROUPING_ID
The GROUPING_ID function provides an alternate and more compact way to identify subtotal rows. Passing the dimension columns as arguments, it returns a number indicating the GROUP BY level.
SELECT fact_1_id, fact_2_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id) AS grouping_id
FROM dimension_tab
GROUP BY CUBE (fact_1_id, fact_2_id)
ORDER BY fact_1_id, fact_2_id;
FACT_1_ID FACT_2_ID SALES_VALUE GROUPING_ID
--------- ---------- ----------- -----------
1 1 5806.42 0
1 2 4724.82 0
1 3 4358.52 0
1 4 5049.58 0
1 5 4929.04 0
1 24868.38 1
2 1 5181.96 0
2 2 5008.37 0
2 3 4856.44 0
2 4 4342.02 0
2 5 4619.73 0
2 24008.52 1
1 10988.38 2
2 9733.19 2
3 9214.96 2
4 9391.6 2
5 9548.77 2
48876.9 3
GROUP_ID
It‘s possible to write queries that return the duplicate subtotals, which can be a little confusing. The GROUP_ID function assigns the value "0" to the first set, and all subsequent sets get assigned a higher number. The following query forces duplicates to show the GROUP_ID function in action.
SELECT fact_1_id, fact_2_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id) AS grouping_id,
GROUP_ID() AS group_id
FROM dimension_tab
GROUP BY GROUPING SETS(fact_1_id, CUBE (fact_1_id, fact_2_id))
ORDER BY fact_1_id, fact_2_id;
FACT_1_ID FACT_2_ID SALES_VALUE GROUPING_ID GROUP_ID
---------- ---------- ----------- ----------- ----------
1 1 5806.42 0 0
1 2 4724.82 0 0
1 3 4358.52 0 0
1 4 5049.58 0 0
1 5 4929.04 0 0
1 24868.38 1 0
1 24868.38 1 1
2 1 5181.96 0 0
2 2 5008.37 0 0
2 3 4856.44 0 0
2 4 4342.02 0 0
2 5 4619.73 0 0
2 24008.52 1 1
2 24008.52 1 0
1 10988.38 2 0
2 9733.19 2 0
3 9214.96 2 0
4 9391.6 2 0
5 9548.77 2 0
48876.9 3 0
If necessary, you could then filter the results using the group.
SELECT fact_1_id, fact_2_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id) AS grouping_id,
GROUP_ID() AS group_id
FROM dimension_tab
GROUP BY GROUPING SETS(fact_1_id, CUBE (fact_1_id, fact_2_id))
HAVING GROUP_ID() = 0
ORDER BY fact_1_id, fact_2_id;
FACT_1_ID FACT_2_ID SALES_VALUE GROUPING_ID GROUP_ID
--------- ---------- ----------- ----------- ----------
1 1 5806.42 0 0
1 2 4724.82 0 0
1 3 4358.52 0 0
1 4 5049.58 0 0
1 5 4929.04 0 0
1 24868.38 1 0
2 1 5181.96 0 0
2 2 5008.37 0 0
2 3 4856.44 0 0
2 4 4342.02 0 0
2 5 4619.73 0 0
2 24008.52 1 0
1 10988.38 2 0
2 9733.19 2 0
3 9214.96 2 0
4 9391.6 2 0
5 9548.77 2 0
48876.9 3 0
GROUPING SETS
Calculating all possible subtotals in a cube, especially those with many dimensions, can be quite an intensive process. If you don‘t need all the subtotals, this can represent a considerable amount of wasted effort. The following cube with three dimensions gives 8 levels of subtotals (GROUPING_ID: 0-7), shown here.
SELECT fact_1_id, fact_2_id, fact_3_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id, fact_3_id) AS grouping_id
FROM dimension_tab
GROUP BY CUBE(fact_1_id, fact_2_id, fact_3_id)
ORDER BY fact_1_id, fact_2_id, fact_3_id;
If we only need a few of these levels of subtotaling we can use the GROUPING SETS expression and specify exactly which ones we need, saving us having to calculate the whole cube. In the following query we are only interested in subtotals for the "FACT_1_ID, FACT_2_ID" and "FACT_1_ID, FACT_3_ID" groups.
SELECT fact_1_id, fact_2_id, fact_3_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id, fact_3_id) AS grouping_id
FROM dimension_tab
GROUP BY GROUPING SETS((fact_1_id, fact_2_id), (fact_1_id, fact_3_id))
ORDER BY fact_1_id, fact_2_id, fact_3_id;
FACT_1_ID FACT_2_ID FACT_3_ID SALES_VALUE GROUPING_ID
---------- ---------- ---------- ----------- -----------
1 1 5806.42 1
1 2 4724.82 1
1 3 4358.52 1
1 4 5049.58 1
1 5 4929.04 1
1 1 2328.63 2
1 2 2562.87 2
1 3 2576.24 2
1 4 2489.73 2
1 5 2645.77 2
1 6 2795.96 2
1 7 2763.93 2
1 8 2448.43 2
1 9 2237.71 2
1 10 2019.11 2
2 1 5181.96 1
2 2 5008.37 1
2 3 4856.44 1
2 4 4342.02 1
2 5 4619.73 1
2 1 2091.33 2
2 2 2299.23 2
2 3 2381.08 2
2 4 2884.19 2
2 5 2704.9 2
2 6 2364.08 2
2 7 2261.54 2
2 8 2582.8 2
2 9 2399.91 2
2 10 2039.46 2
Notice how we have gone from returning 198 rows with 8 subtotal levels in the cube, to just 30 rows with 2 subtotal levels
More Examples with EMP Table
@connect scott/tiger
Analytics running total
set linesize 100
set pagesize 80
select * from emp order by DEPTNO;
EMPNO ENAME JOB MGR HIREDATE SAL COMM DEPTNO
---------- ---------- --------- ---------- ------------------ ---------- ---------- ----------
7782 CLARK MANAGER 7839 09/JUN/81 00:00:00 2450 10
7839 KING PRESIDENT 17/NOV/81 00:00:00 5000 10
7934 MILLER CLERK 7782 23/JAN/82 00:00:00 1300 10
7566 JONES MANAGER 7839 02/APR/81 00:00:00 2975 20
7902 FORD ANALYST 7566 03/DEC/81 00:00:00 3000 20
7876 ADAMS CLERK 7788 23/MAY/87 00:00:00 1100 20
7369 SMITH CLERK 7902 17/DEC/80 00:00:00 800 20
7788 SCOTT ANALYST 7566 19/APR/87 00:00:00 3000 20
7521 WARD SALESMAN 7698 22/FEB/81 00:00:00 1250 500 30
7844 TURNER SALESMAN 7698 08/SEP/81 00:00:00 1500 0 30
7499 ALLEN SALESMAN 7698 20/FEB/81 00:00:00 1600 300 30
7900 JAMES CLERK 7698 03/DEC/81 00:00:00 950 30
7698 BLAKE MANAGER 7839 01/MAY/81 00:00:00 2850 30
7654 MARTIN SALESMAN 7698 28/SEP/81 00:00:00 1250 1400 30
Getting a SUM of SAL by DEPTNO
set echo on
break on deptno skip 1
Select deptno, ename, sal,
sum(sal) over (partition by deptno order by sal) running_total1,
sum(sal) over (partition by deptno order by sal, rowid) running_total2
from emp order by deptno, sal;
DEPTNO ENAME SAL RUNNING_TOTAL1 RUNNING_TOTAL2
---------- ---------- ---------- -------------- --------------
10 MILLER 1300 1300 1300
CLARK 2450 3750 3750
KING 5000 8750 8750
20 SMITH 800 800 800
ADAMS 1100 1900 1900
JONES 2975 4875 4875
SCOTT 3000 10875 7875
FORD 3000 10875 10875
30 JAMES 950 950 950
WARD 1250 3450 2200
MARTIN 1250 3450 3450
TURNER 1500 4950 4950
ALLEN 1600 6550 6550
BLAKE 2850 9400 9400
Analytics Percentages within a group
break on deptno skip 1
select deptno, ename, sal,
to_char( round( ratio_to_report(sal) over (partition by deptno) *100, 2 ), ‘990.00‘ )||‘%‘ rtr
from emp;
DEPTNO ENAME SAL RTR
---------- ---------- ---------- --------
10 CLARK 2450 28.00%
KING 5000 57.14%
MILLER 1300 14.86%
20 JONES 2975 27.36%
FORD 3000 27.59%
ADAMS 1100 10.11%
SMITH 800 7.36%
SCOTT 3000 27.59%
30 WARD 1250 13.30%
TURNER 1500 15.96%
ALLEN 1600 17.02%
JAMES 950 10.11%
BLAKE 2850 30.32%
MARTIN 1250 13.30%
Analytics Top-N queries
set echo on
break on deptno skip 1
select deptno, ename, sal, rn
from (Select deptno, ename, sal, row_number() over (partition by deptno order by sal desc) rn
from emp)
where rn <= 3;
DEPTNO ENAME SAL RN
---------- ---------- ---------- ----------
10 KING 5000 1
CLARK 2450 2
MILLER 1300 3
20 SCOTT 3000 1
FORD 3000 2
JONES 2975 3
30 BLAKE 2850 1
ALLEN 1600 2
TURNER 1500 3
select deptno, ename, sal, rank
from ( Select deptno, ename, sal, rank() over (partition by deptno order by sal desc) rank
from emp )
where rank <= 3;
DEPTNO ENAME SAL RANK
---------- ---------- ---------- ----------
10 KING 5000 1
CLARK 2450 2
MILLER 1300 3
20 SCOTT 3000 1
FORD 3000 1
JONES 2975 3
30 BLAKE 2850 1
ALLEN 1600 2
TURNER 1500 3
select deptno, ename, sal, dr
from ( Select deptno, ename, sal, dense_rank() over (partition by deptno order by sal desc) dr
from emp )
where dr <= 3;
DEPTNO ENAME SAL DR
---------- ---------- ---------- ----------
10 KING 5000 1
CLARK 2450 2
MILLER 1300 3
20 SCOTT 3000 1
FORD 3000 1
JONES 2975 2
ADAMS 1100 3
30 BLAKE 2850 1
ALLEN 1600 2
TURNER 1500 3
Analytics Moving Averages
column last_5 format a30
set echo on
Select ename, sal,
round( avg(sal) over (order by sal rows 5 preceding) ) avg_sal,
rtrim( lag(sal) over (order by sal) || ‘,‘ ||
lag(sal,2) over (order by sal) || ‘,‘ ||
lag(sal,3) over (order by sal) || ‘,‘ ||
lag(sal,4) over (order by sal) || ‘,‘ ||
lag(sal,5) over (order by sal),‘,‘) last_5
from emp
order by sal;
ENAME SAL AVG_SAL LAST_5
---------- ---------- ---------- ------------------------------
SMITH 800 800
JAMES 950 875 800
ADAMS 1100 950 950,800
WARD 1250 1025 1100,950,800
MARTIN 1250 1070 1250,1100,950,800
MILLER 1300 1108 1250,1250,1100,950,800
TURNER 1500 1225 1300,1250,1250,1100,950
ALLEN 1600 1333 1500,1300,1250,1250,1100
CLARK 2450 1558 1600,1500,1300,1250,1250
BLAKE 2850 1825 2450,1600,1500,1300,1250
JONES 2975 2113 2850,2450,1600,1500,1300
SCOTT 3000 2396 2975,2850,2450,1600,1500
FORD 3000 2646 3000,2975,2850,2450,1600
KING 5000 3213 3000,3000,2975,2850,2450
select round( (3000+3000+2975+2850+2450+5000)/6 ) from dual;
ROUND((3000+3000+2975+2850+2450+5000)/6)
----------------------------------------
3213
Analytics Ranking Queries
set echo on
break on deptno skip 1
Select deptno, ename, sal,
rank() over ( partition by deptno order by sal desc ) r,
dense_rank() over ( partition by deptno order by sal desc ) dr,
row_number() over ( partition by deptno order by sal desc ) rn
from emp
order by deptno, sal desc;
DEPTNO ENAME SAL R DR RN
---------- ---------- ---------- ---------- ---------- ----------
10 KING 5000 1 1 1
CLARK 2450 2 2 2
MILLER 1300 3 3 3
20 SCOTT 3000 1 1 1
FORD 3000 1 1 2
JONES 2975 3 2 3
ADAMS 1100 4 3 4
SMITH 800 5 4 5
30 BLAKE 2850 1 1 1
ALLEN 1600 2 2 2
TURNER 1500 3 3 3
MARTIN 1250 4 4 4
WARD 1250 4 4 5
JAMES 950 6 5 6
源自：http://pafumi.net/Analytic_Functions.html
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