Analytical functions are collectively called ordered analytical functions in Teradata, and they provide powerful analytical abilities for data mining, analysis and business intelligence.

Analytical Functions in ORDER BY

Input: Analytic function in ORDER BY clause

SELECT customer_id, customer_name, RANK(customer_id, customer_address DESC) 
   FROM customer_t
 WHERE  customer_state = 'CA'
 ORDER BY RANK(customer_id, customer_address DESC);

Output

SELECT customer_id, customer_name, RANK() over(order by customer_id, customer_address DESC) 
   FROM customer_t
 WHERE  customer_state = 'CA'
 ORDER BY RANK() over(order by customer_id DESC, customer_address DESC) ;

Input: Analytic function in GROUP BY clause

SELECT customer_city, customer_state, postal_code
     , rank(postal_code)
     , rank() over(partition by customer_state order by postal_code)
    , rank() over(order by postal_code)
  FROM Customer_T 
  GROUP BY customer_state
  ORDER BY customer_state;

Output

SELECT customer_city, customer_state, postal_code
     , rank() over(PARTITION BY customer_state ORDER BY postal_code DESC) 
     , rank() over(partition by customer_state order by postal_code)
     , rank() over(order by postal_code)
  FROM Customer_T 
  ORDER BY customer_state;

Analytical Functions in PARTITION BY

When the input script contains a numeric value in the PARTITION BY clause, the migrated script retains the numeric value as it is.

Input: Analytic function in PARTITION BY clause (with numeric value)

SELECT
          Customer_id
          ,customer_name
          ,rank (
          ) over( partition BY 1 ORDER BY Customer_id )
          ,rank (customer_name)
     FROM
          Customer_t
     GROUP BY
          1
;

Output

SELECT
          Customer_id
          ,customer_name
          ,rank (
          ) over( partition BY 1 ORDER BY Customer_id )
          ,rank (
          ) over( PARTITION BY Customer_id ORDER BY customer_name DESC )
     FROM
          Customer_t
;

Window Functions

Window functions perform calculations across rows of the query result. DSC supports the following Teradata window functions:

The DSC supports only single occurrence of window function in QUALIFY clause. Multiple window functions in a QUALIFY may result in invalid migration.

CSUM

The Cumulative Sum (CSUM) function provides a running or cumulative total for a column's numeric value. It is recommended that ALIAS be used in the QUALIFY statements.

Input - CSUM with GROUP_ID

INSERT INTO GSIS_SUM.DW_DAT71 (
   col1
  ,PROD_GROUP
)
   SELECT 
      CSUM(1, T1.col1)
      ,T1.PROD_GROUP
     FROM tab1  T1
    WHERE T1.col1 = 'ABC'
;

Output

INSERT
     INTO
          GSIS_SUM.DW_DAT71 (
               col1
               ,PROD_GROUP
          ) SELECT
                    SUM (1) over( ORDER BY T1.col1 ROWS UNBOUNDED PRECEDING )
                    ,T1.PROD_GROUP
               FROM
                    tab1 T1
               WHERE
                    T1.col1 = 'ABC'
;

Input - CSUM with GROUP_ID

SELECT  top 10
      CSUM(1, T1.Test_GROUP)
      ,T1.col1
  FROM  $[schema}.  T1
 WHERE T1.Test_GROUP = 'Test_group' group by Test_group order by Test_Group;

Output

SELECT
       SUM (1) over( partition BY Test_group ORDER BY T1.Test_GROUP ROWS UNBOUNDED PRECEDING )
       ,T1.col1
  FROM
       $[schema}. T1
 WHERE
       T1.Test_GROUP = 'Test_group'
 ORDER BY
       Test_Group LIMIT 10
;

Input - CSUM with GROUP BY + QUALIFY

SELECT c1, c2, c3, CSUM(c4, c3) 
  FROM tab1 
QUALIFY ROW_NUMBER(c4) = 1  
GROUP BY 1, 2;

Output

SELECT c1, c2, c3, ColumnAlias1
  FROM ( SELECT c1, c2, c3
              , SUM (c4) OVER(PARTITION BY 1 ,2 ORDER BY c3 ROWS UNBOUNDED PRECEDING) AS ColumnAlias1
              , ROW_NUMBER( ) OVER(PARTITION BY 1, 2 ORDER BY c4) AS ROW_NUM1
           FROM tab1
       ) Q1
   WHERE Q1.ROW_NUM1 = 1;

MDIFF

The MDIFF function calculates the moving difference for a column based on the preset query width. The query width is the specified number of rows. It is recommended that ALIAS be used in the QUALIFY statements.

Input: MDIFF with QUALIFY

SELECT DT_A.Acct_ID, DT_A.Trade_Date, DT_A.Stat_PBU_ID
      , CAST( MDIFF( Stat_PBU_ID_3, 1, DT_A.Trade_No ASC ) AS DECIMAL(20,0) ) AS MDIFF_Stat_PBU_ID
   FROM Trade_His DT_A
  WHERE Trade_Date >= CAST( '20170101' AS DATE FORMAT 'YYYYMMDD' ) 
  GROUP BY DT_A.Acct_ID, DT_A.Trade_Date
 QUALIFY MDIFF_Stat_PBU_ID <> 0 OR MDIFF_Stat_PBU_ID IS NULL;

Output

SELECT Acct_ID, Trade_Date, Stat_PBU_ID, MDIFF_Stat_PBU_ID
   FROM (SELECT DT_A.Acct_ID, DT_A.Trade_Date, DT_A.Stat_PBU_ID
         , CAST( (Stat_PBU_ID_3 - (LAG(Stat_PBU_ID_3, 1, NULL) OVER (PARTITION BY DT_A.Acct_ID, DT_A.Trade_Date ORDER BY DT_A.Trade_No ASC)))  AS MDIFF_Stat_PBU_ID
           FROM Trade_His DT_A
          WHERE Trade_Date >= CAST( '20170101' AS DATE)
                )
 WHERE MDIFF_Stat_PBU_ID <> 0 OR MDIFF_Stat_PBU_ID IS NULL;

RANK

RANK(col1, col2...)

Input: RANK with GROUP BY

SELECT  c1, c2, c3, RANK(c4, c1 DESC, c3) AS Rank1 
  FROM  tab1 
 WHERE  ... 
 GROUP BY c1;

Output

SELECT c1, c2, c3, RANK() OVER (PARTITION BY c1 ORDER BY c4, c1 DESC ,c3) AS Rank1
  FROM tab1
 WHERE ...;

ROW_NUMBER

ROW_NUMBER(col1, col2...)

Input: ROW NUMBER with GROUP BY + QUALIFY

SELECT c1, c2, c3, ROW_NUMBER(c4, c3) 
   FROM tab1 
QUALIFY RANK(c4) = 1  
  GROUP BY 1, 2;

Output

SELECT
      c1
     ,c2
     ,c3
     ,ColumnAlias1
  FROM
      (
        SELECT
                c1
               ,c2
               ,c3
               ,ROW_NUMBER( ) over( PARTITION BY 1 ,2 ORDER BY c4 ,c3 ) AS ColumnAlias1
               ,RANK (
               ) over( PARTITION BY 1 ,2 ORDER BY c4 ) AS ROW_NUM1
          FROM
              tab1
      ) Q1
 WHERE
      Q1.ROW_NUM1 = 1
;

COMPRESS (specified with *****)

Input

ORDCADBRN VARCHAR(6) CHARACTER SET LATIN CASESPECIFIC TITLE '    ' COMPRESS '******'

Output

ORDCADBRN VARCHAR( 6 ) /* CHARACTER SET LATIN*/ /* CASESPECIFIC*/ /*TITLE '    '*/ /* COMPRESS  '******' */