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Updated on 2024-04-28 GMT+08:00
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Analytical Functions

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

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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

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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

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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

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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)

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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 
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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

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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 
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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

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

Output

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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

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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

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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

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

Output

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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

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

Output

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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  '******' */
Parent topic: Functions and Operators