Updated on 2023-04-03 GMT+08:00

System Functions and Operators

This section contains the migration syntax for migrating Teradata system functions and operators. The migration syntax determines how the keywords and features are migrated.

Schema

The database with the schema name should be changed to SET SESSION CURRENT_SCHEMA.

Oracle Syntax

Syntax After Migration

DATABASE SCHTERA 
SET SESSION CURRENT_SCHEMA TO SCHTERA

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.

  1. 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;
    
  2. 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
    ;
  3. Window Functions

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

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

  4. 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;
    
  5. 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;
    
  6. 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 ...;
    
  7. 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
    ;
    
  8. COMPRESS specified with *****

    Input

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

    Output

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

Comparison and List Operators

The comparison operators LT, LE, GT, GE, EQ, and NE must not be used as TABLE alias or COLUMN alias.

The following comparison and list operators are supported:

  1. ^= and GT
    Input: Comparison operations (^= and GT)
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    SELECT t1.c1, t2.c2 
      FROM tab1 t1, tab2 t2 
     WHERE t1.c3 ^= t1.c3
       AND t2.c4 GT 100;
    

    Output

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    SELECT t1.c1, t2.c2 
      FROM tab1 t1, tab2 t2 
     WHERE t1.c3 <> t1.c3
       AND t2.c4 > 100;
    
  2. EQ and NE
    Input: Comparison operations (EQ and NE)
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    SELECT t1.c1, t2.c2 
      FROM tab1 t1 INNER JOIN tab2 t2 
        ON t1.c2 EQ t2.c2
     WHERE t1.c6 NE 1000;
    

    Output

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     SELECT t1.c1, t2.c2 
      FROM tab1 t1 INNER JOIN tab2 t2 
        ON t1.c2 = t2.c2
     WHERE
            t1.c6 <> 1000;
    
  3. LE and GE
    Input: Comparison operations (LE and GE)
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    SELECT t1.c1, t2.c2 
      FROM tab1 t1, tab2 t2 
     WHERE t1.c3 LE 200
       AND t2.c4 GE 100;
    

    Output

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     SELECT t1.c1, t2.c2 
       FROM tab1 t1, tab2 t2 
      WHERE t1.c3 <= 200
        AND t2.c4 >= 100;
    
  4. NOT= and LT
    Input: Comparison operations (NOT= and LT)
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    SELECT t1.c1, t2.c2 
      FROM tab1 t1, tab2 t2 
     WHERE t1.c3 NOT= t1.c3
       AND t2.c4 LT 100;
    

    Output

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    SELECT t1.c1, t2.c2 
      FROM tab1 t1, tab2 t2 
     WHERE t1.c3 <> t1.c3
       AND t2.c4 < 100;
    
  5. IN and NOT IN

    For details, see IN and NOT IN Conversion.

    Input: IN and NOT IN
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     SELECT c1, c2
       FROM tab1
      WHERE c1 IN 'XY';
    

    Output

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    SELECT c1, c2
      FROM tab1
     WHERE c1 = 'XY';
    

    GaussDB(DWS) does not support IN and NOT IN operators in some specific scenarios.

  6. IS NOT IN
    Input: IS NOT IN
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    SELECT c1, c2
      FROM tab1
     WHERE c1 IS NOT IN (subquery);
    

    Output

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    SELECT c1, c2
      FROM tab1
     WHERE c1 NOT IN (subquery);
    
  7. LIKE ALL / NOT LIKE ALL
    Input: LIKE ALL / NOT LIKE ALL
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    SELECT c1, c2
      FROM tab1
     WHERE c3 NOT LIKE ALL ('%STR1%', '%STR2%', '%STR3%');
    

    Output

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    SELECT c1, c2
      FROM tab1
     WHERE c3 NOT LIKE ALL (ARRAY[ '%STR1%', '%STR2%', '%STR3%' ]);
    
  8. LIKE ANY / NOT LIKE ANY
    Input: LIKE ANY / NOT LIKE ANY
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    SELECT c1, c2
      FROM tab1
     WHERE c3 LIKE ANY ('STR1%', 'STR2%', 'STR3%');
    

    Output

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    SELECT c1, c2
      FROM tab1
     WHERE c3 LIKE ANY (ARRAY[ 'STR1%', 'STR2%', 'STR3%' ]);
    

Table Operators

The functions that can be called in the FROM clause of a query are from the table operator.

Input: Table operator with RETURNS
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SELECT * 
  FROM TABLE( sales_retrieve (9005) RETURNS ( store INTEGER, item CLOB, quantity BYTEINT) ) AS ret;

Output

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SELECT *
  FROM sales_retrieve(9005) AS ret (store, item, quantity);