Help Center/ GaussDB(DWS)/ Tool Guide/ DSC: SQL Syntax Migration Tool/ Teradata Syntax Migration/ System Functions and Operators
Updated on 2023-04-03 GMT+08:00
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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);