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Info

NOTE: Column names in custom SQL statements are case-sensitive. Case mismatches between SQL statement and your datasource can cause jobs to fail.

 


  • SQL statements are stored as part of the query instance for the object. If the same query is being made across multiple users using private connections, the SQL must be shared and entered by individual users.

    Info

    NOTE: If a dataset created from custom SQL is shared, collaborators are not permitted to edit the custom SQL.


  • SQL statements must be valid for the syntax of the target relational system. Syntax examples are provided below.

  • If you modify the custom SQL statement when reading from a source, all samples generated based on the previous SQL are invalidated.
  • Declared variables are not supported

  • For each SQL statement, all columns must have an explicit name. Example:
    • Function references such as: 

      Code Block
      UPPER(col)


    • Must be specified as:

      Code Block
      UPPER(col) as col_name



  • When using custom SQL to read from a Hive view, the results of a nested function are saved to a temporary name, unless explicitly aliased. 

      • If aliases are not used, the temporary column names can cause jobs to fail, on Spark in particular.
      • For more information, see Using Hive.

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  1. Selecting columns with the same name, even with "*", is not supported and generates an ambiguous column name error. 

    Tip

    Tip: You should use fully qualified column names or proper aliasing. See Column Aliasing below.


  2. Users are encouraged to provide fully qualified path to table being used. Example:

    Code Block
    SELECT "id", "value" FROM "public"."my_table"


  3. You should use proper escaping in SQL.

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  1. In the Library page, click Import Data.
  2. In the Import Data page, select a connection. 
  3. Within your source, locate the table from which you wish to import. Do not select the table.
  4. Click the Preview icon to review the columns in the dataset.

    Tip

    Tip: You may wish to copy the database, table name, and column names to a text editor to facilitate generating your SQL statement.


  5. Click Create Dataset with SQL. Enter or paste your SQL statement.

    Warning

    Through the custom SQL interface, it is possible to enter SQL statements that can delete data, change table schemas, or otherwise corrupt the targeted database. Please use this feature with caution.


    D caption
    Create Dataset with SQL dialog
     


    1. See Examples165278276 below.

    2. To test the SQL, click Validate SQL. For details, see below.

    3. To apply the SQL to the import process, click Create Dataset.

  6. The customized source is added to the right panel. To re-edit, click Custom SQL.

  7. Complete the other steps to define your imported dataset. 

  8. When the data is imported, it is altered or filtered based on your SQL statement. 

    1. After dataset creation, you can modify the SQL, if needed. See Dataset Details Page.

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