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As needed, you can insert custom SQL statements as part of the data import process. These custom SQL statements allow you to pre-filter the rows and columns of relational source data within the database, where performance is faster. This query method can also be used for wider operations on relational sources from within Cloud Dataprep by TRIFACTA® INC..

Limitations

General

All queries are blindly executed. It is your responsibility to ensure that they are appropriate. Queries like DELETE and DROP can destroy data in the database. Please use caution.

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.

    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.

    NOTE: Standard SQL syntax is supported. Legacy SQL syntax is not supported.

  • 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. 


  • Each SQL query must be a single statement.

Single Statement

The following limitations apply to creating datasets from a single statement. 

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

    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:

    SELECT "id", "value" FROM "public"."my_table"
  3. You should use proper escaping in SQL.

Use

To use, please complete the following steps.

Steps:

  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: 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.

    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.



    Figure: Create Dataset with SQL dialog

     

    1. See Examples 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.

Create with Variables

If parameterization has been enabled, you can specify variables as part of your SQL statement. Suppose you had table names like the following:

publish_create_all_types_97912510
publish_create_all_types_97944183
publish_create_all_types_14202824

You can insert an inline variable as part of your custom SQL to capture all of these variations. 

Figure: Insert variables in your custom SQL

In the above, custom SQL has been added to match the first example table. When the value is highlighted and the icon is clicked, the highlighted value is specified as the default value. Provide a name for the variable, and click Save.

Through the Run Job page, you can specify overrides for the default value, so the same job definition can be used across all matching tables without much modification. For more information, see Run Job Page.

For more information on this feature, see Overview of Parameterization.

Create with timestamp parameter

You can insert a timestamp parameter into your custom SQL. These parameters are used to describe timestamp formats for matching timestamps relative to the start of the job at the time of execution. 


NOTE: A SQL timestamp parameter only describes the formatting of a timestamp value. It cannot be used to describe actual values. For example, you cannot insert fixed values for the month to parameterize your input using this method. Instead, parameterize the input using multiple input variables, as described in the previous section.

NOTE: Values for seconds in a SQL timestamp parameter are not supported. The finest supported granularity is at the minutes level.

NOTE: When the dataset is created, the current date is used for comparison, instead of the job execution date.

In the following example, the timestamp parameter has been specified as YYYY-MM-DD:

SELECT * FROM <YYYY-MM-DD> 

If the job executes on May 28th, 2019, then this parameter resolves as 2019-05-28 and gathers data from that table.

Figure: Insert timestamp parameter

Steps:

  1. Click the Clock icon in the custom SQL dialog.
  2. Timestamp format: You can specify the format of the timestamp using supported characters. 

    Tip: The list and definition of available tokens is available in the help popover.

  3. Timestamp value: Choose whether the timestamp parameter is to match the exact start time or a time relative to the start of the job.

    Tip: You can use relative timestamp parameters to collect data from the preceding week, for example. This relative timestamp allows you to execute weekly jobs for the preceding week's data.

  4. To indicate that the timestamps are from a timezone different from the system timezone, click Change.
  5. To save the specified timestamp parameter, click Save.

SQL Validation

You cannot create a SQL-based dataset if any of your SQL statements do not pass validation. Errors must be corrected in the SQL or in the underlying database.

  • All SELECT statements are planned, which includes syntactical validation. However, these statements are not executed. Validation should be a matter of a few seconds.

Examples

Here are some basic SQL examples to get started.

Basic Syntax

Your SQL statements must be valid for the syntax expected by the target relational system. In particular, object delimiters may vary between systems. 

NOTE: The proper syntax depends on your database system. Please consult the documentation for your product for details.


Tip: Although some relational systems do not require object delimiters around column names, it is recommended that you add them to all applicable objects.

Tip: Avoid using column type identifiers (e.g. int) and other SQL keywords as object names. Some systems may generate invalid SQL errors.

NOTE: In the following sections, Oracle syntax is used in the examples. Please modify the examples for your target system.

Oracle syntax

Object delimiter: double-quote

Example syntax:

Double quotes required around database and table names and not required around column names.

SELECT "column1","column2" FROM "databaseName"."tableName"

SQL Server syntax

Object delimiter: none

Example syntax:

SELECT "column1","column2" FROM "databaseName"."tableName"


PostgreSQL syntax

Object delimiter: double-quote

Example syntax:

Double quotes required around database, table names, and column names.

SELECT "column1","column2" FROM "databaseName"."tableName"



BigQuery syntax

Object delimiters:

  • Back-ticks around datasets (database/table combination)
  • No quotes around column/field references
  • Double-quotes can be used around mock data in a SELECT statement

Example syntax:

SELECT column1,column2 FROM `databaseName.tableName`

For more information, see https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax.


Column Aliasing

If your select statement results in multiple columns with same name, the query fails to validate or fails on execution, such as selecting all columns in a JOIN. In these cases, columns must be properly aliased.

NOTE: This error will be caught either during validating or during dataset import.

For example, in the following JOIN, the EMPLOYEE and DEPARTMENT tables have column names department_id and department_id

SELECT * FROM EMPLOYEE INNER JOIN DEPARTMENT ON (department_id = department_id)

The above query generates an error. Columns must be properly aliased, as in the following:

SELECT e.id, e.department_id, e.first_name, e.last_name, d.department_name FROM EMPLOYEE AS E INNER JOIN DEPARTMENT d ON (e.department_id = d.department_id)

Collect Whole Table

SELECT * FROM "DB1"."table2"

Filter Columns

SELECT lastName,firstName FROM "DB1"."table2

Filter Rows

SELECT lastName,firstName FROM "DB1"."table2" WHERE invoiceAmt > 10000



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