Apply SQL Transformation
This article describes a module of Azure Machine Learning designer.
Using the Apply SQL Transformation module, you can:
Create tables for results and save the datasets in a portable database.
Perform custom transformations on data types, or create aggregates.
Execute SQL query statements to filter or alter data and return the query results as a data table.
The SQL engine used in this module is SQLite. For more information about SQLite syntax, see SQL as Understood by SQLite.
This module will bump data to SQLite, which is in the memory DB, hence the module execution requires much more memory and may hit an
Out of memory error. Make sure your computer has enough RAM.
How to configure Apply SQL Transformation
The module can take up to three datasets as inputs. When you reference the datasets connected to each input port, you must use the names
t3. The table number indicates the index of the input port.
Following is sample code to show how to join two tables. t1 and t2 are two datasets connected to the left and middle input ports of Apply SQL Transformation:
SELECT t1.* , t3.Average_Rating FROM t1 join (SELECT placeID , AVG(rating) AS Average_Rating FROM t2 GROUP BY placeID ) as t3 on t1.placeID = t3.placeID
The remaining parameter is a SQL query, which uses the SQLite syntax. When typing multiple lines in the SQL Script text box, use a semi-colon to terminate each statement. Otherwise, line breaks are converted to spaces.
This module supports all standard statements of the SQLite syntax. For a list of unsupported statements, see the Technical Notes section.
This section contains implementation details, tips, and answers to frequently asked questions.
An input is always required on port 1.
For column identifiers that contain a space or other special characters, always enclose the column identifier in square brackets or double quotation marks when referring to the column in the
Although SQLite supports much of the ANSI SQL standard, it does not include many features supported by commercial relational database systems. For more information, see SQL as Understood by SQLite. Also, be aware of the following restrictions when creating SQL statements:
SQLite uses dynamic typing for values, rather than assigning a type to a column as in most relational database systems. It is weakly typed, and allows implicit type conversion.
LEFT OUTER JOINis implemented, but not
RIGHT OUTER JOINor
FULL OUTER JOIN.
You can use
ADD COLUMNstatements with the
ALTER TABLEcommand, but other clauses are not supported, including
ALTER COLUMN, and
You can create a VIEW within SQLite, but thereafter views are read-only. You cannot execute a
UPDATEstatement on a view. However, you can create a trigger that fires on an attempt to
UPDATEon a view and perform other operations in the body of the trigger.
In addition to the list of non-supported functions provided on the official SQLite site, the following wiki provides a list of other unsupported features: SQLite - Unsupported SQL
See the set of modules available to Azure Machine Learning.