AI News, How to Execute R and Python in SQL Server with Machine Learning Services

How to Execute R and Python in SQL Server with Machine Learning Services

By Kyle Weller, Microsoft Azure Machine Learning Did you know that you can write R and Python code within your T-SQL statements? Machine Learning Services in SQL Server eliminates the need for data movement.

Instead of transferring large and sensitive data over the network or losing accuracy with sample csv files, you can have your R/Python code execute within your database.

If you are excited to try out SQL Server Machine Learning Services, check out the hands on tutorial below. If you do not have Machine Learning Services installed in SQL Server,you will first want to follow the getting started tutorial I published here: In this tutorial, I will cover the basics of how to Execute R and Python in T-SQL statements.

Open a new query and paste this basic example: (While I use Python in these samples, you can do everything with R as well) EXEC sp_execute_external_script @language = N'Python',@script = N'print(3+4)' Sp_execute_external_script is a special system stored procedure that enables R and Python execution in SQL Server.

If you need to convert scalar values into a dataframe here is an example: EXEC sp_execute_external_script  @language =N'Python',@script=N'import pandas as pdc = 1/2d = 1*2s = pd.Series([c,d])df = pd.DataFrame(s)OutputDataSet = df' Variables c and d are both scalar values, which you can add to a pandas Series if you like, and then convert them to a pandas dataframe.

This one shows a little bit more complicated example, go read up on the python pandas package documentation for more details and examples: EXEC sp_execute_external_script  @language =N'Python',@script=N'import pandas as pds = {'col1': [1, 2], 'col2': [3, 4]}df = pd.DataFrame(s)OutputDataSet = df' You now know the basics to execute Python in T-SQL!

How to Execute R/Python in SQL Server with Machine Learning Services

SQL Machine Learning Services enables you to work with any open source packages and additionally provides algorithms which enable multi-threaded, parallel processing for industry leading scale and performance.If you don't have Machine Learning Services in SQL Server installed or enabled, check out these other related how-to videos:How to Install Machine Learning Services in SQL Server: https://aka.ms/InstallMLServices How to Enable R/Python Code Execution in SQL Server: https://aka.ms/EnableMLServicesVisit our documentation to learn more advanced techniques like how to remotely execute R/Python in SQL Server from your own IDE like Jupyter Notebooks, PyCharm, RStudio, etc.Advanced Execution Documentation: https://aka.ms/ExecuteSQLMLDocsHere is code from the video for quick copy/paste:Basic:EXEC sp_execute_external_script @language = N'Python', @script = N'print(3+4)'Input parameter:EXEC sp_execute_external_script @language =N'Python',@script=N'OutputDataSet = InputDataSet;',@input_data_1 =N'SELECT 1 AS Col1';Input/Output name:EXEC sp_execute_external_script @language =N'Python',@script=N'MyOutput = MyInput;',@input_data_1_name = N'MyInput',@input_data_1 =N'SELECT 1 AS foo',@output_data_1_name =N'MyOutput';Result Column Name:EXEC sp_execute_external_script @language =N'Python',@script=N'MyOutput = MyInput;',@input_data_1_name = N'MyInput',@input_data_1 =N'SELECT 1 AS foo, 2 AS bar',@output_data_1_name =N'MyOutput'WITH RESULT SETS ((MyColName int, MyColName2 int));Pandas Dataframe conversion:EXEC sp_execute_external_script @language =N'Python',@script=N'import pandas as pdc = 1/2d = 1*2s = pd.Series([c,d])df = pd.DataFrame(s)OutputDataSet = df'Pandas Dataframe conversion 2:EXEC sp_execute_external_script @language =N'Python',@script=N'import pandas as pds = {'col1': [1, 2], 'col2': [3, 4]}df = pd.DataFrame(s)OutputDataSet = df'

Install SQL Server machine learning R and Python on computers with no internet access

By default, installers connect to Microsoft download sites to get required and updated components for machine learning on SQL Server.

If firewall constraints prevent the installer from reaching these sites, you can use an internet-connected device to download files, transfer files to an offline server, and then run setup.

In-database analytics consist of database engine instance, plus additional components for R and Python integration, depending on the version of SQL Server.

When you run SQL Server Setup on a computer disconnected from the internet, Setup adds an Offline installation page to the wizard so that you can specify the location of the CAB files you copied in the previous step.

After installation is finished, restart the service and then configure the server to enable script execution: An initial offline installation of either SQL Server 2017 Machine Learning Services or SQL Server 2016 R Services requires the same configuration as an online installation:

Slipstream setup refers to the ability to apply a patch or update to a failed instance installation, to repair existing problems.

When a server does not have Internet access, service updates are applied by downloading an updated SQL Server installer and corresponding versions of language-specific CAB files.

How to Execute Python/R in SQL

Instead of transferring large and sensitive data over the network or losing accuracy with sample csv files, you can have your R/Python code execute within your database.

After completing the tutorial, you will know: If you do not have Machine Learning Services installed on SQL Server, you will first want to read this getting started tutorial or watch these videos to get set up: https://aka.ms/InstallMLServices https://aka.ms/EnableMLServices If you like learning through videos, this tutorial is also available on YouTube: @https://aka.ms/ExecuteMLServices

Open a new query and paste this basic example: (While Python is used in these samples, you can do everything with R as well) Sp_execute_external_script is a special system stored procedure that enables R and Python execution in SQL Server.

After discussing a basic example, let's start adding more pieces: Machine Learning Services provides more natural communications between SQL and R/Python with an input data parameter that accepts any SQL query.

The default variable names are OutputDataSet and InputDataSet You can change these default names like this example: As you executed these examples, you might have noticed that they each return a result with (No column name)?

If you need to convert scalar values into a DataFrame here is an example: Variables c and d are both scalar values, which you can add to a pandas Series if you like, and then convert them to a pandas DataFrame.

This one shows a little bit more complicated example, go read up on the python pandas package documentation for more details and examples: You now know the basics of how to execute R and Python in T-SQL!

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