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There are two supported ways to use Jupyter with SQL Server: run a local Python notebook that coordinates computation on a machine-learning-enabled SQL Server, or connect to SQL Server and invoke sp_execute_external_script, which runs Python or R in the server-managed external runtime. The first route is the documented remote-client workflow for Python; the stored-procedure route supports both Python and R.
Choose the execution model first
“Send execution to SQL Server” can describe two different arrangements. Your choice determines what must be installed, where code runs, and which permissions are required.
| Aspect | Local Jupyter with remote Python client | sp_execute_external_script |
|---|---|---|
| Code authoring | Local Jupyter notebook | Notebook cell or SQL client issuing T-SQL |
| Execution | Microsoft client libraries coordinate or push computation to the remote SQL Server | SQL Server Machine Learning Services manages the external Python or R runtime |
| Primary mechanism | Microsoft machine-learning client libraries, including revoscalepy where applicable |
T-SQL procedure with @language, @script and optional SQL input |
| Languages established by the documented path | Python | Python and R |
| Main prerequisites | Matching client libraries, reachable configured server and valid authentication | Machine Learning Services, enabled external scripts, Launchpad, authentication and database permissions |
Route 1: use Jupyter as a remote Python client
Microsoft documents a workstation setup in which Jupyter runs locally and Microsoft’s client libraries coordinate work with a remote SQL Server enabled for machine-learning integration. The guide covers SQL Server 2016, 2017, 2019 and SQL Server 2019 on Linux. Treat that as the guide’s stated scope, not a universal support matrix for every newer release or platform.
Prepare the server
- Install SQL Server Machine Learning Services and the Python component on the target instance.
- Confirm that the instance is reachable from the notebook workstation through the required network and SQL Server port.
- Verify that the server edition, operating system and client-library versions are a supported combination for your release.
Prepare the notebook workstation
- Install a compatible Python environment and Jupyter.
- Install Microsoft’s machine-learning client libraries required by the documented remote workflow, including
revoscalepywhen the selected operation uses it. - Configure the notebook to connect to the remote SQL Server using its instance name, database and authentication method.
- Keep credentials out of notebooks that will be shared. Prefer an approved credential store or integrated authentication rather than a plain-text password.
Understand what this route does not establish
The cited client setup documents Python specifically. It does not establish an equivalent remote-client procedure for sending R code from Jupyter. If R is the requirement, use the in-database procedure described below unless current Microsoft documentation for your exact release provides a separate R client workflow.
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Route 2: run Python or R inside SQL Server
With SQL Server Machine Learning Services installed and configured, a notebook can submit T-SQL that calls sp_execute_external_script. The code in @script runs in SQL Server’s external runtime, and @input_data_1 can supply rows from a SQL query.
Install and enable the server feature
- Install Machine Learning Services with Python, R, or both on the database instance, subject to the feature and platform support for your SQL Server release.
- On Windows, enable external scripts:
EXEC sp_configure 'external scripts enabled', 1;
RECONFIGURE;- Restart the SQL Server database engine. This also restarts the associated Launchpad service used to host external runtimes.
- Verify that the configuration is enabled and that Launchpad is running before testing a script.
The first external-runtime call can take longer than later calls while the runtime is loaded.
Grant the required permissions
- Use a valid SQL Server login or Windows integrated authentication. Microsoft generally recommends integrated authentication, although a SQL login can be simpler in some environments.
- For a non-administrator, grant
EXECUTE ANY EXTERNAL SCRIPTin every database where external scripts will run. - Grant ordinary data permissions only as needed. For example, add reader, writer or DDL permissions when the SQL query or script actually requires those operations.
Call Python from a notebook through T-SQL
A minimal call specifies the language and script. In a real notebook, submit this SQL through your SQL Server connection rather than executing it as local Python.
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EXEC sp_execute_external_script
@language = N'Python',
@script = N'
print("Python is running in SQL Server")
';Pass SQL data into Python
Use @input_data_1 for a relational query. The external runtime receives the resulting data frame through the procedure’s input convention.
EXEC sp_execute_external_script
@language = N'Python',
@script = N'
mean_value = InputDataSet["amount"].mean()
OutputDataSet = pandas.DataFrame({"mean_amount": [mean_value]})
',
@input_data_1 = N'
SELECT amount
FROM dbo.Sales
WHERE amount IS NOT NULL
';The exact Python data-frame variable conventions and available packages depend on the SQL Server Machine Learning Services version and its installed runtime. Verify those details for your instance before deploying a notebook unchanged.
Call R from a notebook through T-SQL
R uses the same stored procedure but changes @language and the script syntax.
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EXEC sp_execute_external_script
@language = N'R',
@script = N'
result <- data.frame(message = "R is running in SQL Server")
OutputDataSet <- result
';For data processing, provide a query through @input_data_1 and return a data frame through the R output convention supported by your installed version.
Control the returned result schema
Names assigned inside Python or R do not necessarily become the column headings that the SQL client displays. When consumers depend on stable names and SQL types, declare them with WITH RESULT SETS.
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@language = N'Python',
@script = N'
OutputDataSet = pandas.DataFrame({"score": [0.97]})
'
WITH RESULT SETS ((score decimal(5,4)));Declare every returned column with the SQL type expected by downstream queries, notebook code or applications.
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Connect Jupyter safely and reliably
Authentication and network checks
- Confirm the server name, instance name, port and database from the notebook environment.
- Test the same identity with a normal SQL query before testing external scripts.
- Use integrated authentication where organizational policy supports it; otherwise protect SQL credentials with a secret manager.
- Do not embed passwords, access tokens or connection strings containing secrets in a shared notebook.
Version and platform checks
Before copying a remote-client example, record the SQL Server release, operating system, Machine Learning Services components, Python or R runtime versions and client-library versions. The older remote-client guide does not prove that one package set works unchanged with all current SQL Server releases, Linux configurations or both languages.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Troubleshoot common failures
“External script execution is disabled”
Enable external scripts enabled, run RECONFIGURE, restart the database engine and verify the setting. Ensure the required Machine Learning Services language component was installed.
Launchpad or runtime startup errors
Check that the Launchpad service is running and that the first-call delay is not being mistaken for a query failure. Review SQL Server and Launchpad logs, then confirm that the installed runtime matches the instance configuration.
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Permission denied
Grant EXECUTE ANY EXTERNAL SCRIPT in the target database to non-administrative users, then separately grant the data permissions needed by the input query or any table writes.
Connection or login failures
Validate network reachability, instance and port, authentication mode and database name. A notebook can connect successfully yet still fail to run an external script if its identity lacks database permissions.
Unexpected or missing output columns
Use WITH RESULT SETS to define names and SQL types explicitly. Do not assume a variable name created inside Python or R will be used as the SQL result heading.
Which route should you use?
- Choose the remote Python client when you specifically need Microsoft’s documented local-Jupyter-to-remote-Python workflow and your server/client combination matches its supported scope.
- Choose
sp_execute_external_scriptwhen the requirement includes R, when you want SQL Server to manage the external runtime, or when computation should execute in the database environment alongside the data.
Microsoft describes in-database execution as running scripts without moving data outside SQL Server or over the network. That benefit applies to the Machine Learning Services procedure path; it should not be generalized to every local-client workflow.
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