Archive for August, 2018

SQL Server on Linux – Best practices post installation

August 30, 2018 1 comment


Setup SQL Server on Linux:

1. Spinning up a new Linux VM on Microsoft Azure

2. Install and Configure SQL Server 2017 on Linux Azure VM

3. Connect SQL Server on Linux with SSMS from a Windows machine

Best Practices:

Here are some of the best practices post installing SQL Server on Linux that can help you maximize database performance:

1. To maintain efficient Linux and SQL Scheduling behavior, it’s recommended to use the ALTER SERVER CONFIGURATION command to set PROCESS AFFINITY for all the NUMANODEs and/or CPUs. [Setting Process Affinity]

2. To reduce the risk of tempdb concurrency slowdowns in high performance environments, configure multiple tempdb files by adding additional tempdb files by using the ADD FILE command. [tempdb Contention]

3. Use mssql-conf to configure the memory limit and ensure there’s enough free physical memory for the Linux operating system.

sudo /opt/mssql/bin/mssql-conf set memory.memorylimitmb 1024
sudo systemctl restart mssql-server

4. On multi-node Non-Uniform Memory Access (NUMA) installations, auto NUMA balancing needs to be disabled to allow SQL Server to operate at maximum efficiency on a NUMA system.

sysctl -w kernel.numa_balancing=0

5. You can also change the kernel settings value for virtual address space to 256K, as the default value of 65K may be insufficient for a SQL Server installation.

sysctl -w vm.max_map_count=262144

6. Use the noatime attribute to disable last accessed timestamps with any file system that is used to store SQL Server data and log files.

7. For the most consistent performance experience, you must leave the Transparent Huge Pages (THP) option enabled.

8. Virtual machine (VM) features like Hyper-V Dynamic Memory shouldn’t be used with SQL Server installations. When using VMs, be sure to assign sufficient fixed-memory sizes.

9. Make sure you have a properly configured swapfile to avoid any out of memory issues.


SQL Python Error – ‘sp_execute_external_script’ is disabled on this instance of SQL Server. Use sp_configure ‘external scripts enabled’ to enable it.

August 29, 2018 Leave a comment

You are running a Python script by using sp_execute_external_script SP but its throwing this error:

Msg 39023, Level 16, State 1, Procedure sp_execute_external_script, Line 1 [Batch Start Line 27]
‘sp_execute_external_script’ is disabled on this instance of SQL Server. Use sp_configure ‘external scripts enabled’ to enable it.


You can refer to my blog post on setting up ML with Python with SQL Server, link:

This fix will also work with R support with SQL Server.


Get started with Python on SQL Server – Run Python with T-SQL on SSMS

August 10, 2018 1 comment

With SQL Server 2016 Microsoft added Machine Learning support with R Language in SQL engine itself and called it SQL Server R Services.

Going ahead with the new SQL Server 2017 version Microsoft added Python too as part of Machine Learning with existing R Language, and thus renamed it to SQL Server Machine Learning Services.


Here are few steps to get you started with Python programming in SQL Server, so that you can run Python scripts with T-SQL scripts within SSMS:

1. Feature Selection: While installing SQL Server 2017 make sure you’ve selected below highlighted services


2. Configure “external scripts enabled”: Post installation run below SQL statements to enable this option

sp_configure 'external scripts enabled'
sp_configure 'external scripts enabled', 1;
sp_configure 'external scripts enabled'


3. Restart SQL Server service: by “services.msc” program from command prompt, and run below SQL statement, this should show run _value = 1

sp_configure 'external scripts enabled'


If still you don’t see run _value = 1, then try restarting the Launchpad service mentioned below in Step #4.

4. Launchpad Service: Make sure this service is running, in “services.msc” program from command prompt. Restart the service, and it should be in Running state.

Its a service to launch Advanced Analytics Extensions Launchpad process that enables integration with Microsoft R Open using standard T-SQL statements. Disabling this service will make Advanced Analytics features of SQL Server unavailable.

Post restarting this service, it should return run _value = 1 on running the query mentioned at Step #3

Run Python from SSMS

So as you’ve installed SQL Server with ML services with Python & R, and enabled the components, now you can try running simple “Hello World!” program to test it:

EXEC sp_execute_external_script 
	@language = N'Python', 
	@script = N'print(''Hello Python !!! from T-SQL'')'


Let’s do simple math here:

EXEC sp_execute_external_script 
@language = N'Python', 
@script = N'
x = 5
y = 6
a = x * y
b = x + y


If you still face issues you can go through addition configuration steps mentioned in [MSDN Docs link].


Azure Databricks learning resources (documentation and videos)

August 7, 2018 1 comment


Databricks Introduction

What is Azure Databricks [Video]

Create Databricks workspace with Apache Spark cluster

Extract, Transform & Load (ETL) with Databricks

– Documentation:
   – Azure
   – Databricks

From Channel 9

1. Data Science using Azure Databricks and Apache Spark [Video]

2. Data ingestion, stream processing and sentiment analysis using Twitter [Video]

3. ETL with Azure Databricks using ADF [Video]

4. ADF new features & integration with Azure Databricks [Video]

5. Azure Databricks introduces R Studio Integration [Video]

6. Run Jars and Python scripts on Azure Databricks using ADF [Video]

From Microsoft Build Conf