Merge pull request #4 from microsoft/master

sync with msft samples repo

Author: alexprotsenko

samples/features/intelligent-query-processing/notebooks/Batch_Mode_on_Rowstore.ipynb

@@ -0,0 +1,3 @@
+# Security Notebooks
+1. **TDE_on_Standard.ipynb** - This notebook demonstrates the ability to enable TDE on SQL Server 2019 Standard Edition along with Encryption Scan SUSPEND and RESUME.
+2. **TDE_on_Standard_EKM.ipynb** - This notebook demonstrates the ability to enable TDE on a SQL Server 2019 Standard Edition using EKM and Azure Key Vault.

…ery-processing/Scalar_UDF_Inlining.ipynb …sing/notebooks/Scalar_UDF_Inlining.ipynbsamples/features/sql2019notebooks/intelligent-query-processing/Scalar_UDF_Inlining.ipynb renamed to samples/features/intelligent-query-processing/notebooks/Scalar_UDF_Inlining.ipynb samples/features/sql2019notebooks/intelligent-query-processing/Scalar_UDF_Inlining.ipynb renamed to samples/features/intelligent-query-processing/notebooks/Scalar_UDF_Inlining.ipynb

@@ -24,5 +24,8 @@ In this example, we will deploy Kubernetes over multiple Linux machines (physica
 1. Execute [setup-k8s-master.sh](setup-k8s-master.sh/) script on the machine designated as Kubernetes master (_not_ under sudo su as otherwise you'll setup K8S .kube/config permissions for root)
 1. After successful initialization of the Kubernetes master, follow the kubeadm join commands output by the setup script on each agent machine
 1. Execute [setup-volumes-agent.sh](setup-volumes-agent.sh/) script on each agent machine to create volumes for local storage
-1. Execute ***kubectl apply -f local-storage-provisioner.yaml*** against the Kubernetes cluster to create the local storage provisioner.
-1. Now, you can deploy the SQL Server 2019 big data cluster following instructions [here](https://docs.microsoft.com/en-us/sql/big-data-cluster/deployment-guidance?view=sqlallproducts-allversions)
+1. Execute ***kubectl apply -f local-storage-provisioner.yaml*** against the Kubernetes cluster to create the local storage provisioner. This will create a Storage Class named "local-storage".
+1. Now, you can deploy the SQL Server 2019 big data cluster following instructions [here](https://docs.microsoft.com/en-us/sql/big-data-cluster/deployment-guidance?view=sqlallproducts-allversions). 
+Simply type in "local-storage" twice (once for data, once for logs) when facing the following prompt by azdata :
+
+`Kubernetes Storage Class - Config Path: spec.storage.data.className - Description: This indicates the name of the Kubernetes Storage Class to use. You must pre-provision the storage class and the persistent volumes or you can use a built in storage class if the platform you are deploying provides this capability. - Please provide a value:`

samples/features/security/tde-sql2019-standard/ReadMe.md

@@ -7,5 +7,7 @@ for i in $(seq 1 $PV_COUNT); do
   vol="vol$i"
 
   mkdir -p /mnt/local-storage/$vol
+  # If wondering why the next code line is needed, see https://github.com/kubernetes-sigs/sig-storage-local-static-provisioner/blob/master/docs/faqs.md#why-i-need-to-bind-mount-normal-directories-to-create-pvs-for-them
+  # Experience showed that the mount need to exist at cluster provision time. The fact they're not put in fstab and will disappear after reboot doesn't seem to be an issue once the K8S PV are created.
   mount --bind /mnt/local-storage/$vol /mnt/local-storage/$vol
 done

samples/features/security/tde-sql2019-standard/TDE_on_Standard.ipynb

@@ -4,5 +4,22 @@ In this folder, you will find various notebooks that you can use in [Azure Data
 The [What's New](https://docs.microsoft.com/en-us/sql/sql-server/what-s-new-in-sql-server-ver15?view=sql-server-ver15) article covers all the *NEW* features in SQL Server 2019.
 
 ## Notebook List
-1. Intelligent Query Processing
-*  Scalar UDF Inlining
+### Intelligent Query Processing
+*  **[Scalar_UDF_Inlining.ipynb](https://github.com/microsoft/sql-server-samples/blob/master/samples/features/intelligent-query-processing/notebooks/Scalar_UDF_Inlining.ipynb)** - This notebook demonstrates the benefits of Scalar UDF Inlining along with how to find out which UDFs in your database can be inlined.
+* **[IQP_tablevariabledeferred.ipynb](https://github.com/microsoft/sqlworkshops/blob/master/sql2019lab/01_IntelligentPerformance/iqp/iqp_tablevariabledeferred.ipynb)** - In this example, you will learn about the new cardinality estimation for table variables called deferred compilation.
+* **[Batch_Mode_on_Rowstore.ipynb](https://github.com/microsoft/sql-server-samples/blob/master/samples/features/intelligent-query-processing/notebooks/Batch_Mode_on_Rowstore.ipynb)** - In this notebook, you will learn about how Batch Mode for Rowstore can help execute queries faster on SQL Server 2019.
+
+### Security 
+* **[TDE_on_Standard.ipynb](https://github.com/microsoft/sql-server-samples/blob/master/samples/features/security/tde-sql2019-standard/TDE_on_Standard.ipynb)** - This notebook demonstrates the ability to enable TDE on SQL Server 2019 Standard Edition along with Encryption Scan SUSPEND and RESUME.
+* **[TDE_on_Standard_EKM.ipynb](https://github.com/microsoft/sql-server-samples/blob/master/samples/features/security/tde-sql2019-standard/TDE_on_Standard_EKM.ipynb)** - This notebook demonstrates the ability to enable TDE on a SQL Server 2019 Standard Edition using EKM and Azure Key Vault.
+
+### In-Memory Database
+* **[MemoryOptimizedTempDBMetadata-TSQL.ipynb](https://github.com/microsoft/sql-server-samples/blob/master/samples/features/in-memory-database/memory-optimized-tempdb-metadata/MemoryOptimizedTempDBMetadata-TSQL.ipynb)** - This is a T-SQL notebook which shows the benefits of Memory Optimized Tempdb metadata.
+* **[MemoryOptmizedTempDBMetadata-Python.ipynb](https://github.com/microsoft/sql-server-samples/blob/master/samples/features/in-memory-database/memory-optimized-tempdb-metadata/MemoryOptmizedTempDBMetadata-Python.ipynb)** - This is a Python notebook which shows the benefits of Memory Optimized Tempdb metadata.
+
+### Availability
+* **[Basic_ADR.ipynb](https://github.com/microsoft/sqlworkshops/blob/master/sql2019workshop/sql2019wks/04_Availability/adr/basic_adr.ipynb)** - In this notebook, you will see how fast rollback can now be with Accelerated Database Recovery. You will also see that a long active transaction does not affect the ability to truncate the transaction log.
+* **[Recovery_ADR.ipynb](https://github.com/microsoft/sqlworkshops/blob/master/sql2019workshop/sql2019wks/04_Availability/adr/recovery_adr.ipynb)** - In this example, you will see how Accelerated Database Recovery will speed up recovery.
+
+
+

samples/features/security/tde-sql2019-standard/TDE_on_Standard_EKM.ipynb

@@ -1,12 +1,12 @@
-declare @db_name sysname = 'master'
+USE <database_name, , > --> put the database name here like WideWorldImporters
 
 begin
 declare @result NVARCHAR(MAX);
-set @result = (select compatibility_level, recovery_model_desc, snapshot_isolation_state_desc, is_read_committed_snapshot_on, 
+set @result = (select database_name = name, compatibility_level, recovery_model_desc, snapshot_isolation_state_desc, is_read_committed_snapshot_on, 
 					is_auto_update_stats_on, is_auto_update_stats_async_on, delayed_durability_desc,
 					is_encrypted, is_auto_create_stats_incremental_on, is_arithabort_on, is_ansi_warnings_on, is_parameterization_forced
 from sys.databases
-where name = @db_name 
+where name = db_name()
 for xml raw('db'), elements);
 set @result += (select compatibility_level, snapshot_isolation_state_desc, is_read_committed_snapshot_on, 
 					is_auto_update_stats_on, is_auto_update_stats_async_on, delayed_durability_desc,
@@ -53,6 +53,8 @@ where name in ('cost threshold for parallelism','cursor threshold','fill factor
 for xml raw, elements
 );
 set @result += (select name = 'version', value = @@VERSION for xml raw, elements)
+set @result += (select name = 'script version', value = '1.0' for xml raw, elements)
+set @result += (select name = 'date', value = GETUTCDATE() for xml raw, elements)
 
 set @result += isnull
 ((SELECT scheduler_count, scheduler_total_count FROM sys.dm_os_sys_info
@@ -67,6 +69,25 @@ isnull((SELECT name = REPLACE([type], 'MEMORYCLERK_', 'MEMORY:')
    HAVING sum(pages_kb) /1024. /1024 > 1
    for xml raw, elements),'');
 
+set @result += 
+isnull((
+	select name = 'INDEX:'+schema_name(schema_id)+'.'+object_name(t.object_id)+'.'+ix.name,
+		value = concat(ix.type_desc COLLATE SQL_Latin1_General_CP1_CI_AS,
+		'/disabled:',is_disabled,'/row_locks:',allow_row_locks,'/page_locks:',allow_page_locks,'/filter:',filter_definition,'/compression_delay:',compression_delay)
+	from sys.indexes ix
+		join sys.tables t on t.object_id = ix.object_id
+	where ix.type <> 0
+for xml raw, elements),'');
+
+set @result += 
+isnull((
+	select	name = 'JOB::'+j.name + '/' + s.step_name, 
+			value = subsystem
+	from msdb.dbo.sysjobs j
+		join msdb.dbo.sysjobsteps s on j.job_id = s.job_id
+for xml raw, elements),'');
+
+
 select cast(@result as xml);
 
 end;