Create microsoft.sp_update_statistics.sql

README.md

@@ -1,6 +1,6 @@
-# Azure SQL Data Warehouse Samples Repository
+# Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse) Samples Repository
 
-This GitHub repository contains code samples that demonstrate how to use [Microsoft's Azure SQL Data Warehouse](http://aka.ms/sqldw) service. Each sample includes a README file
+This GitHub repository contains code samples that demonstrate how to use [Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse)](http://aka.ms/sqldw) service. Each sample includes a README file
 that explains how to run and use the sample.
 
 ## Releases in this repository

arm-templates/sqlDwLogicAppAutoPause/readme.md

@@ -1,4 +1,4 @@
-# SQL Data Warehouse Auto-Pause and Auto-Resume using Logic apps
+# Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse) Auto-Pause and Auto-Resume using Logic apps
 
 This sample includes 3 templates to showcase different scenarios for using Logic apps to pause and resume compute resources for a SQL Data Warehouse
 
@@ -11,7 +11,7 @@ In this scenario, you deploy a timer-based Logic app configured with a [service
 **1. Deploy timeschedule-based autopause and autoresume template**
 
 > [!NOTE]
-> You will need an existing SQL Data Warehouse to deploy the template below. 
+> You will need an existing Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse) to deploy the template below. 
 
 * Use the server-name and database-name properties of your data warehouse, and a pause-resume schedule to complete the deployment. 
 * You can additionally specify the timezone for the pause-resume schedule, and if the schedule should only run on weekdays.

arm-templates/sqlDwLogicAppAutoScale/readme.md

@@ -1,4 +1,3 @@
-# SQL Data Warehouse AutoScale using Logic apps
 
 This sample includes 3 templates to showcase different scenarios for using Logic apps to autoscale compute resources for a SQL Data Warehouse
 

arm-templates/sqlDwSpokeDbTemplate/README.md

@@ -1,4 +1,5 @@
-# SQL Data Warehouse Hub Spoke Template with SQL Databases
+# Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse) AutoScale using Logic apps
+ Hub Spoke Template with SQL Databases
 
 <a href="https://ms.portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FMicrosoft%2Fsql-data-warehouse-samples%2Fmaster%2Farm-templates%2FsqlDwSpokeDbTemplate%2Fazuredeploy.json" target="_blank">
 <img src="https://raw.githubusercontent.com/Azure/azure-quickstart-templates/master/1-CONTRIBUTION-GUIDE/images/deploytoazure.png"/>

arm-templates/sqlDwTimerScaler/README.md

@@ -1,5 +1,6 @@
 
-# SQL Data Warehouse Timer Scaler
+# Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse) AutoScale using Logic apps
+ Timer Scaler
 
 <a href="https://ms.portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FMicrosoft%2Fsql-data-warehouse-samples%2Fmaster%2Farm-templates%2FsqlDwTimerScaler%2Fazuredeploy.json" target="_blank">
 <img src="https://raw.githubusercontent.com/Azure/azure-quickstart-templates/master/1-CONTRIBUTION-GUIDE/images/deploytoazure.png"/>

samples/adf/Readme.md

@@ -1,6 +1,7 @@
 # Azure Data Factory samples
 
-This is a collection of samples that demonstrate how to use [Azure SQL Data Warehouse](https://aka.ms/sqldw) with [Azure Data Factory](https://azure.microsoft.com/services/data-factory).
+This is a collection of samples that demonstrate how to use [Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse) AutoScale using Logic apps
+](https://aka.ms/sqldw) with [Azure Data Factory](https://azure.microsoft.com/services/data-factory).
 
 Samples:
 

samples/automation/README.md

@@ -1,6 +1,7 @@
 # Azure Automation Samples
 
-This is a collection of samples that demonstrate how to use [Azure SQL Data Warehouse](https://aka.ms/sqldw) with [Azure Automation](https://azure.microsoft.com/services/automation).
+This is a collection of samples that demonstrate how to use [Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse) AutoScale using Logic apps
+](https://aka.ms/sqldw) with [Azure Automation](https://azure.microsoft.com/services/automation).
 
 When using the SQL Azure Data Warehouse service (ADW) it can be important to automate operations. One of these goals of this project is to help with the pause, resume and scale of the data warehouse.
 
@@ -10,7 +11,8 @@ If you have an automated ETL/ELT process, it can be important to properly manage
 
 ## When to Pause or Scale ADW
 
-Your Azure SQL Data Warehouse is not a regular SQL server. Though it has many similarities, many of request of the service cause [data movement operations](https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute#understanding-data-movement) which can be time consuming. When you pause/scale the service, all running queries are canceled. Other than having lost productivity, rollback of queries that are in the middle of data movement could take hours to complete. While the rollback operations are processing the compute will be running but you will not be able to connect or submit request. These workflows will test to ensure that is is OK to pause or scale before taking action. Though there are retry sequences in the workflows, it will also be important to ensure that the data warehouse is in the right state before your processes continue.
+Your Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse) AutoScale using Logic apps
+ is not a regular SQL server. Though it has many similarities, many of request of the service cause [data movement operations](https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute#understanding-data-movement) which can be time consuming. When you pause/scale the service, all running queries are canceled. Other than having lost productivity, rollback of queries that are in the middle of data movement could take hours to complete. While the rollback operations are processing the compute will be running but you will not be able to connect or submit request. These workflows will test to ensure that is is OK to pause or scale before taking action. Though there are retry sequences in the workflows, it will also be important to ensure that the data warehouse is in the right state before your processes continue.
 
 ## How to Use Workflows
 

samples/automation/RefreshReplicatedTable/README.md

@@ -1 +1 @@
-# Refresh Replicated Tables in a Azure SQL Data Warehouse
+# Refresh Replicated Tables in a Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse)

samples/automation/SuspendOrPauseAzureSQLDataWarehouse/README.md

@@ -1,4 +1,4 @@
-# Suspend or Pause Azure SQL Data Warehouse
+# Suspend or Pause Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse)
 
 See the blog post [here](https://blogs.msdn.microsoft.com/allanmiller/2017/09/20/pausing-azure-sql-data-warehouse-using-an-automation-runbook/ "Pausing Azure SQL Data Warehouse using an Automation Runbook")
 

samples/scripts/Readme.md

@@ -1,6 +1,6 @@
 # Database scripts
 
-Contains sample scripts for managing, monitoring, and maintaining Azure SQL Data Warehouse databases.
+Contains sample scripts for managing, monitoring, and maintaining Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse) databases.
 
 ## Data-load
 The [data-load](data-load/) folder contains TSQL scripts that can be adapted to simplify loading DW tables from Azure Blob Storage or Azure Data Lake Store using tables in DW as the model.

samples/scripts/data-load/README.md

@@ -1,6 +1,6 @@
-# Auto Generate Azure SQL DW Load – TSQL Scripts
+# Auto Generate Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse)Load – TSQL Scripts
 
-These scripts were developed to help with a large Azure SQL DW POC with one of the DMJ customers. Some of the assumptions made in the scripts are very specific to the scenario encountered at the customer. Feel free to adapt these scripts to your data loading scenario.
+These scripts were developed to help with a large Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse) POC with one of the DMJ customers. Some of the assumptions made in the scripts are very specific to the scenario encountered at the customer. Feel free to adapt these scripts to your data loading scenario.
 
 Each of the scripts creates the appropriate objects to allow an optimal performance DW load from text files in blob storage into Azure SQL DW using Polybase. You can easily change the external data source to use Azure Data Lake as a source as well. The optimal nature of the load will depend on the number of files provided. Our customer had many gzip’d files that distributed well among several DW nodes (few larger gzip’d files would not have done as well).
 

samples/scripts/queries/Readme.md

@@ -1,5 +1,5 @@
 # Database queries
 
-This folder contains a collection of scripts used to monitor query usage in your [Azure SQL Data Warehouse](http://aka.ms/sqldw) database. 
+This folder contains a collection of scripts used to monitor query usage in your [Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse)](http://aka.ms/sqldw) database. 
 
  - [query_memory_usage](query_memory_usage.sql): This query shows the memory utilization of queries run against your data warehouse. It allows you to easily identify any query that could benefit from additional memory via a larger resource class.

samples/sqlops/MonitoringScripts/README.md

@@ -1,6 +1,6 @@
 ## Introduction
 
-The following scripts are leveraged to create custom monitoring dashboard widgets in SQL Operations Studio (preview) for Azure SQL Data Warehouse. 
+The following scripts are leveraged to create custom monitoring dashboard widgets in SQL Operations Studio (preview) for Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse). 
 
 ## Current Widgets
 

samples/sqlops/README.md

@@ -1,13 +1,13 @@
-## Azure SQL Data Warehouse Insights
+## Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse) Insights
 The Azure SQL Data Warehouse insight widget extension provides a pre-built dashboard surfacing insights to your data warehouse. This helps with scenarios around managing and tuning your data warehouse to ensure it is optimized for consistent performance.
 
 For any issues with this extension, please submit them here:
-[SQL Data Warehouse Samples - issues](https://github.com/Microsoft/sql-data-warehouse-samples/issues).
+[Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse) Samples - issues](https://github.com/Microsoft/sql-data-warehouse-samples/issues).
 
 If you have any feedback, please contact: sqldwadvisor@service.microsoft.com.
 
 The following widgets are generated by using T-SQL monitoring scripts embedded within SQL Operations Studio. All monitoring scripts are uploaded to the following github:
-[SQL Data Warehouse Samples](https://github.com/Microsoft/sql-data-warehouse-samples/tree/master/samples/sqlops/MonitoringScripts).
+[Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse) Samples](https://github.com/Microsoft/sql-data-warehouse-samples/tree/master/samples/sqlops/MonitoringScripts).
 
 ## Backup Details and User Activities
 Identify and understand workload patterns through active sessions, active queries, queued queries, loads, and backups.

solutions/monitoring/Readme.md

@@ -1,5 +1,5 @@
-# Microsoft Toolkit for SQL Data Warehouse
-The Microsoft Toolkit for SQL Data Warehouse offers a set of views and procedures that help you optimize your [Azure SQL Data Warehouse](http://aka.ms/sqldw). 
+# Microsoft Toolkit for Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse)
+The Microsoft Toolkit for SQL Data Warehouse offers a set of views and procedures that help you optimize your [Azure Synapse Analytics SQL Pool (formerly SQL Data Warehouse)](http://aka.ms/sqldw). 
 
 ## Installation
 The toolkit installs the following items:

solutions/monitoring/scripts/procs/microsoft.sp_update_statistics.sql

@@ -0,0 +1,90 @@
+PRINT 'Info: Creating the ''microsoft.sp_update_statistics'' procedure';
+GO
+
+CREATE PROCEDURE    [microsoft].[sp_update_statistics] 
+(   @update_type    tinyint -- 1 default 2 fullscan 3 sample 4 resample
+   ,@sample_pct     tinyint
+   ,@user_created     tinyint -- 0-system created stats, 1-user created stats
+)
+AS
+
+IF @update_type NOT IN (1,2,3,4)
+BEGIN;
+    THROW 151000,'Invalid value for @update_type parameter. Valid range 1 (default), 2 (fullscan), 3 (sample) or 4 (resample).',1;
+END;
+
+IF @sample_pct IS NULL
+BEGIN;
+    SET @sample_pct = 20;
+END;
+
+IF OBJECT_ID('tempdb..#stats_ddl') IS NOT NULL
+BEGIN
+    DROP TABLE #stats_ddl
+END
+
+CREATE TABLE #stats_ddl
+WITH
+(
+    DISTRIBUTION = HASH([seq_nmbr])
+)
+AS
+SELECT
+    CASE @update_type
+    WHEN 1
+    THEN 'UPDATE STATISTICS '+[two_part_name]+'('+[stats_name]+');'
+    WHEN 2
+    THEN 'UPDATE STATISTICS '+[two_part_name]+'('+[stats_name]+') WITH FULLSCAN;'
+    WHEN 3
+    THEN 'UPDATE STATISTICS '+[two_part_name]+'('+[stats_name]+') WITH SAMPLE '+CAST(@sample_pct AS VARCHAR(20))+' PERCENT;'
+    WHEN 4
+    THEN 'UPDATE STATISTICS '+[two_part_name]+'('+[stats_name]+') WITH RESAMPLE;'
+    END AS [update_stats_ddl]
+,   [seq_nmbr]
+FROM    (
+SELECT
+        sm.[name]                                                                AS [schema_name]
+,        tb.[name]                                                                AS [table_name]
+,        st.[name]                                                                AS [stats_name]
+,        st.[has_filter]                                                            AS [stats_is_filtered]
+,       ROW_NUMBER()
+        OVER(ORDER BY (SELECT NULL))                                            AS [seq_nmbr]
+,                                 QUOTENAME(sm.[name])+'.'+QUOTENAME(tb.[name])  AS [two_part_name]
+,        QUOTENAME(DB_NAME())+'.'+QUOTENAME(sm.[name])+'.'+QUOTENAME(tb.[name])  AS [three_part_name]
+,  STATS_DATE(st.[object_id],st.[stats_id])   AS [stats_last_updated_date]
+FROM    sys.objects            AS ob
+JOIN    sys.stats            AS st    ON    ob.[object_id]        = st.[object_id]
+JOIN    sys.stats_columns    AS sc    ON    st.[stats_id]        = sc.[stats_id]
+                                    AND st.[object_id]        = sc.[object_id]
+JOIN    sys.columns            AS co    ON    sc.[column_id]        = co.[column_id]
+                                    AND    sc.[object_id]        = co.[object_id]
+JOIN    sys.tables            AS tb    ON    co.[object_id]        = tb.[object_id]
+JOIN    sys.schemas            AS sm    ON    tb.[schema_id]        = sm.[schema_id]
+WHERE    1=1 and STATS_DATE(st.[object_id],st.[stats_id])   is not null
+AND        st.[user_created]   = @user_created 
+GROUP BY
+        sm.[name]
+,        tb.[name]
+,        st.[name]
+,        st.[filter_definition]
+,        st.[has_filter]
+) t1 
+
+;
+
+DECLARE @i INT              = 1
+,       @t INT              = (SELECT COUNT(*) FROM #stats_ddl)
+,       @s NVARCHAR(4000)   = N''
+
+WHILE @i <= @t
+BEGIN
+    SET @s=(SELECT update_stats_ddl FROM #stats_ddl WHERE seq_nmbr = @i);
+
+    PRINT @s
+    EXEC sp_executesql @s
+    SET @i+=1;
+END
+
+
+DROP TABLE #stats_ddl;
+