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Copy file name to clipboardExpand all lines: docs/big-data-cluster/deployment-upgrade.md
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@@ -5,7 +5,7 @@ description: Learn how to upgrade SQL Server Big Data Clusters to a new release.
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author: MikeRayMSFT
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ms.author: mikeray
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ms.reviewer: mihaelab
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ms.date: 02/13/2020
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ms.date: 09/02/2020
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ms.topic: conceptual
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ms.prod: sql
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ms.technology: big-data-cluster
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This section explains how to upgrade a SQL Server BDC from a supported release (starting with SQL Server 2019 GDR1) to a newer supported release.
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1. Verify no active Livy sessions.
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Make sure no active Livy sessions or batch jobs are running in Azure Data Studio. An easy way to confirm this is either through `curl` command or a browser to request these URLs:
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To increase the timeouts for an upgrade, use **--controller-timeout** and **--component-timeout** parameters to specify higher values when you issue the upgrade. This option is only available starting with SQL Server 2019 CU2 release. For example:
**--controller-timeout** designates the number of minutes to wait for the controller or controller db to finish upgrading.
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**--component-timeout** designates the amount of time that each subsequent phase of the upgrade has to complete.
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### Backup and delete the old cluster
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There is no in place upgrade for big data clusters deployed before SQL Server 2019 GDR1 release. The only way to upgrade to a new release is to manually remove and recreate the cluster. Each release has a unique version of `azdata` that is not compatible with the previous version. Also, if a newer container image is downloaded on cluster deployed with different older version, the latest image might not be compatible with the older images on the cluster. The newer image is pulled if you are using the `latest` image tag for in the deployment configuration file for the container settings. By default, each release has a specific image tag corresponding to the SQl Server release version. To upgrade to the latest release, use the following steps:
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There is no in place upgrade for big data clusters deployed before SQL Server 2019 GDR1 release. The only way to upgrade to a new release is to manually remove and recreate the cluster. Each release has a unique version of `azdata` that is not compatible with the previous version. Also, if a newer container image is downloaded on cluster deployed with different older version, the latest image might not be compatible with the older images on the cluster. The newer image is pulled if you are using the `latest` image tag for in the deployment configuration file for the container settings. By default, each release has a specific image tag corresponding to the SQL Server release version. To upgrade to the latest release, use the following steps:
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1. Before deleting the old cluster, back up the data on the SQL Server master instance and on HDFS. For the SQL Server master instance, you can use [SQL Server backup and restore](data-ingestion-restore-database.md). For HDFS, you [can copy out the data with `curl`](data-ingestion-curl.md).
> There are no SQL Server 2019 Big Data Clusters updates for CU7.
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## How to install updates
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To install updates, see [How to upgrade [!INCLUDE[big-data-clusters-2019](../includes/ssbigdataclusters-ss-nover.md)]](deployment-upgrade.md).
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## Known issues
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### Empty Livy jobs before you apply cumulative updates
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-**Affected releases**: Through current cumulative update
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-**Issue and customer impact**: During an upgrade, sparkhead returns 404 error.
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-**Workaround**: Before upgrading BDC, ensure that there are no active Livy sessions or batch jobs. Follow the instructions under [Upgrade from supported release](deployment-upgrade.md#upgrade-from-supported-release) to avoid this.
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If Livy returns a 404 error during the upgrade process, restart the Livy server on both sparkhead nodes. For example:
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|Product Versions | Latest Service Pack | Latest GDR | Latest cumulative update | CU Release Date | General Guidance |
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|--|--|--|--|--|--|
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|SQL Server 2019|N/A|[KB 4517790](https://support.microsoft.com/help/4517790)|CU 6[(KB 4563110)](https://support.microsoft.com/help/4563110)|8/4/2020|[SQL Server 2019 Installation](https://docs.microsoft.com/sql/database-engine/install-windows/installation-for-sql-server)|
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|SQL Server 2019|N/A|[KB 4517790](https://support.microsoft.com/help/4517790)|CU 7[(KB 4570012)](https://support.microsoft.com/help/4570012)|9/2/2020|[SQL Server 2019 Installation](https://docs.microsoft.com/sql/database-engine/install-windows/installation-for-sql-server)|
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|SQL Server 2017|N/A|[KB 4505224](https://support.microsoft.com/help/4505224)|CU 21 [(KB 4557397)](https://support.microsoft.com/help/4557397)|7/1/2020|[SQL Server 2017 Installation](https://docs.microsoft.com/sql/database-engine/install-windows/installation-for-sql-server)|
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|SQL Server 2016|SP2 [(KB 4052908)](https://support.microsoft.com/help/4052908)|[KB 4532097](https://support.microsoft.com/help/4532097)|CU 14 [(KB 4564903)](https://support.microsoft.com/kb/4564903)|8/6/2020|[SQL Server 2016 Installation](https://technet.microsoft.com/library/bb500469.aspx)|
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|SQL Server 2016|SP1 [(KB 3182545)](https://support.microsoft.com/help/3182545/sql-server-2016-service-pack-1-release-information)|[KB 4505219](https://support.microsoft.com/help/4505219)|CU 15 + GDR [(KB 4505221)](https://support.microsoft.com/help/4505221)|7/9/2019|[SQL Server 2016 Installation](https://technet.microsoft.com/library/bb500469.aspx)|
Copy file name to clipboardExpand all lines: docs/linux/sql-server-linux-release-notes-2019.md
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@@ -3,7 +3,7 @@ title: Release notes for SQL Server 2019 on Linux
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description: This article contains the release notes and supported features for SQL Server 2019 running on Linux. Release notes are included for the most recent release and several previous releases.
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author: VanMSFT
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ms.author: vanto
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ms.date: 08/04/2020
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ms.date: 09/02/2020
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ms.topic: conceptual
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ms.prod: sql
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ms.technology: linux
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-[Enable SQL Server Agent](sql-server-linux-setup-sql-agent.md)
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## <aid="cu7"></a> CU7 (August 2020)
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This is the Cumulative Update 7 (CU7) release of SQL Server 2019 (15.x). The SQL Server Database Engine version for this release is 15.0.4063.15. For information about the fixes and improvements, see <https://support.microsoft.com/help/4570012>.
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### Package details
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For manual or offline package installations, you can download the RPM and Debian packages with the information in the following table:
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> [!NOTE]
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> Starting with CU1, the offline package installation links for Red Hat are pointing to RHEL 8 packages. If you are looking for RHEL 7 packages, refer to the download path <https://packages.microsoft.com/rhel/7/mssql-server-2019/>
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>
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> **Ubuntu 18.04** is now supported on SQL Server 2019 starting with CU3. The offline package installation links for Ubuntu are pointing to Ubuntu 18.04 packages. If you are looking for Ubuntu 16.04 packages, refer to the download path <https://packages.microsoft.com/ubuntu/16.04/mssql-server-2019/pool/main/m/>
This is the Cumulative Update 6 (CU6) release of SQL Server 2019 (15.x). The SQL Server Database Engine version for this release is 15.0.4053.23. For information about the fixes and improvements, see <https://support.microsoft.com/help/4563110>.
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This is the Cumulative Update 6 (CU6) release of SQL Server 2019 (15.x). The SQL Server Database Engine version for this release is 15.0.4053.23. For information about the fixes and improvements, see <https://support.microsoft.com/help/4563110>
@@ -251,7 +251,6 @@ GRANT SELECT ON security.fn_securitypredicate TO Sales1;
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GRANTSELECTONsecurity.fn_securitypredicate TO Sales2;
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```
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-
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Now test the filtering predicate, by selected from the Sales table as each user.
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```sql
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SELECT*FROM Sales;
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REVERT;
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```
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The Manager should see all six rows. The Sales1 and Sales2 users should only see their own sales.
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Alter the security policy to disable the policy.
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### <aname="external"></a> B. Scenarios for using Row Level Security on an Azure Synapse external table
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This short example creates three users and an external table with six rows. It then creates an inline table-valued function and a security policy for the external table. The example shows how select statements are filtered for the various users.
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This short example creates three users and an external table with six rows. It then creates an inline table-valued function and a security policy for the external table. The example shows how select statements are filtered for the various users.
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### Prerequisites
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Create three user accounts that will demonstrate different access capabilities.
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1. You must have a SQL pool. See [Create a Synapse SQL pool](/azure/synapse-analytics/sql-data-warehouse/create-data-warehouse-portal)
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1. The server hosting your SQL pool must be registered with AAD and you must have an Azure storage account with Storage Blog Contributor permissions. Follow the steps [here](/azure/azure-sql/database/vnet-service-endpoint-rule-overview#steps).
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1. Create a file system for your Azure Storage account. Use Storage Explorer to view your storage account. Right click on containers and select *Create file system*.
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Once you have the prerequisites in place, create three user accounts that will demonstrate different access capabilities.
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```sql
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CREATE LOGIN Manager WITH PASSWORD ='somepassword'
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--run in master
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CREATE LOGIN Manager WITH PASSWORD ='<user_password>'
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GO
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CREATE LOGIN Sales1 WITH PASSWORD ='somepassword'
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CREATE LOGIN Sales1 WITH PASSWORD ='<user_password>'
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GO
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CREATE LOGIN Sales2 WITH PASSWORD ='somepassword'
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CREATE LOGIN Sales2 WITH PASSWORD ='<user_password>'
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GO
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--run in master and your SQL pool database
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CREATEUSERManager FOR LOGIN Manager;
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CREATEUSERSales1 FOR LOGIN Sales1;
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CREATEUSERSales2 FOR LOGIN Sales2 ;
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Populate the table with six rows of data, showing three orders for each sales representative.
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```sql
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INSERT INTO Sales VALUES(1, 'Sales1', 'Valve', 5);
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INSERT INTO Sales VALUES(2, 'Sales1', 'Wheel', 2);
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INSERT INTO Sales VALUES(3, 'Sales1', 'Valve', 4);
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INSERT INTO Sales VALUES(4, 'Sales2', 'Bracket', 2);
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INSERT INTO Sales VALUES(5, 'Sales2', 'Wheel', 5);
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INSERT INTO Sales VALUES(6, 'Sales2', 'Seat', 5);
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INSERT INTO Sales VALUES(1, 'Sales1', 'Valve', 5);
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INSERT INTO Sales VALUES(2, 'Sales1', 'Wheel', 2);
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INSERT INTO Sales VALUES(3, 'Sales1', 'Valve', 4);
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INSERT INTO Sales VALUES(4, 'Sales2', 'Bracket', 2);
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INSERT INTO Sales VALUES(5, 'Sales2', 'Wheel', 5);
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INSERT INTO Sales VALUES(6, 'Sales2', 'Seat', 5);
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-- View the 6 rows in the table
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SELECT*FROM Sales;
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```
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Create an Azure Synapse external table from the Sales table created.
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```sql
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CREATE MASTER KEY ENCRYPTION BY PASSWORD ='somepassword';
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CREATE MASTER KEY ENCRYPTION BY PASSWORD ='<user_password>';
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CREATEDATABASESCOPED CREDENTIAL msi_cred WITH IDENTITY ='Managed Service Identity';
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CREATE EXTERNAL DATA SOURCE ext_datasource_with_abfss WITH (TYPE = hadoop, LOCATION ='abfss://myfile@mystorageaccount.dfs.core.windows.net', CREDENTIAL = msi_cred);
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CREATE EXTERNAL DATA SOURCE ext_datasource_with_abfss WITH (TYPE = hadoop, LOCATION ='abfss://<file_system_name@storage_account>.dfs.core.windows.net', CREDENTIAL = msi_cred);
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CREATE EXTERNAL FILE FORMAT MSIFormat WITH (FORMAT_TYPE=DELIMITEDTEXT);
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GRANTSELECTON Sales_ext TO Manager;
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```
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Create a security policy on external table using the function in session A as a filter predicate. The state must be set to ON to enable the policy.
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Create a new schema, and an inline table-valued function, you may have completed this in example A. The function returns 1 when a row in the SalesRep column is the same as the user executing the query (`@SalesRep = USER_NAME()`) or if the user executing the query is the Manager user (`USER_NAME() = 'Manager'`).
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```sql
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CREATESCHEMASecurity;
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GO
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CREATEFUNCTIONSecurity.fn_securitypredicate(@SalesRep AS sysname)
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RETURNS TABLE
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WITH SCHEMABINDING
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AS
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RETURN SELECT1AS fn_securitypredicate_result
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WHERE @SalesRep = USER_NAME() OR USER_NAME() ='Manager';
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```
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Create a security policy on your external table using the inline table-valued function as a filter predicate. The state must be set to ON to enable the policy.
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