mirror of
https://github.com/Microsoft/sql-server-samples.git
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97 lines
3.8 KiB
Transact-SQL
97 lines
3.8 KiB
Transact-SQL
USE master;
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GO
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-- Create login root that is part of sysadmin. You can then login as root to get the integrated
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-- login experience in Azure Data Studio
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IF SUSER_SID('root') IS NULL
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BEGIN
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CREATE LOGIN root WITH PASSWORD = '$(SA_PASSWORD)';
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ALTER SERVER ROLE sysadmin ADD MEMBER root;
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END;
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GO
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-- Enable external scripts execution for R/Python/Java:
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DECLARE @config_option nvarchar(100) = 'external scripts enabled';
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IF NOT EXISTS(SELECT * FROM sys.configurations WHERE name = @config_option)
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BEGIN
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exec sp_configure @config_option, 1;
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RECONFIGURE WITH OVERRIDE;
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END;
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GO
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IF DB_ID('sales') IS NULL
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RESTORE DATABASE sales
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FROM DISK=N'/var/opt/mssql/data/tpcxbb_1gb.bak'
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WITH
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MOVE N'tpcxbb_1gb' TO N'/var/opt/mssql/data/sales.mdf',
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MOVE N'tpcxbb_1gb_log' TO N'/var/opt/mssql/data/sales.ldf';
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GO
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USE sales;
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GO
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-- Create database master key (required for database scoped credentials used in the samples)
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IF NOT EXISTS(SELECT * FROM sys.databases WHERE name = DB_NAME() and is_master_key_encrypted_by_server = 1)
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CREATE MASTER KEY ENCRYPTION BY PASSWORD = 'sql19bigdatacluster!';
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-- Create default data sources for SQL Big Data Cluster
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IF NOT EXISTS(SELECT * FROM sys.external_data_sources WHERE name = 'SqlDataPool')
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CREATE EXTERNAL DATA SOURCE SqlDataPool
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WITH (LOCATION = 'sqldatapool://service-mssql-controller:8080/datapools/default');
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IF NOT EXISTS(SELECT * FROM sys.external_data_sources WHERE name = 'SqlStoragePool')
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CREATE EXTERNAL DATA SOURCE SqlStoragePool
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WITH (LOCATION = 'sqlhdfs://service-mssql-controller:8080');
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GO
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-- Create view used for ML services training and scoring stored procedures
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CREATE OR ALTER VIEW [dbo].[web_clickstreams_book_clicks]
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AS
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SELECT
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/* There is a bug in TPCx-BB data generator which results in data where all users have purchased books.
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As a result, we cannot use the data as is for ML training purposes. So we will treat users with 1-5 clicks
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in the book category as not interested in books. */
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CASE WHEN q.clicks_in_category < 6 THEN 0 ELSE q.clicks_in_category END AS clicks_in_category,
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CASE WHEN cd.cd_education_status IN ('Advanced Degree', 'College', '4 yr Degree', '2 yr Degree') THEN 1 ELSE 0 END AS college_education,
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CASE WHEN cd.cd_gender = 'M' THEN 1 ELSE 0 END AS male,
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COALESCE(cd.cd_credit_rating, 'Unknown') as cd_credit_rating,
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q.clicks_in_1,
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q.clicks_in_2,
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q.clicks_in_3,
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q.clicks_in_4,
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q.clicks_in_5,
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q.clicks_in_6,
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q.clicks_in_7,
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q.clicks_in_8,
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q.clicks_in_9
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FROM(
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SELECT
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w.wcs_user_sk,
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SUM( CASE WHEN i.i_category = 'Books' THEN 1 ELSE 0 END) AS clicks_in_category,
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SUM( CASE WHEN i.i_category_id = 1 THEN 1 ELSE 0 END) AS clicks_in_1,
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SUM( CASE WHEN i.i_category_id = 2 THEN 1 ELSE 0 END) AS clicks_in_2,
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SUM( CASE WHEN i.i_category_id = 3 THEN 1 ELSE 0 END) AS clicks_in_3,
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SUM( CASE WHEN i.i_category_id = 4 THEN 1 ELSE 0 END) AS clicks_in_4,
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SUM( CASE WHEN i.i_category_id = 5 THEN 1 ELSE 0 END) AS clicks_in_5,
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SUM( CASE WHEN i.i_category_id = 6 THEN 1 ELSE 0 END) AS clicks_in_6,
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SUM( CASE WHEN i.i_category_id = 7 THEN 1 ELSE 0 END) AS clicks_in_7,
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SUM( CASE WHEN i.i_category_id = 8 THEN 1 ELSE 0 END) AS clicks_in_8,
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SUM( CASE WHEN i.i_category_id = 9 THEN 1 ELSE 0 END) AS clicks_in_9
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FROM web_clickstreams as w
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INNER JOIN item as i ON (w.wcs_item_sk = i_item_sk
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AND w.wcs_user_sk IS NOT NULL)
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GROUP BY w.wcs_user_sk
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) AS q
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INNER JOIN customer as c ON q.wcs_user_sk = c.c_customer_sk
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INNER JOIN customer_demographics as cd ON c.c_current_cdemo_sk = cd.cd_demo_sk;
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GO
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-- Create table for storing the ML models
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DROP TABLE IF EXISTS sales_models;
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CREATE TABLE sales_models (
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model_name varchar(100) PRIMARY KEY,
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model varbinary(max) NOT NULL,
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model_native varbinary(max) NULL,
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created_by nvarchar(500) NOT NULL DEFAULT(SYSTEM_USER),
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create_time datetime2 NOT NULL DEFAULT(SYSDATETIME())
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);
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GO
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