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![Native compilation enables faster data access and more efficient query execution
than interpreted (traditional) Transact-SQL.
CREATE PROCEDURE [dbo].[usp_1]
WITH NATIVE_COMPILATION, SCHEMABINDING, EXECUTE AS OWNER
AS BEGIN ATOMIC WITH
(
TRANSACTION ISOLATION LEVEL = SNAPSHOT, LANGUAGE = N'us_english'
)
SELECT c1, c2 FROM dbo.T1
END
GO
To create a Natively Compiled Stored Procedure, use the NATIVE_COMPILATION, SCHEMABINDING,
and BEGIN ATOMIC clauses](https://image.slidesharecdn.com/brk3315-180508205543/85/Modernizing-Mission-Critical-Apps-with-SQL-Server-21-320.jpg)
![Steps to creating a columnstore index (NCCI)
Add a columnstore index to the table by executing the T-SQL
SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice,
SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty
FROM Sales.SalesOrderDetail
GROUP BY ProductID
ORDER BY ProductID
Execute the query that should use the columnstore index to scan the table
SELECT * FROM sys.indexes WHERE name = 'IX_SalesOrderDetail_ColumnStore'
GO
SELECT *
FROM sys.dm_db_index_usage_stats
WHERE database_id = DB_ID('AdventureWorks')
AND object_id = OBJECT_ID('AdventureWorks.Sales.SalesOrderDetail');
Verify that the columnstore index was used by looking up its object_id and confirming
that it appears in the usage stats for the table
CREATE NONCLUSTERED COLUMNSTORE INDEX [IX_SalesOrderDetail_ColumnStore]
ON Sales.SalesOrderDetail
(UnitPrice, OrderQty, ProductID)
GO](https://image.slidesharecdn.com/brk3315-180508205543/85/Modernizing-Mission-Critical-Apps-with-SQL-Server-25-320.jpg)
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- 3. HYBRID On-premises Private cloud Cloud Security and privacy Flexibility ofchoice Reason over any data, anywhere Data warehouses Data lakes Operational databases Data warehouses Data lakes Operational databases
- 4. Security and privacyFlexibilityof choiceReason over any data, anywhere SQL Server Data warehouses #1 TPC-H performance Operational databases Industry leader 4 years in a row Only commercial DB with AI built-in R 0 50 100 150 Vulnerabilities SQL Server Most secure over last 7 years Any language, any platform, anywhere MULTIJAVA 1/10th the cost of Oracle Data lakes T-SQL query over any data
- 5. End-to-end mobile BI onany device Choice of platform and language Most secure over the last 7 years 0 20 40 60 80 100 120 140 160 180 200 Vulnerabilities(2010-2016) A fraction of the cost Self-serviceBIperuser Only commercial DB with AI built-in Microsoft Tableau Oracle $120 $480 $2,230 Industry-leading performance 1/10 Most consistent data platform #1 TPC-H performance 1TB, 10TB, 30TB #1 TPC-E performance #1 price/performance T-SQL Java C/C++ C#/VB.NET PHP Node.js Python Ruby R R and Python + in-memory at massive scale Native T-SQL scoring Private cloud Public cloud In-memory across all workloads 1/10th the cost of Oracle
- 6. $320K SQL Server BUSINESS INTELLIGENCE DATAWAREHOUSING OLTP ETL 11.7x more $640,000$803,000$1,272,000$3,433,000$3,745,000 Everything built-inMOBILE BI & SELF-SERVICE BI #1 TPC-H—DW INDUSTRY LEADER—OLTP BUILT-IN ETL R SERVERIN-DB ADVANCED ANALYTICS
- 7. The above graphicswere published by Gartner, Inc. as part of larger research documents and should be evaluated in the context of the entire document. The Gartner document is available upon request from Microsoft. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Leader in BI and analytics platforms Leader in data management solutions for analytics
- 9. SQL Server onthe platform of your choice Choice of platform and language
- 10. Windows Linux Editions Developer,Express, Web, Standard, Enterprise Services Database Engine, Integration Services ML Services, Analysis Services, Reporting Services, MDS, DQS Mission critical performance Maximum number of cores Unlimited Unlimited Maximum memory utilized per instance 24 TB 12 TB Maximum database size 524 PB 524 PB Basic OLTP (Basic In-Memory OLTP, Basic operational analytics) Advanced OLTP (Advanced In-Memory OLTP, Advanced operational analytics, adaptive query processing) Basic high availability (2-node single database failover, non-readable secondary) Advanced HA (Always On - multi-node, multi-db failover, readable secondaries) Security Basic security (Basic auditing, Row-level security, Data masking, Always Encrypted) Advanced security (Transparent Data Encryption) Data warehousing PolyBase Basic data warehousing/data marts (Basic In-Memory ColumnStore, Partitioning, Compression) Advanced data warehousing (Advanced In-Memory ColumnStore) Advanced data integration (Fuzzy grouping and look ups) Tools Windows ecosystem: Full-fidelity Management & Dev Tool (SSMS & SSDT), command line tools Linux/OSX/Windows ecosystem: Dev tools (VS Code), DB Admin GUI tool, command line tools Developer Programmability (T-SQL, CLR, Data Types, JSON, Graph) Windows Filesystem Integration - FileTable BI & Advanced Analytics Corporate Business Intelligence (Multi-dimensional models, Basic tabular model) Machine Learning Services (R and Python integration) Hybrid cloud Stretch Database
- 12. SQL Platform AbstractionLayer (SQLPAL) RDBMS IS AS RS Windows Linux Windows Host Ext. Linux Host Extension SQL Platform Abstraction Layer (SQLPAL) Host extension mapping to OS system calls (IO, Memory, CPU scheduling) Win32-like APIsSQL OS API SQL OS v2 All other systems System resource & latency sensitive code paths
- 13. SQL Server serviceruns natively using systemd Linux file paths are supported in T-SQL statements and scripts (defining/changing the path, database backup files) Popular Linux high-availability solutions like Pacemaker and Corosync Familiar experience Package-based installation using yum for Fedora-based distributions, apt-get for Debian-based distributions, and zypper for SLES Existing package update/upgrade processes for SQL Server upgrade Standard installation process SQL Server command-line tools (sqlcmd, bcp) available for Linux (and soon on macOS) Existing Windows tools such as SQL Server Management Studio (SSMS), SQL Server Data Tools (SSDT), and PowerShell module ( sqlps) to manage SQL Server on Linux from Windows Cross Platform open source tools such SQL Server Ops Studio, mssql-cli Visual Studio Code extension for SQL Server on macOS, Linux, or Windows Cross-platform tools
- 14. Choice of platformand language Always On cross-platform capabilities
- 15. HW and storagecosts slashed 80% SQL Server on the platform of your choice FLEXIBILITY CHALLENGE High HW and storage expenses for tier two workloads hampered consulting firm’s profitability IMPACT Cut costs 80% and time to ROI 70% with white box servers running SQL Server on Linux Saved resources with easy migration from Windows to Linux SQL Server on LinuxBefore Tier 1 Tier 2 Tier 2 Tier 1 Tier 2 Windows on leading servers Windows on leading server Linux on white box servers 80% savings on tier 2 workloads Tier 2
- 17. Real-time reporting withoutcompromising performance 30x transactions In-Memory OLTP
- 19. ALTER DATABASE CURRENT SETCOMPATIBILITY_LEVEL = 140; GO Recommended to set the database to the latest compatibility level, particularly for In-Memory OLTP: ALTER DATABASE CURRENT SET MEMORY_OPTIMIZED_ELEVATE_TO_SNAPSHOT=ON GO When a transaction involves both a disk-based table and a memory-optimized table, it’s essential that the memory- optimized portion of the transaction operates at the transaction isolation level named SNAPSHOT. ALTER DATABASE AdventureWorks ADD FILEGROUP AdventureWorks_mod CONTAINS memory_optimized_data GO ALTER DATABASE AdventureWorks ADD FILE (NAME='AdventureWorks_mod', FILENAME='c:varoptmssqldataAdventureWorks_mod') TO FILEGROUP AdventureWorks_mod GO Before you can create a memory-optimized table, you must first create a memory-optimized FILEGROUP and a container for data files:
- 20. CREATE TABLE dbo.ShoppingCart( ShoppingCartId INT IDENTITY(1,1) PRIMARY KEY NONCLUSTERED, UserId INT NOT NULL INDEX ix_UserId NONCLUSTERED HASH WITH (BUCKET_COUNT=1000000), CreatedDate DATETIME2 NOT NULL, TotalPrice MONEY ) WITH (MEMORY_OPTIMIZED=ON) GO To create a memory-optimized table, use the MEMORY_OPTIMIZED = ON clause INSERT dbo.ShoppingCart VALUES (8798, GETUTCDATE(), NULL) INSERT dbo.ShoppingCart VALUES (23, GETUTCDATE(), 45.4) INSERT dbo.ShoppingCart VALUES (80, GETUTCDATE(), NULL) INSERT dbo.ShoppingCart VALUES (342, GETUTCDATE(), 65.4) Insert records into the table
- 21. Native compilation enablesfaster data access and more efficient query execution than interpreted (traditional) Transact-SQL. CREATE PROCEDURE [dbo].[usp_1] WITH NATIVE_COMPILATION, SCHEMABINDING, EXECUTE AS OWNER AS BEGIN ATOMIC WITH ( TRANSACTION ISOLATION LEVEL = SNAPSHOT, LANGUAGE = N'us_english' ) SELECT c1, c2 FROM dbo.T1 END GO To create a Natively Compiled Stored Procedure, use the NATIVE_COMPILATION, SCHEMABINDING, and BEGIN ATOMIC clauses
- 24. Data stored incolumnar format
- 25. Steps to creatinga columnstore index (NCCI) Add a columnstore index to the table by executing the T-SQL SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM Sales.SalesOrderDetail GROUP BY ProductID ORDER BY ProductID Execute the query that should use the columnstore index to scan the table SELECT * FROM sys.indexes WHERE name = 'IX_SalesOrderDetail_ColumnStore' GO SELECT * FROM sys.dm_db_index_usage_stats WHERE database_id = DB_ID('AdventureWorks') AND object_id = OBJECT_ID('AdventureWorks.Sales.SalesOrderDetail'); Verify that the columnstore index was used by looking up its object_id and confirming that it appears in the usage stats for the table CREATE NONCLUSTERED COLUMNSTORE INDEX [IX_SalesOrderDetail_ColumnStore] ON Sales.SalesOrderDetail (UnitPrice, OrderQty, ProductID) GO
- 26. Industry-leading performance 0100101010110 In-Memory ColumnStore In-Memory OLTP ETL ! Real-timeoperational analytics
- 27. Insights to agents10x faster Fastest in-memory technology DATA MANAGEMENT // OPERATIONAL DATA CHALLENGE Real estate data business struggled to meet customer expectations for on-demand services and manage modern data types IMPACT Sped service updates from 24 hours to <3 hours Storage costs slashed by compressing data 64% In-memory Columnstore 1010010 1001010 1000010 Before ETL 24 hours 12.5 TB <3 hours 4.5 TB No ETL required 1 Data compression Fast reads
- 29. Improved efficiency withAdaptive Query Processing 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 Industry-leading performance 1 0 1 0 0 1 0 1 0 1 1 0
- 30. Better performance withAutomatic Plan Correction Industry-leading performance Querytimes Plan 1 Plan 2 Plan 3 Plan 2 Revert to previously effective plan
- 33. Layers of securityat disk, database, and app Customers Enhanced SQL Server Library CIPHERTEXT Customer Credit card # Exp. Denny Usher 4949-8003-8473-1930 5/17 Customer Credit card # Exp. Tim Irish 4839-2939-1919-3987 7/19 Denny Usher 4949-8003-8473-1930 5/17 Alicia Hodge 9000-4899-1600-1324 4/18 Credit card # 1x7fg655se2e 0x7ff654ae6d 0y8fj754ea2c SQL Column master key Column encryption key SELECT Customer, CreditCard, Exp FROM Customers WHERE CreditCard=@CC @CC='4949-8003-8473-1930' SELECT Customer, CreditCard, Exp FROM Customers WHERE CreditCard=@CC @CC= 0x7ff654ae6d Customer Credit card # Exp. Denny Usher 0x7ff654ae6d 5/17
- 34. Layers of securityat disk, database, and app Azure SQL Database SQL Server 2017 Table.CreditCardNo 4465-6571-7868-5796 4468-7746-3848-1978 4484-5434-6858-6550 Real-time data masking, partial masking
- 35. 700K+ customers protected Leastvulnerable SECURITY CHALLENGE Ecuadorian bank must provide unwavering security for customers while data volume and distribution channels rapidly expand IMPACT Internal data concealed with Dynamic Data Masking while testing applications Customer data protected from app to database with Always Encrypted and Row Level Security Layers of security Protection at rest and in motion 0 20 40 60 80 100 120 140 160 180 200 Vulnerabilities(2010-2016) Row Level Security Dynamic Data Masking Always Encrypted Auditing Data Encryption Control Access Monitoring & Insights +
- 37. SQL Server Hadoop Quote: ************************ ********************** ********************* ********************** *********************** $658.39 Name DOBState Denny Usher 11/13/58 WA Gina Burch 04/29/76 WA Azure Storage T-SQL query
- 38. Value through policycustomization Big Data insights with PolyBase DATA MANAGEMENT // BIG DATA PROCESSING CHALLENGE Car insurance company needed quick access to disparate data sources to create tailored policy rates IMPACT Customized policy rates based on driving history Increased revenues based on driver behavior Big Data with PolyBaseBefore T-SQL Car sensor data Insurance policy data PolyBase Customized policies incentivize safer driving $$$ $$$ $$$ Nearly uniform policy rates $$ $$$ $
- 39.
- 43. SQL Server Any Ror Python IDE Data Scientist Workstation Script Execution 1 2 Results3
- 45. SQL Server Application Call storedproc Execution 1 2 Results3
- 47. Streamline Productivity andSimplify Deployment Machine Learning in SQL Server Machine Learning Group customers, products & transactions into micro-segments of similar willingness-to-pay Pricing Optimization Expert Target Floor Segmentation Apply optimization algorithms to target the pricing envelope ‘sweet spot’ in every segment
Editor's Notes
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- #18 © 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.
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