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PASS Summit - SQL Server 2017 Deep Dive

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Deep dive into SQL Server 2017 covering SQL Server on Linux, containers, HA improvements, SQL graph, machine learning, python, adaptive query processing, and much much more.

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PASS Summit - SQL Server 2017 Deep Dive

  1. 1. Travis Wright Shreya Verma Nellie Gustafsson Tobias Ternstrom Program Managers Database Systems, Microsoft SQL Server 2017 Deep Dive
  2. 2. End-to-end mobile BI on any 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 S Q L S E R V E R 2 0 1 7 I N D U S T R Y - L E A D I N G P E R F O R M A N C E A N D S E C U R I T Y N O W O N L I N U X A N D D O C K E R Private cloud Public cloud + T-SQL In-memory across all workloads 1/10th the cost of Oracle
  3. 3. F L E X I B L E , R E L I A B L E D ATA M A N A G E M E N T SQL Server on the platform of your choice Support for RedHat Enterprise Linux (RHEL), Ubuntu, and SUSE Enterprise Linux (SLES) Linux and Windows Docker containers Windows Server / Windows 10 Choice of platform and language
  4. 4. Prioritization principles Performance and scale Cross-OS compatibility Same app code runs across platforms Native user experience On Linux and macOS (server & tools)
  5. 5. System Architecture SQL Platform Abstraction Layer (SQLPAL) DB Engine IS AS RS Windows Linux Windows Host Ext. Linux Host Extension SQL Platform Abstraction Layer (SQLPAL) Win32-like APIs Host Extension mapping to OS system calls (IO, Memory, CPU scheduling) SQL OS API SQL OS v2 Everything else System Resource & Latency Sensitive Code Paths
  6. 6. Windows Linux GA Developer, Express, Web, Standard, Enterprise   Database Engine, Integration Services   R Services, Analysis Services, Reporting Services, MDS, DQS  Maximum number of cores Unlimited Unlimited Maximum memory utilized per instance 12 TB 12 TB Maximum database size 524 PB 524 TB Basic OLTP (Basic In-Memory OLTP, Basic operational analytics)   Advanced OLTP (Advanced In-Memory OLTP, Advanced operational analytics)   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 PolyBase2  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)   Windows Filesystem Integration - FileTable  BI & Advanced Analytics Basic Corporate Business Intelligence (Multi-dimensional models, Basic tabular model)  Basic “R” integration (Connectivity to R Open, Limited parallelism for ScaleR)  Advanced “R” integration (Full parallelism for ScaleR)  Hybrid cloud Stretch Database  What’s in SQL Server on Linux?
  7. 7. Programming Features • Support for RHEL, Ubuntu, Docker • Package based installs, Docker image • Support for Open Shift, Docker Swarm • Failover Clustering through Pacemaker • Backup/Restore • SSMS on Windows connected to Linux • Command line tools: sqlcmd, bcp • SQL Server Agent • Log Shipping • Transparent Data Encryption • SCOM Management Pack • DMVs • Full Text Search Operations Features • All major language driver compatibility • In memory OLTP and ColumnStore • Compression • Always Encrypted, Row Level Security, and Data Masking • Service Broker • Change Data Capture • Partitioning • Auditing • CLR • JSON, XML • Third party tools Features working in 2017 GA …and more!
  8. 8. Demo Getting Started with SQL Server on Linux and Containers
  9. 9. M I S S I O N C R I T I C A L AVA I L A B I L I T Y O N A N Y P L AT F O R M Always On cross-platform capabilities HA and DR for Linux and Windows Clusterless Availability Groups Ultimate HA with OS-level redundancy and low-downtime migration Load balancing of readable secondaries
  10. 10. Other Availability Groups enhancements
  11. 11. Demo Hybrid Always On Availability Groups
  12. 12. Machine Learning & Graph Shreya Verma, Program Manager, Microsoft Nellie Gustafsson, Program Manager, Microsoft
  13. 13. SQL Server Machine Learning Services VS Intelligence Database Application Database Application Intelligence Intelligent Database + AppRegular Database + App
  14. 14. Hello World
  15. 15. In-database Machine Learning Develop Train Deploy Consume Develop, explore and experiment in your favorite IDE Train models with sp_execute_external_ script and save the models in database Deploy your ML scripts with sp_execute_external_ script and predict using the models Make your app/reports intelligent by consuming predictions
  16. 16. SQL Server Machine Learning Services SQL Server 2016 • Extensibility Framework • R Support (3.2.2) • Microsoft R Server SQL Server 2017 • Python Support (3.5.2) • R Support (3.3.3) • Native Scoring using PREDICT • In-database Package Management Azure SQL DB • Native scoring using PREDICT (GA) • R Support (3.3.3) • Base R packages • RevoScaleR package • Train & Score in Memory • Trivial parallelism & Streaming support
  17. 17. Text Sentiment Analysis with SQL Server ML Services Use pretrained model by calling Microsoftml get_ sentiment() from Python or R Database Application Intelligence STORED PROCEDURE sp_execute_external_script PRE-TRAINED MODEL Get predictions by calling stored procedure
  18. 18. Sentiment Analysis Demo
  19. 19. Graph Extensions in SQL Server 2017
  20. 20. What is a Graph Database? • Edges or relationships are first class entities in a Graph Database and can have attributes or properties associated with them. • A single edge can flexibly connect multiple nodes in a Graph Database. • You can express pattern matching and multi-hop navigation queries easily. • Supports OLTP and OLAP (analytics) just like SQL databases.
  21. 21. SQL Server 2017 – Graph Extensions • Graph – collection of node and edge tables • DDL Extensions – create node/edge tables • Properties associated with Node and Edge tables • All type of indexes are supported on node and edge tables. • Query Language Extensions – New built-in: MATCH, to • support pattern matching and traversals • Tooling and Eco-system
  22. 22. DDL Extensions CREATE NODE CREATE TABLE [dbo].[Attendee]( [Attendee_Id] [uniqueidentifier] PRIMARY KEY, [Ateendee_FName] varchar(100), [Attendee_LName] varchar(100) ) AS NODE GO SELECT TOP 10 * FROM Attendee;
  23. 23. DDL Extensions CREATE TABLE attends (Rating int) AS EDGE; CREATE TABLE [from] AS EDGE; • CREATE EDGE SELECT TOP 10 * FROM [from];
  24. 24. Query Language Extensions • Multi-hop navigation and join-free pattern matching using MATCH predicate • ASCII-art syntax to facilitate graph traversal SELECT Attendee.Attendee_Name AS ‘AttendeeName’, Session.Session_ID AS ‘SessionName’ FROM Attends, Attendee, Session WHERE MATCH (Attendee-(attends)->Session) AND Session.session_name = 'Building a Graph Database Application with SQL Server 2017 and Azure SQL Database'
  25. 25. Session Recommendation Scenario Speaker Attendee Location Follows From Industry From Session Presents Attends Track Topic From From
  26. 26. -- Find the other sessions that these other users attended other_sessions AS ( SELECT at.name AS attendee_name, s.name AS session_name, COUNT(*) AS other_sessions_attended FROM Conference.Attendee_1 AS at JOIN Conference.SessionAttendee AS sa ON sa.AttendeeID = at.AttendeeID JOIN Conference.Sessions AS s ON s.SessionID = sa.SessionID JOIN OTHER_USR AS ou ON ou.attendeeid = at.attendeeid WHERE s.sessionid <> 101 GROUP BY at.name, s.name ) -- Recommend to the current user the top sessions from the -- list of sessions attended by other users SELECT TOP 10 s.name, COUNT(other_sessions_attended) FROM OTHER_SESSIONS AS os JOIN sessions AS s on s.name = OS.session_name GROUP BY s.name ORDER BY COUNT(other_sessions_attended) DESC; WITH Current_Usr AS ( SELECT AttendeeID = 6 ,SessionID = 101 -- Graph session ,AttendeeCount = 1 ) , -- Identify the other users who also attended the -- graph session Other_Usr AS ( SELECT at.attendeeID, s.sessionid, COUNT(*) AS Attended_by_others FROM Conference.Attendee_1 AS at JOIN Conference.SessionAttendee AS sa ON sa.AttendeeID = at.AttendeeID JOIN Conference.Sessions AS s ON s.SessionID = sa.SessionID JOIN Current_Usr AS cu ON cu.SessionID = sa.SessionID WHERE cu.AttendeeID <> sa.AttendeeID GROUP BY s.sessionid, at.attendeeid ) , Session Recommendations (“Before”)
  27. 27. SELECT TOP 10 RecommendedSessions.SessionName ,COUNT(*) FROM Sessions ,Attendee ,Attended AS AttendedThis ,Attended AS AttendedOther ,Sessions AS RecommendedSessions WHERE Session.Session_ID = 101 AND MATCH(RecommendedSessions<-(AttendedOther)-Attendee-(AttendedThis)->Sessions) AND (Sessions.SessionName <> RecommendedSessions.SessionName) AND Attendee.attendeeID <> 6 GROUP BY RecommendedSessions.SessionName ORDER BY COUNT(*) DESC; GO Session Recommendations with SQL Graph (“After”)
  28. 28. Fraud detection using SQL Graph and Machine Learning Services with Python
  29. 29. Machine Learning Modeling Process + Graph Feature Engineering Model Training Model Evaluation Modeling Model Deployment Graph Features
  30. 30. Fraud Detection Transaction TxID Payment Instrument PayInstID Device DeviceID Account AccountID has_account has_paymenthas_device
  31. 31. Fraud Rings Transaction TxID Payment Instrument PayInstID Device DeviceID Account AccountID has_account has_paymenthas_device Transaction TxID
  32. 32. Fraud Rings Transaction TxID Payment Instrument PayInstID Account AccountID has_account has_payment Transaction TxID Device DeviceID has_device
  33. 33. Fraud Rings Transaction TxID Device DeviceID Account AccountID has_account has_device Transaction TxID Payment Instrument PayInstID has_payment
  34. 34. Fraud Detection Scenario Problem Detect potentially fraudulent transactions Solution Train a model to learn patterns of fraudulent transactions Train Model Historically labelled transactions, risk factor for IP address geography etc. Deploy Model Use model to predict fraudulent transactions (get probability of fraud) Rings Size, Avg. Chargeback Amount Proportion of Fraud Graph Features
  35. 35. Fraud Detection Demo
  36. 36. Conclusion SQL Server ML Services + SQL Server Graph =
  37. 37. Learn more! • Don’t miss: Building a Graph Database Application with SQL Server 2017 and Azure SQL Database • When: Friday, 3:15PM • Sentiment analysis blog post and scripts on GitHub: • Blog: https://blogs.msdn.microsoft.com/sqlserverstorageengine • GitHub: sql-server-samples/samples/features/machine-learning- services/python/sentiment-analysis/ • Getting started tutorialsmachine learning in SQL Server: AKA.MS/MLSQLDEV
  38. 38. In Memory Improvements in 2017 • CASE statements are now supported for natively compiled T-SQL modules • The limitation of 8 indexes on memory-optimized tables has been removed • All JSON functions and clauses are now supported in natively compiled T-SQL modules and constraints in memory-optimized tables. Indexes on computed columns allow indexing JSON data. • Computed columns are now supported for memory-optimized tables • TOP (N) WITH TIES is now supported in natively compiled T-SQL modules • The CROSS APPLY operator is now supported in natively compiled T-SQL modules. • Built-in functions TRIM, TRANSLATE, and CONCAT_WS are now supported for natively compiled T-SQL modules and for constraints in memory- optimized tables • ALTER TABLE against memory-optimized tables is now substantially faster in most cases • Transaction log redo for memory-optimized tables is now done in parallel. This improves recovery times and significantly increases the sustained throughput of AlwaysOn availability group configuration. • Significant perf improvements for recovery of bwtree (i.e., NONCLUSTERED) indexes on memory-optimized tables. • sp_rename is now supported for memory-optimized tables and natively compiled T-SQL modules • sp_spaceused now reflects disk space utilization of In-Memory OLTP checkpoint files • In-Memory OLTP checkpoint files can now be stored on Azure Storage • Snapshot backup is supported for In-Memory OLTP checkpoint files in Azure Storage
  39. 39. Other improvements • • • • •
  40. 40. Tiger Team Improvements • SELECT INTO … ON FileGroup Loading data into staging tables in non-default file groups • tempdb setup improvements - 1 GB -> 256 GB • Support MAXDOP option for statistics create/update • Improved backup performance –> • + 10s of other improvements
  41. 41. Adaptability in SQL Server Adaptive Query Processing Interleaved Execution Batch Mode Memory Grant Feedback Batch Mode Adaptive Joins ?...
  42. 42. Interleaved Execution for MSTVFs Problem: Multi-statement table valued functions (MSTVFs) are treated as a black box by QP and we use a fixed optimization guess Interleaved Execution will materialize and use row counts for MSTVFs Downstream operations will benefit from the corrected MSTVF cardinality estimate
  43. 43. Batch Mode Memory Grant Feedback (MGF) Problem: Queries may spill to disk or take too much memory based on poor cardinality estimates MGF will adjust memory grants based on execution feedback MGF will remove spills and improve concurrency for repeating queries
  44. 44. Batch Mode Adaptive Joins (AJ) Problem: If cardinality estimates are skewed, we may choose an inappropriate join algorithm AJ will defer the choice of hash join or nested loop until after the first join input has been scanned AJ uses nested loop for small inputs, hash joins for large inputs
  45. 45. Announcing * As of 11/2/2017. The results may be viewed at: HPE Proliant DL580 Gen 10: http://www.tpc.org/3329; Lenovo ThinkSystem SR950: http://www.tpc.org/3328; Cisco USC C460 M4 Server: http://www.tpc.org/3326; Lenovo System x3850 X6: http://www.tpc.org/3325; HPE Proliant DL380 Gen 10: http://www.tpc.org/3330; HPE ProLiant DL580 Gen 9: http://www.tpc.org/3323; Cisco USC C460 M4: http://www.tpc.org/3323;
  46. 46. Thank You Hit us up! tobiast@microsoft.com@tobiassql twright@microsoft.com@radtravis
  47. 47. Session evaluations Download the GuideBook App and search: PASS Summit 2017 Follow the QR code link displayed on session signage throughout the conference venue and in the program guide Your feedback is important and valuable. Go to passSummit.com Submit by 5pm Friday, November 10th to win prizes. 3 Ways to Access:

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