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SQL Server Evolution SQL 2016 new innovations – Part 2 Lindsey Allen Principal Group Program Manager Borko Novakovic Program Manager What’s in this session • SQL 2016 highlights • Scaling up to new heights – 16 sockets • In-memory Engine Advances • Query flight recorder - Query Store • Time travel and auditing with Temporal database • Bring Advanced Analytics to your data • Call to action Mission critical platform Performance Security Availability Scalability Operational analytics Always Encrypted Enhanced AlwaysOn Row level security • 3 synchronous replicas for auto failover across domains Support for Windows Server 2016 • Minimize performance impact running real-time analytics on transaction data • Avoid data sprawl In-memory OLTP for more applications Query Store Dynamic Data Masking • Round robin load balancing of replicas • DTC for transactional integrity across database instances with AlwaysOn Enhanced online operations 12TB 16 Sockets Demo SQL scalability on HP superdomeX Lindsey Allen In-memory engine Faster Transactions IN-MEMORY OLTP Faster Queries IN-MEMORY DW Up to 30x faster transaction processing with In-Memory OLTP Over 100x query speed and significant data compression with In-Memory ColumnStore 6 Drivers Architectural Pillars Customer Benefits In-memory OLTP High performance data operations Efficient business-logic processing Frictionless scale-up Hybrid engine and integrated experience Main-Memory Optimized T-SQL Compiled to Machine Code High Concurrency SQL Server Integration • Optimized for in-memory data • Indexes (hash and range) exist only in memory • No buffer pool, B-trees • Stream-based storage • T-SQL compiled to machine code via C code generator • Invoking a procedure is just a DLL entry-point • Aggressive optimizations @ compile-time • Multi-version optimistic concurrency control with full ACID support • Core engine uses lock-free algorithms • No lock manager, latches or spinlocks Business Hardware trends Steadily declining memory price, NVRAM Stalling CPU clock rate • Same manageability, administration & development experience • Integrated queries & transactions • Integrated HA and backup/restore Many-core processors TCO Columnstore (index) Data stored as rows Data stored as columns C1 C2 C3 C4 C5 Benefits: • Improved compression: Data from same domain compress better … • Reduced I/O: Fetch only columns needed • Improved Performance: More data fits in memory • Updatable NCCI • In-Memory OLTP + Column-store • Faster batch mode scans using CPU vector instructions • Dynamic Aggregate pushdown • PK/FK enforcement • Offload Reporting to AlwaysOn Secondary Replica Demo Column store performance improvement Lindsey Allen LINEITEM: 600M rows ORDERS: 150M rows CUSTOMER: 15M rows CUSTOMER_ZIP: 315M rows Deeper insights across data & Hyperscale Cloud Access any data PolyBase Native JSON Temporal database support Scale and manage Hybrid solutions Enterprise-grade Analysis Services Stretch tables into Azure New single SSDT in Visual Studio 2015 Power Query for analytics and reporting Enhanced MDS Built-in Advanced Analytics Enhanced Reporting Services Business insights through rich visualizations on any mobile device Enhanced SSIS Power BI with on-premises data Hybrid scenarios with SSIS • Azure Data Factory integration with SSIS • Package Lineage and impact analysis • Connect SSIS to cloud data sources Enhanced backup to Azure • X faster restore and 50% reduction in storage Easy migration of on-premises SQL Server Query Store Flight data recorder for your database When performance is not good… Web site is down Database is not working Temporary perf. issues Impossible to predict / root cause DB upgraded Regression caused by new bits With Query Store you CAN… Find and fix plan regressions Identify top resource consumers De-risk SQL Server upgrade Deeply analyze workload patterns Demo Fixing performance regression using Query Store Borko Novakovic Temporal Database Why temporal? • Real data sources are dynamic • Historical data may be critical to business success • Traditional databases fail to provide required insights • Workarounds are… • Complex, expensive, limited, inflexible, inefficient • SQL Server 2016 makes life easy • No change in programming model • New Insights SELECT * FROM Department FOR SYSTEM_TIME AS OF '2010.01.01' Azure SQL Database Demo Auditing with Temporal database Borko Novakovic Built-in advanced analytics In-database analytics Example Solutions Extensibility • Fraud detection R Integration • Sales forecasting R • Warehouse efficiency ? New R scripts • Predictive maintenance 010010 100100 010101 Analytic Library 010010 100100 010101 Data Scientist Interact directly with data 010010 100100 010101 T-SQL Interface Relational Data Built-in to SQL Server Data Developer/DBA Manage data and analytics together 010010 100100 010101 010010 100100 010101 Microsoft Azure Machine Learning Marketplace Demo Build-in Advanced analytics Lindsey Allen Fisher’s Iris flower dataset machine learning AML Gallery ML Studio SSMS / R SSRS / CR Excel / PV Power BI.com CTA • [TAE8DD] Azure SQL Data Warehouse Overview • [T55A62] Microsoft Azure SQL Database: Overview • [TC530B] Stretching on-prem databases to cloud • [T4D1C9]In-Memory Technologies Overview • Polybase in SQL Server Futures - A sneak Peek • [TB63B1] In-Memory OLTP Futures • [T9F2FD] Overview of Microsoft SQL Server Security Futures • Temporal, Query Store and JSON Support in SQL Server Futures • [TCFAC2] ColumnStore Index: Microsoft SQL Server 2014 and Beyond • [TD4D79] Best Practices for Designing Your Cloud-Based, Data-Tier Strategy • [TBD345] APS and Data Warehousing in the cloud - Technical drilldown • [TB01EC] Elastic Scale for Microsoft Azure SQL Database http://myignite.microsoft.com