SQL Server EvolutionSQL 2016 new innovations – Part 2

Lindsey AllenPrincipal Group Program Manager

Borko NovakovicProgram 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

Operational analytics• Minimize performance

impact running real-time analytics on transaction data

• Avoid data sprawl

In-memory OLTP for more applications

Query Store

Security

Always Encrypted

Row level security

Dynamic Data Masking

Availability

Enhanced AlwaysOn• 3 synchronous replicas

for auto failover across domains

• Round robin load balancing of replicas

• DTC for transactional integrity across database instances with AlwaysOn

Enhanced online operations

Scalability

Support for Windows Server 2016

12TB 16 Sockets

4

In-memory engine

Over 100x query speed and significant data compression withIn-Memory ColumnStore

Up to 30x faster transaction processing with In-Memory OLTP

Faster QueriesIN-MEMORY DW

Faster TransactionsIN-MEMORY OLTP

In-memory OLTP

SQL Server Integration

• Same manageability, administration & development experience

• Integrated queries & transactions

• Integrated HA and backup/restore

Main-Memory Optimized

• 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• T-SQL compiled to

machine code via C code generator

• Invoking a procedure is just a DLL entry-point

• Aggressive optimizations @ compile-time

Steadily declining memory price,

NVRAM

Many-core processors

Stalling CPU clock rate

TCO

Hardware trends Business

Hybrid engine and integrated

experience

High performance

data operations

Efficient business-logic

processingCu

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High Concurrency

• Multi-version optimistic concurrency control with full ACID support

• Core engine uses lock-free algorithms

• No lock manager, latches or spinlocks

Frictionless scale-up

…

C1 C2

C3 C5C4 Benefits:• Improved

compression:Data from same domain compress better

• Reduced I/O:Fetch only columns needed

• Improved Performance:More data fits in memory

Data stored as rows

Columnstore (index)

Data stored as columns

In-memory column store 2016

• 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

Deeper insights across data & Hyperscale CloudAccess any data

PolyBase

Native JSON

Temporal database support

Power Query for analytics and reporting

Built-in Advanced Analytics

Business insights through rich visualizations on any mobile device

Scale and manage

Enterprise-grade Analysis Services

New single SSDT in Visual Studio 2015

Enhanced MDS

Enhanced SSIS

Enhanced Reporting Services

Hybrid solutionsStretch tables into Azure

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

When performance is not good… Database is not working

Web site is down

Impossible to predict / root cause

Temporary perf.

issues

Regression caused by new bits

DB upgraded

Plan choice change can cause these problems

With Query Store you CAN…Find and fix plan regressions

Identify top resource consumers

De-risk SQL Server upgradeDeeply analyze workload patterns

Short-term/tactical

Long-term/strategic

Why temporal?Real data sources are dynamic Historical data may be critical to business successTraditional databases fail to provide required insights

Workarounds are…Complex, expensive, limited, inflexible, inefficient

SQL Server 2016 makes life easyNo change in programming modelNew Insights

Facts:1. History is much bigger

than actual data2. Retained between 3 and

10 years3. “Warm”: up to a few

weeks/months4. “Cold”: rarely queried

Solution:history as a

stretch table:

PeriodEnd < “Now - 6 months”

SELECT * FROM Department FOR SYSTEM_TIME AS OF '2010.01.01'

Azure SQL Database

Data ScientistInteract directly with data

Built-in to SQL Server

Data Developer/DBAManage data and analytics together

Built-in advanced analyticsIn-database analytics

Example Solutions• Fraud detection

• Sales forecasting

• Warehouse efficiency

• Predictive maintenance

Relational Data

Analytic Library

T-SQL Interface

Extensibility

?R

R Integration

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Microsoft AzureMachine Learning Marketplace

New R scripts

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AML Gallery

ML Studio

SSMS / R

SSRS / CR

Excel / PV

Power BI.com

Fisher’s Iris flower dataseta typical test case in machine learning• Iris species can be identified based on their sepal and

petal length/width• Plotting these attributes shows well differentiated

classes with few overlaps

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