sql server 2016 part 2
TRANSCRIPT
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
sto
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en
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itectu
ral P
illa
rsD
rivers
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