2. Meet Kevin Liu Principal Lead Program Manager Kevin Liu has been with Microsoft and the SQL Server engine team for 7 years, working on key projects like AlwaysOn and has been leading program management for the In-Memory OLTP project since its transition into the product team from incubation. Prior to Microsoft, Kevin worked in enterprise software consulting (Accenture and etc) and holds a Ph.D on computational neural networks.

3. Kevin Liu| Principal Lead Program Manager Kevin.Liu@Microsoft.com

4. SQL Server 2014 Investments In-Memory Technologies Enhanced High Availability New Hybrid Scenarios In-Memory OLTP 5-20X performance gain for OLTP integrated into SQL Server In-Memory DW 5-25X performance gain and high data compression Updatable and clustered SSD Bufferpool Extension 4-10X of RAM and up to 3X performance gain transparently for apps Always On Enhancements Increased availability and improved manageability of active secondaries Online Database Operations Increased availability for index/partition maintenance Backup to Azure Easy to implement and cost effective Disaster Recovery solution to Azure Storage HA to Azure VM Easy to implement and cost effective high availability solution with Windows Azure VM Deploy to Azure Deployment wizard to migrate database Better together with Windows Server WS2012 ReFS support Online resizing VHDx Hyper-V replica Windows “Blue” support Extending Power View Enable Power View on existing analytic models and support new multi- dimensional models. Other investments

5. In-memory Technologies In-Memory Technologies In-Memory OLTP 5-20X performance gain for OLTP integrated into SQL Server In-Memory DW 5-25X performance gain and high data compression Updatable and clustered SSD Bufferpool Extension 4-10X of RAM and up to 3X performance gain transparently for apps

6. Why In-memory OLTP (Hekaton) 6

7. Decreasing RAM cost Moore’s Law on total CPU processing power holds but in parallel processing… CPU clock rate stalled…

8. 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 High Concurrency Multi-version optimistic concurrency control with full ACID support Core engine uses lock- free algorithms No lock manager, latches or spinlocks T-SQL Compiled to Machine Code T-SQL compiled to machine code via C code generator and VC Invoking a procedure is just a DLL entry-point Aggressive optimizations @ compile-time Steadily declining memory price, NVRAM Many-core processors Stalling CPU clock rateTCO Hardware trends Business In-memory OLTP Architecture Pillars 8 Hybrid engine and integrated experience High performance data operations Frictionless scale-up Efficient, business- logic processing Customer Benefits Hekaton Tech Pillars Drivers

9. 9 Demo 1

10. Memory-optimized Table Filegroup Data Filegroup SQL Server.exe In-memory OLTP Engine: Memory_optimized Tables & Indexes TDS Handler and Session Management In-memory OLTP Integration and Application Migration 10 Natively Compiled SPs and Schema Buffer Pool for Tables & Indexes Proc/Plan cache for ad-hoc T- SQL and SPs Client App Transaction Log Query Interop Non-durable Table T1 T4 T3 T2 T1 T4 T3 T2 T1 T4 T3 T2 T1 T4 T3 T2 Tables Indexes Interpreter for TSQL, query plans, expressions T1 T4 T3 T2 T1 T4 T3 T2 Checkpoint & Recovery Access Methods Parser, Catalog, Algebrizer, Optimizer In-Memory OLTP Compiler In-Memory OLTP Component Key Existing SQL Component Generated.dll

11. Memory-optimized Table Filegroup Data Filegroup SQL Server.exe In-Memory OLTP Engine for Memory_optimized Tables & Indexes TDS Handler and Session Management Performance Gains Natively Compiled SPs and Schema Buffer Pool for Tables & Indexes Proc/Plan cache for ad- hoc T-SQL and SPs Client App Transaction Log Query Interop Interpreter for TSQL, query plans, expressions Access Methods Parser, Catalog, Algebrizer, Optimizer Hekaton Compiler 10-30x more efficient Reduced log bandwidth & contention. Log latency remains Checkpoints are background sequential IO No improvements in communication stack, parameter passing, result set generation Hekaton Component Key Existing SQL Component Generated.dll

12. SQL Server row-store and column-store scenarios Row-store for OLTP: mainly for operational transaction with minimum reporting and shorter period of time Column-store for DW: mainly for reporting of transaction history over a longer period of time ConsiderationsIM Row storeIM Updatable Columnstore Data size and currency Data sizeDesigned to address bottlenecks in hot data (For V1, < 256GB) Designed for cold and archival data (>256G) Read patterns Point select and ad hoc query for operational report Ideal – key design points for non-blocking high performance data access Not ideal – minimum scan set is 1M row + delta row store Large scan set with aggregates Not idealIdeal – key design points Star schema and related DW type of complex joins Not idealIdeal – key design points Write patterns Heavy updates and deletes Ideal – key design points for contention free data operations Functional but not optimized – change happens to on-disk row store and gets merged into column store in batches Heavy ETL and data ingestion Ideal – key design pointsFunctional but not optimized – same as above Relational cache scenario Ideal (with NDT)Not ideal

13. 13 Demo 2

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