An introduction to SQL Server In-Memory OLTP

Krishnakumar S

Traditional OLTP Architecture

•Disk is cheap – Data lives in disk•Memory is fast – Data modified in memory (Buffer) •Disk to Memory – I/O utilization•Query processing – CPU & Memory utilization•ACID – I/O utilization

Write ahead log, Transaction Log CPU utilization (Locking, latching)

•Memory to Disk – I/O utilization (Dirty pages to disk – Checkpoint)

High volume data modifications

Traditional OLTP Architecture

Contentions – “Competition for resources”

• Lock• Latch• Disk I/O• Allocation

AirlinesSensor dataApplication loggingConversations

etc…

CPU vs. Memory vs. I/O

Changing scenario…..

CPUStill hold Moor’s lawBecoming fasterMulticore

MemoryGetting cheaperBecoming fasterHold more data

I/OLagging far behindCPU and Memory

“The Free lunch is over…” – Herb Sutter, Dr. Dobb’s Journal, March 2005

“Tape is dead Disk is Tape Flash is Disk…” – Jim Gray, Microsoft, Dec 2006

SQL Server 2014 In-memory OLTP Engine

Goal was to achieve 100x speedup for certain TP operations

Hekaton a Greek word for 100

Microsoft spend nearly seven years on Hekaton

A major change in SQL Server engine from version 7.0

Four architectural pillars

SQL Server 2014 In-memory OLTP Engine

1. Optimize for main memory data access2. Accelerate business logic processing3. Provide frictionless scale-up4. Built-in to SQL Server

Optimize for main memory

SQL Server 2014 In-memory OLTP Engine

1. No 8KB pages for tables; only stream of bytes2. New lock and latch free in-memory indexes

1. Hash index (equality)2. Bw index (range) [“Buzz Word” index]

3. Using MVCC (Muti-Version Concurrency control); transactions see a snapshot of data

4. Log, data changes to transaction log5. No logging for index changes; recreates when

server restart

Accelerate business logic processing

SQL Server 2014 In-memory OLTP Engine

1. Table and index metadata compiled to DLL1. Table row format preserved in DLL2. Traditional query engine can be leveraged to access in-

memory tables 2. Stored Procedure compiled to DLL

Frictionless scale-up

SQL Server 2014 In-memory OLTP Engine

1. Multi-version optimistic concurrency control with ACID support

2. Lock free algorithms for in-memory engine3. No lock manager, latches or spin lock

Built-in to SQL Server

SQL Server 2014 In-memory OLTP Engine

1. In-memory engine is integrated in to SQL Server2. Both memory optimized and traditional tables can be

created for a database3. No lock manager, latches or spin lock

In-memory OLTP Engine – Behind the scenes

Rows

In-memory OLTP Engine – Behind the scenes

• Row Header– BeginTS

• TX TS that created the row– EndTS

• TX TS that deleted the row• Infinity

– StmtID• Statement that created the row

– IdxLinkCount• Number of indexes that reference this row

• Payload = actual data

TxID global DB counterReset on restartIncremented on TX start

TxTimeStamp global DB counter

Not reset on restartIncremented on TX end

Rows – Hash index

In-memory OLTP Engine – Behind the scenes

Hash index on Name

f(John)

Timestamps NameChain ptrs City

90, ∞ Susan Bogota

50, ∞ John Paris

Transaction 99:SELECT City WHERE Name = ‘John’Simple hash lookup returns direct pointer to ‘John’ row

100, ∞ John Prague

50, 100

Transaction 100: UPDATE City = ‘Prague’ where Name = ‘John’No locks of any kind, no interference with transaction 99

Rows – Non clustered index

In-memory OLTP Engine – Behind the scenes

Rows – Non clustered index

In-memory OLTP Engine – Behind the scenes

Addr.

0

1

2

3

4

…

Page Mapping Table

Page P

Δ Inserted Record 50

Δ Deleted record 48

Delta record

Delta record

PID

0

1

2

3

4

…

4.14.2

In-memory OLTP Engine – Summary

• T-SQL Features– 2 ways to access in-memory tables

• T-SQL interop (usually slower)• Natively compiled stored procedures (fast)

• T-SQL non supported features– TRUNCATE Table– MERGE (when in-memory table is the target)– Linked servers– Locking hints: TABLOCK, XLOCK, PAGLOCK, etc.

• NOLOCK is supported, but is quietly ignored

In-memory OLTP Engine – Summary

• Stored Procedures– TRY, CATCH, error functions, IF, WHILE– Memory-optimized table types and table variables– Math, Date, String functions – SCOPE_IDENTITY, ISNULL, NEWID,

NEWSEQUENTIALID

In-memory OLTP Engine – Summary

• Common Design patterns– In-Memory OLTP– High-performance OLTP– High data input or ”Shock Absorber”– Relational cache– Read-scale– ETL target and staging/temp tables

Demo

Questions?

Thank You