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SAP HANA DATABASEMihnea Andrei SAP Products & Innovation HANA Platform July 8, 2014 Public

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Agenda

SAP

SAP HANA DB Background

Architecture

Column Store & Compression

Snapshot Isolation

Outlook

SAP

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Who was SAP (before HANA)?

Sales Order Management

Production Planning Talent Management

Financial/Mgmt Accounting

Business Intelligence

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74% of the world’s transaction revenue touches an SAP system.

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SAPBusiness Applications – Database & Technology – Analytics – Cloud – Mobile

Annual revenue (IFRS) of € 16,82 billion

More than 253,500 customers in 188 countries

More than 66,500 employees – and locations in more than 130 countries

A 42-year history of innovation and growth as a true industry leader

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Products & Innovation HANA PlatformCalifornia Campus – Worldwide

SAP HANA DB BackgroundWhy did we build HANA?

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How Did the SAP Use Database Before HANA?

See “The SAP Transaction Model:  Know Your Applications”, SIGMOD 2008 Industrial Talk Database was mainly a dumb store …

– Retrieve/Store data (Open SQL, no stored procedures)– Transaction commit, with locks held very briefly– Operational utilities

… because SAP kept the following in the application server:– Application logic– Business object-level locks– Queued updates– Data buffers– Indexes

With the HANA platform, computation-intensive data-centric operations are moved to the Database

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DRAM Price/GB

Year Price/GB2013 $5.50 2010 $12.37 2005 $189 2000 $1,107 1995 $30,875 1990 $103,880 1985 $859,375 1980 $6,328,125

Source: http://www.statisticbrain.com/average-historic-price-of-ram/

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In-Memory Computing

Yes, DRAM is 125,000 times faster than disk, but DRAM access is still 10-80 times slower than on-chip

caches

80 NS, TBs

CPU

Core Core

L1 Cache L1 Cache

L2 Cache L2 Cache

L3 Cache

Main Memory

Disk

1 NS, 64K/core

3 NS, 256k/core

8 NS, >2M shared

SSD: 100K NSHD: 10M NS

Using Intel Ivy Bridge for approximate values.Actual numbers depends on specific hardware.

4-12 cores/CPU

4-8 sockets

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Enterprise Workloads are Read Dominated

Workload in Enterprise Applications consists of: Mainly read queries (OLTP 83%, OLAP 94%) Many queries access large sets of data

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Simplify Technology Stack with the SAP HANA Platform

SAP HANA Platform

Applications Analytics

Insight to ActionContextual. Real-time. Closed-loop.

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SAP HANA Database Background

BWA / BIA

Trex

PTime

MaxDB

201120102000-2009Ancient times

Enterprise Search

NewDB / HANA

BW on HANA

Suite on HANA

2012

HANA Platform

now

Sybase IQ/ASE/SA/RS/etc.

SAP HANA DB Architecture

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SAP HANA DB Processes

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Business Applications

Connection and Session Management

Authori-zation

Manager

Metadata Manager

Trans- action

Manager

SQL SQL Script MDX …

Optimizer and Plan Generator

Calculation Engine

Execution Engine

In-Memory Processing Engines

Column Engine Row Engine Text Engine

Persistency Logging and Recovery Data Storage

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Distributed Share-Nothing In-Memory Computing

NewDB Database Server

NewDB Database Server

Execution Layer

In-Memory Stores

Persistence Layer

R

R

NewDB Database Server

Execution Layer

In-Memory Stores

Persistence Layer

R

R

Execution Layer

In-Memory Stores

Persistence Layer

R

R

Column Store & Compression

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Tuple 2

Tuple 1

Tuple 3

Tuple n

SAP HANA Technology Hybrid Data Storage

SAP HANA Column Store stores tables by column

SAP HANA Row Store stores tables by row

Tuple 1

Tuple 2

Tuple 3

Tuple n

Att1 Att2 Att3 Att4 Att5 Att2 Att4 Att5Att3Att1

Search and calculation on values of a few columns Big number of columns Big number of rows and columnar operations

aggregate, scan, etc. High compression rates possible

Most columns contain only few distinct values

Application often processes single records at once many selects and /or updates of single

records Application typically accesses the complete record Columns contain mainly distinct values Aggregations and fast searching not required Small number of rows (e.g. configuration tables)

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SAP HANA TechnologyDictionary Compression & N-bit Compression

Company[CHAR50]

Region[CHAR30]

Group[CHAR5]

INTEL USA A

Siemens Europe B

Siemens Europe C

SAP Europe A

SAP Europe A

IBM USA A

0

1

1

2

2

3

0 INTEL1 Siemens2 SAP3 IBM

1

0

0

0

0

1

0 Europe1 USA

0

1

2

0

0

0

0 A1 B2 C

HANA Column Store Classical Row Store

Dictionary for attribute/column „Group“

Index VectorStored in one memory chunk=> data locality for fast scans

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SAP HANA TechnologyCompression with run length encoding

Company[CHAR50]

Region[CHAR30]

Group[CHAR5]

INTEL USA A

Siemens Europe B

Siemens Europe C

SAP Europe A

SAP Europe A

IBM USA A

0

1

1

2

2

3

0 INTEL1 Siemens2 SAP3 IBM

1

0

0

0

0

1

0 Europe1 USA

0

1

2

0

0

0

0 A1 B2 C

HANA Column Store:Dictionary compressed

Classical Row StoreDifficult to compress

1 x „0“

2 x „1“

2 x „2“

0 INTEL1 Siemens2 SAP3 IBM

0 Europe1 USA

0 A1 B2 C

HANA Column Store:Run length compressed*

1 x „1“

4 x „0“

1 x „1“

1 x „3“

1 x „0“

1 x „1“

1 x „2“

3 x „0“

* Note that there is a variety of compression methods and algorithms like run-length compression

Snapshot Isolation

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Initial Design – set oriented, optimized for OLAP

timeoldest reader

111011……………111101

tx1 commit

1111

1100010……………01000000010

base

list

of

row

s vi

sibl

e to

all

tx2 access

inse

rted

dele

ted

011001……………101101111011

tx3 commit

111

000001……………100000000001000

tx2 begin tx4 begin & access

011000……………

001101111100111

DATA-D……………DATA-DATA-DATA-

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New Design – OLTP friendly

Problems to solve Memory overhead

– Valid from/to for every row?

Tx identity: TID vs. CID– If TID: visibility rules, TCB memory overhead– If CID: DML time ID, atomic commit, post-commit

L2/3 cache friendly – Stay local, avoid dereferencing pointers

OLAP performance

DATA

New rows

tx2: insert n rows

tx2…

New row tx1tx1: insert 1 row

validfrom

tx3: delete where …

validto

tx3

tx3

txn: reader

OutlookWhere is HANA going next?

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Continuing Challenges of Emerging Hardware

Challenge 1: Parallelism: Take advantage of tens, hundreds, thousands of cores Challenge 2: Large memories & data locality/NUMA

– Yes, DRAM is 125,000 times faster than disk…– But DRAM access is still 10-80 times slower than on-chip caches

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HANA Platform On-Going Architectural Evolution

Data models Flexible schemas, graph functionality, geospatial, time series, historical data,

Big Data, external libraries

Resource and workload management Memory, threads, scheduling, admission control, service level management, data aging

Application services XS Engine, CDS and River

Continuing performance improvements Hardware advances, NUMA, improved modularization and architecture

Cloud and multi-tenancy

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Co-innovating the Next Big Wave in Hardware Evolution

Multi-Core and Large “Memory” Footprints

Storage Class Memories / Non-Volatile Memory Leverage as DRAM and/or as persistent storage

On-Board DIMMs Very high density, byte-addressable DRAM like (< 3X) latency and bandwidth; similar endurance Compete with disk on cost/bit by 2020

Extreme Speed Network Fabric/Interconnects Inter-socket NUMA gets worse while inter-host NUMA gets better Inter-socket and Inter-host latencies converge

Exploiting Dark Silicon for Database Hardware Acceleration Also exploit GPUs for specific use cases, such as regression analysis

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Thank you!

http://www.careersatsap.com/

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https://www.saphana.com

http://www.sap.com/pc/tech/in-memory-computing-hana.html

Contact information:

Arne Schwarz, arne.schwarzy@sap.comMihnea Andrei, mihnea.andrei@sap.com Richard Pledereder, richard.pledereder@sap.com

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