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Use this title slide only with an image SAP HANA DATABASE Mihnea Andrei SAP Products & Innovation HANA PlatformJuly 8, 2014 Public.

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Präsentation zum Thema: "Use this title slide only with an image SAP HANA DATABASE Mihnea Andrei SAP Products & Innovation HANA PlatformJuly 8, 2014 Public."—  Präsentation transkript:

1 Use this title slide only with an image SAP HANA DATABASE Mihnea Andrei SAP Products & Innovation HANA PlatformJuly 8, 2014 Public

2 ©2014 SAP AG or an SAP affiliate company. All rights reserved.2 Public Agenda SAP SAP HANA DB Background Architecture Column Store & Compression Snapshot Isolation Outlook

3 SAP

4 ©2014 SAP AG or an SAP affiliate company. All rights reserved.4 Public Who was SAP (before HANA)? Sales Order Management Production PlanningTalent Management Financial/Mgmt Accounting Business Intelligence

5 5©2014 SAP AG or an SAP affiliate company. All rights reserved. 74% of the world’s transaction revenue touches an SAP system.

6 ©2014 SAP AG or an SAP affiliate company. All rights reserved.6 Public SAP Business 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

7 7©2014 SAP AG or an SAP affiliate company. All rights reserved.

8 8 Public Products & Innovation HANA Platform California Campus – Worldwide

9 SAP HANA DB Background Why did we build HANA?

10 ©2014 SAP AG or an SAP affiliate company. All rights reserved.10 Public How Did the SAP Use Database Before HANA? See “The SAP Transaction Model: Know Your Applications”, SIGMOD 2008 Industrial TalkThe SAP Transaction Model: Know Your Applications  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

11 ©2014 SAP AG or an SAP affiliate company. All rights reserved.11 Public DRAM Price/GB YearPrice /GB 2013$ $ $ $1, $30, $103, $859, $6,328,125 Source:

12 ©2014 SAP AG or an SAP affiliate company. All rights reserved.12 Public In-Memory Computing Yes, DRAM is 125,000 times faster than disk, but DRAM access is still times slower than on-chip caches 80 NS, TBs CPU Core L1 Cache L2 Cache L3 Cache Main Memory Disk 1 NS, 64K/core 3 NS, 256k/core 8 NS, >2M shared SSD:100K NS HD: 10M NS Using Intel Ivy Bridge for approximate values.Intel Ivy Bridge Actual numbers depends on specific hardware cores/CPU 4-8 sockets

13 ©2014 SAP AG or an SAP affiliate company. All rights reserved.13 Public 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

14 ©2014 SAP AG or an SAP affiliate company. All rights reserved.14 Public Simplify Technology Stack with the SAP HANA Platform SAP HANA Platform ApplicationsAnalytics Insight to Action Contextual. Real-time. Closed-loop.

15 ©2014 SAP AG or an SAP affiliate company. All rights reserved.15 Public SAP HANA Database Background BWA / BIA Trex PTime MaxDB Ancient times Enterprise Search NewDB / HANA BW on HANA Suite on HANA 2012 HANA Platform now Sybase IQ/ASE/SA/RS/etc.

16 SAP HANA DB Architecture

17 ©2014 SAP AG or an SAP affiliate company. All rights reserved.17 Public Technological Context Multi-Core CPUs Clock speed does not increase More CPU cores Small cache in CPU Large Memory 1 TB RAM widely available Slow compared to CPU Disk “Unlimited” Size Increasing Latency gap

18 ©2014 SAP AG or an SAP affiliate company. All rights reserved.18 Public SAP HANA DB Processes

19 19©2014 SAP AG or an SAP affiliate company. All rights reserved. Business Applications Connection and Session Management Authori- zation Manager Metadata Manager Trans- action Manager SQLSQL ScriptMDX… Optimizer and Plan Generator Calculation Engine Execution Engine In-Memory Processing Engines Column EngineRow EngineText Engine Persistency Logging and RecoveryData Storage

20 ©2014 SAP AG or an SAP affiliate company. All rights reserved.20 Public Distributed Share-Nothing In-Memory Computing

21 Column Store & Compression

22 ©2014 SAP AG or an SAP affiliate company. All rights reserved.22 Public Motivation: Customer System Sizes (Medium-Sized) “Within the last (exactly) three months, we managed to reduce the memory footprint NewDB (for a sample BW system) from initially 480 Gb to now 160 Gb, thus saving customers  Euros licensing costs and making the compression rates even more competitive.” Sizing Tool for BW on HANA: Customer 1Customer 2Customer 3Customer 4 Row Store (GB) Column Store (GB) Other internal data structures (GB) Total heap memory used (GB) System X Table Size (GB) System X Total DB Size (GB)

23 ©2014 SAP AG or an SAP affiliate company. All rights reserved.23 Public 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 Att1Att2Att3Att4Att5 Att2 Att4Att5 Att3 Att1 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)

24 ©2014 SAP AG or an SAP affiliate company. All rights reserved.24 Public SAP HANA Technology Dictionary Compression & N-bit Compression Company [CHAR50] Region [CHAR30] Group [CHAR5] INTELUSAA SiemensEuropeB SiemensEuropeC SAPEuropeA SAPEuropeA IBMUSAA INTEL 1 Siemens 2 SAP 3 IBM Europe 1 USA A 1 B 2 C HANA Column Store Classical Row Store Dictionary for attribute/ column „Group“ Index Vector Stored in one memory chunk => data locality for fast scans

25 ©2014 SAP AG or an SAP affiliate company. All rights reserved.25 Public SAP HANA Technology Compression with run length encoding Company [CHAR50] Region [CHAR30] Group [CHAR5] INTELUSAA SiemensEuropeB SiemensEuropeC SAPEuropeA SAPEuropeA IBMUSAA INTEL 1 Siemens 2 SAP 3 IBM Europe 1 USA A 1 B 2 C HANA Column Store: Dictionary compressed Classical Row Store Difficult to compress 1 x „0“ 2 x „1“ 2 x „2“ 0 INTEL 1 Siemens 2 SAP 3 IBM 0 Europe 1 USA 0 A 1 B 2 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

26 ©2014 SAP AG or an SAP affiliate company. All rights reserved.26 Public SAP HANA Technology Dictionary Compression Dictionary (Main Storage) Sorted array of values Implicit value ID = position in array Lookup by binary search: works like index For strings data: additional front-coding Column stored as value ID sequence  Bit coded using log 2 (N DICT ) bits Fast comparison ( =, ) on integers Speeds up scan, join, region queries Dictionary (Delta) Unsorted array For lookup: search tree (CSB+ tree) Search Find Value in dictionary scan value ID sequence for occurrences Optional index:  For each value in dictionary list of rows with value

27 ©2014 SAP AG or an SAP affiliate company. All rights reserved.27 Public SAP HANA Technology Compression of Value ID Sequence

28 ©2014 SAP AG or an SAP affiliate company. All rights reserved.28 Public SAP HANA Technology Dictionary Compression HANA Bluebook, p.53

29 Snapshot Isolation

30 ©2014 SAP AG or an SAP affiliate company. All rights reserved.30 Public Initial Design – set oriented, optimized for OLAP time oldest reader …………… tx1 commit …………… base list of rows visible to all tx2 access inserted deleted …………… tx3 commit …………… tx2 begintx4 begin & access …………… DATA-D ……………DATA-D ATA-DA TA-

31 ©2014 SAP AG or an SAP affiliate company. All rights reserved.31 Public 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 rowtx1 tx1: insert 1 row valid from tx3: delete where … valid to tx3 txn: reader

32 Outlook Where is HANA going next?

33 ©2014 SAP AG or an SAP affiliate company. All rights reserved.33 Public 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 times slower than on-chip caches

34 ©2014 SAP AG or an SAP affiliate company. All rights reserved.34 Public 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

35 ©2014 SAP AG or an SAP affiliate company. All rights reserved.35 Public 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

36 ©2014 SAP AG or an SAP affiliate company. All rights reserved. Thank you! https://www.saphana.com Contact information: Arne Schwarz, Mihnea Andrei, Richard Pledereder,

37 ©2014 SAP AG or an SAP affiliate company. All rights reserved.37 Public © 2014 SAP AG or an SAP affiliate company. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP AG or an SAP affiliate company. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG (or an SAP affiliate company) in Germany and other countries. Please see for additional trademark information and notices.http://global12.sap.com/corporate-en/legal/copyright/index.epx Some software products marketed by SAP AG and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP AG or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP AG or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP AG or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. In particular, SAP AG or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP AG’s or its affiliated companies’ strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP AG or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.

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