SAP HANA DATABASE Mihnea Andrei SAP Products Innovation

SAP HANA DATABASE Mihnea Andrei SAP Products & Innovation HANA Platform e only with lid s e tl ti is th e s U July 8, 2014 an image Public

Agenda SAP HANA DB Background Architecture Column Store & Compression Snapshot Isolation Outlook ©

SAP

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

74% of the world’s transaction revenue touches an SAP system. © 2014 SAP AG

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

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

Products & Innovation HANA Platform California Campus – Worldwide © 2014 SAP AG or

SAP HANA DB Background Why did we build HANA?

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 © 2014 SAP AG or an SAP affiliate company. All rights reserved. Public 10

DRAM Price/GB Year Price/GB 2013 2010 2005 2000 1995 1990 1985 1980 $5. 50 $12. 37 $189 $1, 107 $30, 875 $103, 880 $859, 375 $6, 328, 125 Source: http: //www. statisticbrain. com/average-historic-price-of-ram/ © 2014 SAP AG or an SAP affiliate company. All rights reserved. Public 11

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

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

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

SAP HANA Database Background BWA / BIA Trex Enterprise Search New. DB / HANA BW on HANA Suite on HANA 2011 2012 HANA Platform Max. DB PTime Sybase IQ/ASE/SA/RS/etc. Ancient times 2000 -2009 © 2014 SAP AG or an SAP affiliate company. All rights reserved. 2010 now Public 15

SAP HANA DB Architecture

SAP HANA DB Processes © 2014 SAP AG or an SAP affiliate company. All

Business Applications Connection and Session Management SQL Authorization Manager SQL Script MDX … Calculation Engine Optimizer and Plan Generator Trans- action Manager Execution Engine In-Memory Processing Engines Metadata Manager Persistency Column Engine Row Engine Logging and Recovery © 2014 SAP AG or an SAP affiliate company. All rights reserved. Text Engine Data Storage 19

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

Column Store & Compression

SAP HANA Technology Hybrid Data Storage SAP HANA Row Store stores tables by row Att 1 Att 2 Att 3 Att 4 SAP HANA Column Store stores tables by column Att 1 Att 5 Tuple 1 Tuple 2 Tuple 3 Tuple n n Application often processes single records at once n many selects and /or updates of single records n Application typically accesses the complete record n Columns contain mainly distinct values n Aggregations and fast searching not required n Small number of rows (e. g. configuration tables) © 2014 SAP AG or an SAP affiliate company. All rights reserved. Att 2 Att 3 Att 4 Att 5 n Search and calculation on values of a few columns n Big number of rows and columnar operations n aggregate, scan, etc. n High compression rates possible n Most columns contain only few distinct values Public 23

SAP HANA Technology Dictionary Compression & N-bit Compression Classical Row Store Company [CHAR 50] Region [CHAR 30] Group [CHAR 5] INTEL USA A Siemens Europe Siemens HANA Column Store 0 1 2 3 INTEL Siemens SAP IBM 0 Europe 1 USA 0 A 1 B 2 C 0 1 0 B 1 0 1 Europe C 1 0 2 SAP Europe A 2 0 0 IBM USA A 3 1 0 © 2014 SAP AG or an SAP affiliate company. All rights reserved. Dictionary for attribute/ column „Group“ Index Vector Stored in one memory chunk => data locality for fast scans Public 24

SAP HANA Technology Compression with run length encoding Classical Row Store Difficult to compress HANA Column Store: Dictionary compressed 0 1 2 3 INTEL Siemens SAP IBM HANA Column Store: Run length compressed* 0 A 1 B 2 C 0 1 2 3 INTEL Siemens SAP IBM 0 Europe 1 USA 0 A 1 B 2 C 1 x „ 0“ 1 x „ 1“ 1 x „ 0“ 1 2 x „ 1“ 4 x „ 0“ 1 x „ 1“ 0 2 2 x „ 2“ 1 x „ 1“ 1 x „ 2“ 2 0 0 1 x „ 3“ A 2 0 0 A 3 1 0 Company [CHAR 50] Region [CHAR 30] Group [CHAR 5] INTEL USA A 0 1 0 Siemens Europe B 1 0 Siemens Europe C 1 SAP Europe A SAP Europe IBM USA 0 Europe 1 USA 3 x „ 0“ * Note that there is a variety of compression methods and algorithms like run-length compression © 2014 SAP AG or an SAP affiliate company. All rights reserved. Public 25

Snapshot Isolation

time oldest reader tx 1 commit tx 2 begin © 2014 SAP AG or an SAP affiliate company. All rights reserved. tx 3 commit 011000…………… 001101111100111 011001…………… 1011011 111 000001…………… 1000001000 inserted 11111 deleted 100010…………… 01000000010 base list of rows visible to all 111011…………… 111101 DATA-D……………DATA-DATA- Initial Design – set oriented, optimized for OLAP tx 2 access tx 4 begin & access Public 30

New Design – OLTP friendly valid from to tx 3: delete where … tx 2: insert n rows New rows tx 1: insert 1 row New row – Valid from/to for every row? tx 3 tx 1 tx 3 Problems to solve Memory overhead tx 2 … DATA Tx identity: TID vs. CID – If TID: visibility rules, TCB memory overhead – If CID: DML time ID, atomic commit, post-commit L 2/3 cache friendly – Stay local, avoid dereferencing pointers OLAP performance © 2014 SAP AG or an SAP affiliate company. All rights reserved. txn: reader Public 31

Outlook Where is HANA going next?

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 © 2014 SAP AG or an SAP affiliate company. All rights reserved. Public 33

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

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 (< 3 X) 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 © 2014 SAP AG or an SAP affiliate company. All rights reserved. Public 35

Thank you! Contact information: Arne Schwarz, arne. schwarzy@sap. com Mihnea Andrei, mihnea. andrei@sap. com Richard Pleder, richard. pleder@sap. com http: //www. careersatsap. com/ http: //jobs. sap. com https: //www. saphana. com http: //www. sap. com/pc/tech/in-memory-computing-hana. html © 2014 SAP AG or an SAP affiliate company. All rights reserved.

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