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and SQL/MM Part 7: History

32N1766. and SQL/MM Part 7: History. ISO/IEC JTC 1/SC 32 WG 4 SQL/MM Convener Kohji SHIBANO. Revenue Structure. Usual Enterprise. Google. Net income. Net income. 5%. $5B 25%. Equipment cost. Operating income $10B 50%. Operating income 20%. Labor cost. Equipment cost.

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and SQL/MM Part 7: History

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  1. 32N1766 and SQL/MM Part 7: History ISO/IEC JTC 1/SC 32 WG 4 SQL/MM Convener Kohji SHIBANO

  2. Revenue Structure Usual Enterprise Google Net income Net income 5% $5B 25% Equipment cost Operating income $10B 50% Operating income 20% Labor cost Equipment cost Procurement cost Labor cost Sales amont $20B 100% Sales amount 100% Procurement cost

  3. Google’s Businesses and Services • Business • AdWord • AdSense • Service • Search • Web search • Earth • Map • Communicate, show & share • Document • Gmail • YouTube • mobile

  4. Googlebusiness model • From Portal to AdWord

  5. Googledata processingGoogle’s PageRank was a technology breakthroughCrawling and PageRank computation requires a lot of computations • Thus Google develop a set of new technologies for their infrastructure Crawler Text Extraction PageRank Search results

  6. Googleservers

  7. Cloud ComputingGooglecomputational infrastructure Application Framework MapReduce Application Programming Interface Database Chubby (lock mgr) Bigtable Operating System Google File System　（GFS) Google Work Queue　（GWQ) 1 million PC 20 PB/Day

  8. Responding search requests worldwide

  9. Google Bigtable • Data Model • (row:string, column:string, time:int64) → string

  10. Google Bigtable applications

  11. SQL/MM Approach • Using SQL as a formal specification language • In late 70’s and early 80’s within IBM Research • Criticized to use a formal method such as VDM (Vienna Development Method) and VDL (Vienna Development Language) developed by IBM Vienna Lab for the specification of SQL language • In SC 21 (OSI), strong recommendation to use formal methods • SQL/MM adopt SQL as a formal specification language • MM implementations includes • DB2, Oracle, PostGreSQL, MySQL etc. • MM services are implemented directly • Performance optimizations are up to the implementers

  12. SQL Part 7: History • In early 90’s, Temporal Database • Inspired by temporal logic base • In the 21st Century, computing environment drastically changed • Massive computational power and storage capacity make things possible • Massive computation • Massive information storage including historical records • Thus history support in SQL is SQL/MM Part 7: History

  13. SQL/MM requirements • The current SQL functionalities can support most of the functionalities found in Google’s cloud computing • Only lacked functionality is the support of “HISTORY” • Google Bigtable • (row:string, column:string, time:int64) → string • SQL/MM Part 7: History

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