and sql/mm part 7: history iso/iec jtc 1/sc 32 wg 4 sql/mm convener kohji shibano 32n1766

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

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

and SQL/MM Part 7: History

ISO/IEC JTC 1/SC 32 WG 4 SQL/MM ConvenerKohji SHIBANO

32N1766

Page 2: And SQL/MM Part 7: History ISO/IEC JTC 1/SC 32 WG 4 SQL/MM Convener Kohji SHIBANO 32N1766

Revenue Structure

Procurement cost

Labor cost

Equipment cost

Net incom

eOperating income20%

Sales amount100%

Usual Enterprise

5%

Procurement cost

Labor cost

Equipment cost

Net incom

eOperating income$10B50%

Sales amont$20B100%

Google

$5B25%

Page 3: And SQL/MM Part 7: History ISO/IEC JTC 1/SC 32 WG 4 SQL/MM Convener Kohji SHIBANO 32N1766

Google’s Businesses and Services

Business AdWord AdSense

Service Search

Web search Earth Map

Communicate, show & share Document Gmail YouTube

mobile

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

Google business model From Portal to AdWord

Page 5: And SQL/MM Part 7: History ISO/IEC JTC 1/SC 32 WG 4 SQL/MM Convener Kohji SHIBANO 32N1766

Google data 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

CrawlerText

Extraction

PageRank

Search results

Page 6: And SQL/MM Part 7: History ISO/IEC JTC 1/SC 32 WG 4 SQL/MM Convener Kohji SHIBANO 32N1766

Google servers

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

Cloud ComputingGoogle computational infrastructure

1 million PC20 PB/Day

Google File System  ( GFS)Google Work

Queue  ( GWQ)

Bigtable

MapReduce

Chubby (lock mgr)

Operating System

Database

Application Framework

Application Programming Interface

Page 8: And SQL/MM Part 7: History ISO/IEC JTC 1/SC 32 WG 4 SQL/MM Convener Kohji SHIBANO 32N1766

Responding search requests worldwide

Page 9: And SQL/MM Part 7: History ISO/IEC JTC 1/SC 32 WG 4 SQL/MM Convener Kohji SHIBANO 32N1766

Google Bigtable Data Model

(row:string, column:string, time:int64) → string

Page 10: And SQL/MM Part 7: History ISO/IEC JTC 1/SC 32 WG 4 SQL/MM Convener Kohji SHIBANO 32N1766

Google Bigtable applications

Page 11: And SQL/MM Part 7: History ISO/IEC JTC 1/SC 32 WG 4 SQL/MM Convener Kohji SHIBANO 32N1766

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

Page 12: And SQL/MM Part 7: History ISO/IEC JTC 1/SC 32 WG 4 SQL/MM Convener Kohji SHIBANO 32N1766

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

Page 13: And SQL/MM Part 7: History ISO/IEC JTC 1/SC 32 WG 4 SQL/MM Convener Kohji SHIBANO 32N1766

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