"One Size Fits All": An Idea Whose Time Has Come and Gone (Abstract)

The last 25 years of commercial DBMS development can be summed up in a single phrase: "one size fits all". This phrase refers to the fact that the traditional DBMS architecture (originally designed and optimized for business data processing) has been used to support many data-centric applications with widely varying characteristics and requirements. In this paper, we argue that this concept is no longer applicable to the database market, and that the commercial world will fracture into a collection of independent database engines, some of which may be unified by a common front-end parser. We use examples from the stream-processing market and the data-warehouse market to bolster our claims. We also briefly discuss other markets for which the traditional architecture is a poor fit and argue for a critical rethinking of the current factoring of systems services into products.

[1]  David E. Culler,et al.  Lessons from a Sensor Network Expedition , 2004, EWSN.

[2]  Wei Hong,et al.  The design of an acquisitional query processor for sensor networks , 2003, SIGMOD '03.

[3]  Matt Welsh,et al.  CodeBlue: An Ad Hoc Sensor Network Infrastructure for Emergency Medical Care , 2004 .

[4]  Howard Gobioff,et al.  The Google file system , 2003, SOSP '03.

[5]  Michael Stonebraker,et al.  Linear Road: A Stream Data Management Benchmark , 2004, VLDB.

[6]  Lawrence A. Rowe,et al.  Data abstraction, views and updates in RIGEL , 1979, SIGMOD '79.

[7]  Michael Stonebraker,et al.  High-availability algorithms for distributed stream processing , 2005, 21st International Conference on Data Engineering (ICDE'05).

[8]  Bruce H. Krogh,et al.  Energy-efficient surveillance system using wireless sensor networks , 2004, MobiSys '04.

[9]  Margaret Martonosi,et al.  Implementing software on resource-constrained mobile sensors: experiences with Impala and ZebraNet , 2004, MobiSys '04.

[10]  Paul Saffo,et al.  Sensors: the next wave of innovation , 1997, CACM.

[11]  Jim Gray,et al.  Fault Tolerance in Tandem Computer Systems , 1987 .

[12]  Gregory J. Pottie,et al.  Wireless integrated network sensors , 2000, Commun. ACM.

[13]  Michael Stonebraker,et al.  Aurora: a data stream management system , 2003, SIGMOD '03.

[14]  Frederick Reiss,et al.  TelegraphCQ: continuous dataflow processing , 2003, SIGMOD '03.

[15]  Michael Stonebraker,et al.  The design and implementation of INGRES , 1976, TODS.

[16]  Michael Stonebraker,et al.  Monitoring Streams - A New Class of Data Management Applications , 2002, VLDB.

[17]  Eric Brewer,et al.  Combining Systems and Databases: A Search Engine Retrospective , 2004 .

[18]  Michael Stonebraker,et al.  Aurora: a new model and architecture for data stream management , 2003, The VLDB Journal.

[19]  C. Mohan,et al.  ARIES/NT: A Recovery Method Based on Write-Ahead Logging for Nested Transactions , 1989, VLDB.

[20]  Jennifer Widom,et al.  Query Processing, Resource Management, and Approximation ina Data Stream Management System , 2002 .

[21]  Sandeep K. S. Gupta,et al.  Research challenges in wireless networks of biomedical sensors , 2001, MobiCom '01.

[22]  Frederick Reiss,et al.  TelegraphCQ: Continuous Dataflow Processing for an Uncertain World , 2003, CIDR.

[23]  Joachim W. Schmidt,et al.  Some high level language constructs for data of type relation , 1977, TODS.

[24]  Irving L. Traiger,et al.  System R: relational approach to database management , 1976, TODS.