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DB-Inspired Approach to Streams

ATLaS: A Complete Database Language for Streams Carlo Zaniolo, Haixun Wang Richard Luo,Jan-Nei Law et al. Documentation and software downloads: http://wis.cs.ucla.edu/. DB-Inspired Approach to Streams. The Good:

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DB-Inspired Approach to Streams

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  1. ATLaS:A Complete Database Language for Streams Carlo Zaniolo, Haixun WangRichard Luo,Jan-Nei Lawet al.Documentation and software downloads: http://wis.cs.ucla.edu/

  2. DB-Inspired Approach to Streams • The Good: • Information-centered, high level Query languages, Data Independence, Optimization, … • isolate applications from details, amortize investment • The Bad: SQL lacks expressive power! • New application domain  ad-hoc extensions (by committee) • Designed for secondary store  blocking aggregates assumed • The Ugly: • The secondary storage mantra is gone • Can SQL designed for secondary store win over languages such as XQuery, XSLT or Hancock designed for the • web and streams—and also claiming Turing completeness?

  3. A Brave New World • Just extending SQL for streams will not do, if • SQL remains crippled by its impotency in expressing applications • and we will lose out against non-DB approaches since we lost our DB mantra and home field advantage

  4. A Brave New World • User-Defined Aggregates (UDAs) are a path to salvation because they, • Can be defined in SQL (rather than in a procedural language) • Turn SQL into a Turing Complete (TC) language • Fit the stream-oriented computation model like a glove—e.g., non-blocking properties of queries can be characterized syntactically • ATLaS does all that, and demonstrates the effectiveness of UDAs in expressing data-intensive applications, and delivers good performance.

  5. AGGREGATE myavg1(Next Int) : Real { TABLE state(sm Int, cnt Int); INITIALIZE : {INSERT INTO state VALUES (Next, 1)}  ITERATE : {UPDATE state SET sm=sm+Next, cnt=cnt+1;} TERMINATE : {INSERT INTO RETURN SELECT sm/cnt FROM state;} } AGGREGATE myavg2(Next Int) : Real { TABLE state(sm Int, cnt Int); INITIALIZE : {INSERT INTO state VALUES (Next, 1)}  ITERATE : {UPDATE state SET sm=sm+Next, cnt=cnt+1; INSERT INTO RETURN SELECT sm/cnt FROM state WHERE Cnt=100; UPDATE state SET sm=0, cnt=0 WHERE Cnt=100} } No TERMINATE  Non-Blocking UDAs: Initialize, Iterate, Terminate

  6. Homework Problems:Solutions in Two Flavors • without windows (ATLaS is TC for both blocking and nonblocking functions), and • with windows [partition&range construct] • ATLaS provides constructs to simplify the definition of UDAs on windows, • But ATLaS does not support joins of windows,since a. Problems when windows are viewed as data sets, b. in SQL:1999 windows are aggregate modifiers (OLAP), c. Simple solutions are already at hand in the TC ATLaS. • The benefits of TC: we add a new construct only when pros >> cons

  7. Interesting Times Ahead … Language Design Issues: • Semi-structured information and XML documents—can they be viewed as streams? • Ditto for references, • Streams of Updates and Events, active databases Applications: e.g., micro-climate monitoring, from UCLA Embedded Networked Sensors Center . Bridging the gap will not be easy.

  8. References Stephan Kepser: A Proof of the Turing-completeness of XSLT and Xquery, SFB 441, University of Tubingen Technical Report, SFB 441, May 13, 2002,kepser@sfs.uni-tuebingen.de

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