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Continuous Query Language: From CQL to CAPE Algebra Plans

Continuous Query Language: From CQL to CAPE Algebra Plans. Lee Chu Che Wai Kwan MQP 2004/2005. Continuous Query Processing. Emerging Applications: Traffic management Network monitoring Require: Online processing of data streams But: Traditional databases handle persistent data.

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Continuous Query Language: From CQL to CAPE Algebra Plans

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  1. Continuous Query Language: From CQL to CAPE Algebra Plans Lee Chu Che Wai Kwan MQP 2004/2005

  2. Continuous Query Processing • Emerging Applications: • Traffic management • Network monitoring • Require: • Online processing of data streams • But: • Traditional databases handle persistent data

  3. Database System One time query Random access Data Stream System Continuous queries Sequential access Databases Systems VS Data Stream System

  4. CAPE: Constraint-exploiting Adaptive Processing Engine • An on-going project at WPI

  5. CAPE’s limitation • Desire: • High-level query language, such as SQL • Instead: • Enter queries as low-level execution plan • Problems: • Tedious to enter • Error prone

  6. select S.A from R, S, Q where R.A = S.A <queryplan> <operator root = “true” id = “1” className = “ …”> <classVariables> <variable name=“group_pos” value=“0”/> <variable name=“function” value=“null”/> <variable name=“function_pos” value=“0”/> <variable name=“function” value=“count”/> <variable name=“function_pos” value=“0”/> <variable name=“propagate” value=“false”/> <variable name=“debug” value=“true”/> </classVariables> <properties> </properties> <parents> </parents> <children> <child id = “2”/> </children> <streams> </streams> </operator> <operator root…..> . . . </operator> . . . </queryplan> Algebra Plan VS SQL ID = 1 Group By ID = 2

  7. Objective • Define and implement a high-level query language for CAPE

  8. Methodology • Study existing Continuous Processing Language proposals • Identify one, adopt and adapt if appropriate • Implement it for CAPE

  9. Requirements on Language • SQL-alike • Data Streams • Windows on streams

  10. Continuous Processing Languages • UDA – UCLA • TelegraphCQ – Berkeley • STREAM-CQL – Stanford

  11. STREAM-CQL • Well defined semantics • Open source available • Query example: query : rstream (select S.A from R, Q, S[range 1 minute] where R.A = S.A);

  12. STREAM Plan Generator STREAM Parser CQL CAPE XML Plan Writer CAPE Plan Rewriter CAPE Engine Our Query Plan Generator: Big Picture

  13. CQL Generates a parse tree STREAM Plan Generator STREAM Parser Step 1 :STREAM Parser Yacc and Lex

  14. t_rstreamNow t_removeIstream t_streamCross t_removeProject t_makeCrossBinary t_makeStreamCrossBinary t_pushSelect Step 2: STREAM Plan Generator STREAM Plan Generator Modified Plan CAPE Plan Rewriter Parse Tree

  15. RStream ID = 7 Project [1, 0] ID = 6 Select[0,0]==[1,0] ID = 5 Cross (1, 3, 4) ID = 0 Stream Source[2] ID = 4 Range Window[60] ID = 3 Stream Source[0] ID = 1 Stream Source[1] ID = 2 STREAM Plan Generator :Default Query Plan query : rstream (select S.A from R, S [range 1 minute], Q, where R.A = S.A);

  16. RStream ID = 7 Project [1, 0] ID = 6 Cross (10, 4) ID = 9 Select[0,0]==[1,1] ID = 10 Stream Source[2] ID = 4 Cross (1, 3) ID = 8 Range Window[60] ID = 3 Stream Source[0] ID = 1 Stream Source[1] ID = 2 STREAM Plan Generator:Cleaned Query Plan query : rstream (select S.A from R, S [range 1 minute], Q, where R.A = S.A);

  17. ThetaJoin rule WindowPushUp rule Step 3: CAPE Plan Rewriter CAPE Plan Rewriter Optimized Tree Cleaned Tree

  18. Project [1, 0] ID = 6 Cross (10, 4) ID = 9 Cross (11, 4) ID = 9 Select[0,0]==[1,1] ID = 10 Stream Source[2] ID = 4 ThetaJoin[0,0]==[1,1] ID = 11 Cross (1, 3) ID = 8 Range Window[60] ID = 3 Stream Source[0] ID = 1 Stream Source[1] ID = 2 ThetaJoin Rule RStream ID = 7

  19. Range Window[60] ID = 3 Project [1, 0] ID = 6 Project [1, 0] ID = 6 Project [1, 0] ID = 6 ThetaJoin[0,0]==[1,1] ID = 11 Range Window[60] Range Window[60] ID = 3 Cross (11, 4) ID = 9 Range Window [60] Cross (11, 4) ID = 9 Range Window[60] ThetaJoin[0,0]==[1,1] ID = 11 Range Window[60] Cross (11, 4) ID = 9 Stream Source[0] ID = 1 Stream Source[2] ID = 4 ThetaJoin[0,0]==[1,1] ID = 11 Range Window[60] ThetaJoin[0,0]==[1,1] ID = 11 ThetaJoin[0,0]==[1,1] ID = 11 Stream Source[0] ID = 1 ThetaJoin[0,0]==[1,1] ID = 11 Range Window[60] Stream Source[1] ID = 2 Stream Source[1] ID = 2 Range Window[60] ID = 3 Stream Source[0] ID = 1 Stream Source[0] ID = 1 Stream Source[0] ID = 1 WindowPushUp Rule RStream ID = 7

  20. <queryplan> <operator root> <class variables> < /class variables> <properties> </properties> <parents> </parents> <children> </children> <stream> </stream> </operator> </queryplan> CAPE Engine Step 4: CAPE XML Plan Writer Optimized Tree XML Plan CAPE XML Plan Writer

  21. Evaluation Methodology • Query test bed: • Test individual operators • Test complex query plans • Evaluation • Manual inspection of generated XML plan • Test XML file on CAPE

  22. Evaluation of Individual Operators • Regular Project • Function Project • Select • Stream Source • Range Window • Partition • Distinct

  23. CQL: Rstream (Select A from S where A =5);

  24. CQL: rstream (select A + B from S);

  25. Conclusion • Identified query language for CAPE • Designed a loosely coupled translation frameworks from CQL to CAPE: • Rewrite algebra tree • Generate CAPE XML plans • Evaluation of generated query plans

  26. Future Works • Implement Relations • Which will maximize CAPE’s capability • Research on the window size • Support different time range variation • Implement a Graphical User Interface • Drag and Drop feature to input CQL

  27. Acknowledgements • Prof. Rundensteiner • Yali Zhu • Luping Ding

  28. Question or Comments?

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