Continuous Query Language: From CQL to CAPE Algebra

Plans

Lee ChuChe 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

Databases Systems VS Data Stream System

• Database System– One time query

– Random access

• Data Stream System– Continuous queries

– Sequential access

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

Algebra Plan VS SQL

select S.A from R, S, Qwhere 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>

ID = 1Group 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

Our Query Plan Generator: Big Picture

CQL STREAM Plan

Generator

STREAM Plan

Generator

CAPEEngine

STREAM Parser

STREAM Parser

CAPE Plan Rewriter

CAPE Plan Rewriter

CAPE XML Plan Writer

CAPE XML Plan Writer

13

Step 1 :STREAM Parser

CQL

Generates a parse tree

Generates a parse tree

STREAM Parser

STREAM Parser

STREAM Plan

Generator

STREAM Plan

Generator

Yacc and Lex

14

Step 2: STREAM Plan Generator

STREAM Plan

Generator

STREAM Plan

Generator

Modified Plan

t_rstreamNowt_removeIstreamt_streamCross

t_removeProjectt_makeCrossBinary

t_makeStreamCrossBinaryt_pushSelect

t_rstreamNowt_removeIstreamt_streamCross

t_removeProjectt_makeCrossBinary

t_makeStreamCrossBinaryt_pushSelect

CAPE Plan Rewriter

CAPE Plan RewriterParse Tree

15

STREAM Plan Generator :Default Query Plan

query : rstream

(select S.A from R,

S [range 1 minute], Q,

where R.A = S.A);

RStreamID = 7

Project [1, 0]ID = 6

Cross (1, 3, 4)ID = 0

Select[0,0]==[1,0]ID = 5

Stream Source[1]ID = 2

Range Window[60]ID = 3

Stream Source[2]ID = 4Stream Source[0]

ID = 1

16

STREAM Plan Generator:Cleaned Query Plan

query : rstream

(select S.A from R,

S [range 1 minute], Q,

where R.A = S.A);

RStreamID = 7

Project [1, 0]ID = 6

Cross (10, 4)ID = 9

Select[0,0]==[1,1]ID = 10

Cross (1, 3)ID = 8

Stream Source[0]ID = 1

Range Window[60]ID = 3

Stream Source[1]ID = 2

Stream Source[2]ID = 4

17

Step 3: CAPE Plan Rewriter

Optimized Tree

ThetaJoin rule

WindowPushUp rule

ThetaJoin rule

WindowPushUp rule

CAPE Plan Rewriter

CAPE Plan RewriterCleaned Tree

18

ThetaJoin RuleRStream

ID = 7

Project [1, 0]ID = 6

Cross (10, 4)ID = 9

Select[0,0]==[1,1]ID = 10

Cross (1, 3)ID = 8

ThetaJoin[0,0]==[1,1]ID = 11

Cross (11, 4)ID = 9

Stream Source[2]ID = 4

Stream Source[1]ID = 2

Stream Source[0]ID = 1

Range Window[60]ID = 3

19

Project [1, 0]ID = 6

Cross (11, 4)ID = 9

Range Window [60]

ThetaJoin[0,0]==[1,1]ID = 11

Range Window[60]

Stream Source[0]ID = 1 Stream Source[1]

ID = 2

ThetaJoin[0,0]==[1,1]ID = 11

Cross (11, 4)ID = 9

Stream Source[1]ID = 2

Range Window[60]ID = 3

WindowPushUp RuleRStream

ID = 7

Project [1, 0]ID = 6

Stream Source[0]ID = 1

Stream Source[2]ID = 4

ThetaJoin[0,0]==[1,1]ID = 11

Range Window[60]

Stream Source[0]ID = 1

Range Window[60]ID = 3

ThetaJoin[0,0]==[1,1]ID = 11

Range Window[60]

Stream Source[0]ID = 1

Range Window[60]ID = 3

Project [1, 0]ID = 6

Cross (11, 4)ID = 9

Range Window[60]

ThetaJoin[0,0]==[1,1]ID = 11

Range Window[60]

Stream Source[0]ID = 1

ThetaJoin[0,0]==[1,1]ID = 11

20

<queryplan>

</queryplan>

<operator root><class variables> < /class variables><properties></properties><parents></parents><children></children><stream> </stream>

</operator>

Step 4: CAPE XML Plan Writer

XML Plan

CAPEEngine

Optimized Tree CAPE XML Plan Writer

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?