Customizable Parallel Execution of Scientific Stream Queries

Customizable Parallel Execution of Scientific Stream Queries

Milena Ivanova and Tore RischUppsala Database Laboratoryhttp://user.it.uu.se/~udbl/

8/30/2005 2Milena Ivanova and Tore Risch, UDBL

Motivation

Scientific instruments and simulators generate high-volume streams

Information exploration by wide range analyses

Off-line hard-coded analyses on data stored on disks

Need for on-line, flexible, and scalable stream processing


http://www.lois-space.net/– Very high data volume and rate– Complex numerical data– Relatively expensive user-defined computations


Grid Stream Database Manager (GSDM)

• Main-memory object-relational DSMS• Distributed and parallel architecture provides

scalability for data and computations• Customizable distributed execution strategies for

CQs by high-level Data Flow Distribution Templates • PCC (Partition-Compute-Combine) generic

template for lattice-shaped partitioned parallelism• Two customizable stream partitioning strategies:

Window Split and Window Distribute


Related WorkParallel stream processing Flux (Berkeley), Borealis

(MIT, Brown, Brandeis): load balancing and fault tolerance GSDM: Customizable templates and window split stream partitioning

DSMSs: Aurora (MIT, Brown, Brandeis), Gigascope (AT&T), STREAM (Stanford), TelegraphCQ (Berkeley): centralized architecture and cheap operators GSDM: distributed and user-defined numerical functions on large stream data items

Conventional data partitioning: Round Robin, hash, range. GSDM: query based customizable stream partitioning


Outline• Motivation• Related Work• GSDM Usage Scenario• Continuous Query Specification:

– Stream Query Functions– Data Flow Distribution Templates

• Customizable Parallel Strategies:– Window Split– Window Distribute

• Experimental Results• Conclusions and Future Work


GSDM ScenarioGSDM Scenario


Stream Query Functions (SQFs)Declarative query computing a logical

window in a result stream given one or several input streams

create function fft3(Radiosignal s) -> RadioWindow as select radioWindow(ts(v),fft(x(v)), fft(y(v)),fft(z(v))) from RadioWindow v where v = currentWindow(s);


Data Flow Distribution Templates

• Define customizable distibuted strategies• CQs as distributed compositions of SQFs • Logical site assignment for each SQF• Template constructor creates data flow graph• Generic template PCC (Partition-Compute-Combine) for customizable partitioned parallelism.


CQ Specification and Execution• Distributed execution strategy through

template:set wd= PCC(4,"S-Distribute","RRpart",

"fft3","S-Merge",0.1);• Input and output streams: set s1 = register_input_stream( "Radiosignal","1.2.3.4","UDP"); set s2 = register_result_stream( "1.2.3.5","Visualize");• Compilation: compile(wd,{s1},{s2},"hagrid.it.uu.se");• Execution: run(wd);


Partitioned Parallelism for Streams

Requirements for stream partitioning strategies:

• SQF-semantics preserving• Order-preserving• Good load balancing

Examples: Window Distribute and Window Split


Window Distribute

• Distributes several logical windows among different partitions

• SQF-independent• Parameterized on partitioning

functionset wd= PCC(2,"S-Distribute","RRpart",

"fft3","S-Merge",0.1);


Window Distribute (cont.)

Round Robin partitioning: create function RRpart(Stream s, Integer ptot, Integer pno) -> Window as select w[pno] from Vector of Window w where w = slidingWindow(s,ptot,ptot);


Window Split• Splits a single logical window into sub-

windows• Needs knowledge about SQF semantics • SQF-dependent parameters for window

splitting and combining set ws= PCC(2,"OS-Split","fft3part", "fft3","OS-Join","fft3combine");


Window Split (cont.)

FFT-dependent window split based on FFT- Radix K algorithm:

create function fft3part(RadioWindow w, Integer ptot,Integer pno)-> RadioWindow as select radioWindow(ts(w), fftpart(x(w),ptot,pno), fftpart(y(w),ptot,pno), fftpart(z(w),ptot,pno));


Experimental Results• Strategies: Window Split (WS), Window Distribute(WD),

and Central – all expressed as templates • Scalability metrics:

– Total maximum throughput– Scaled logical window sizes: 256-16384 samples

• Impact of computation and communication by two experimental sets (slow and fast implementation of FFT)

• Tree-structured partitioning and combining set wd-tree = PCC(2,"S-Distribute","RRpart","PCC", {2,"S-Distribute","RRpart","fft3", "S-Merge",0.1}, "S-Merge",0.1);


Experimental ResultsSlow Set, Degree 2

• Load of FFT computing nodes dominates• WS2 more efficient than WD2


Experimental Results – Slow Set, Degree 4

0

10

20

30

40

50

60

70

Partition FFT Combine

WD-FlatWS-FlatWS-Tree

Size 512

WS4 better for window size > = 1024


Experimental Results – Fast Set, Degree 4

0510152025303540


WD-FlatWD-TreeWS-FlatWS-Tree

0

5

10

15

20

25

30

35


Size 2048 Size 8192


Communication Costs

Partition Compute Combine

WS Proc 0,42 57,66 13,8WS Comm 15,94 2,82 5,17

WS Comm % 95% 4,6% 26,7%

WD Proc 0,04 62,95 0,09

WD Comm 7,56 2,72 5,01WD Comm % 93,9% 4,1% 91,2%

Elapsed processing and communication time in partition, compute, and combine phases for WS2 and WD2, fast set, size 8192


Speed-up – slow set


Conclusions• Parallel execution of CQs with expensive user-defined

computations• High-level Data Flow Distribution Templates specify

distributed execution strategies for CQs• PCC (Partition-Compute-Combine) template for lattice-

shaped partitioned parallelism• Two customizable stream partitioning strategies by

templates: Window Split – knowledge about application semantics for more efficient

computing– better when expensive CQs execute on limited resourcesWindow Distribute– application independent customizable partitioning– better for non-expensive operations, small window sizes,

or more resources


Ongoing and Future Work

• Optimized data flow graphs using profiled runs in training mode

• More complex CQs and other distribution strategies

• Adaptation• Resource allocation through Grid

infrastructure

Customizable Parallel Execution of Scientific Stream Queries

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