lect-13-project-proposals

lect-13-project-proposals - 5/20/2011 Lazy Evaluation for...

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5/20/2011 1 CSE503: SOFTWARE ENGINEERING PROJECT PROPOSALS David Notkin Spring 2011 Lazy Evaluation for MapReduce Workflows Kristi Morton, Magdalena Balazinska, Dan Grossman, and Christopher Olston Motivating Scenario Data deluge in sciences LSST workflow process 30TB of new data every day Only a subset of data needed High-latency analysis tasks in workflows On MapReduce (MR) can take hours to run Limits scientific discovery Goal: Make workflows efficient by being lazy: only run on region of interest. LSST Workflow MR Job 1 Clean data MR Job 2 Extract/transform features MR Job 3 Classify particles for a region of space (x,y,z) on some property 30 TB Lazy processing: observe usage in workflow and only processes data of interest. Any additional data is computed on demand, per user’s request.
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5/20/2011 2 Lazy Evaluation for MapReduce Workflows Context for Project # 2: Continuation from Project 1 Workflows expressed as PigLatin scripts Use User Defined Function (UDF) for lazy evaluation framework Lazy Evaluation for MapReduce Workflows To produce the lazy materialized view, UDF needs: 1. PigLatin workflow script 2. Fields needed by user: e.g. gas, temp 3. Delimiter REGISTER udfs.jar; gas43 = LOAD ’gas43full’ USING BinStorage() AS (pid:long, mass:double, px:double, py:double, pz:double,temp:double, . ..); gas = FILTER gas43 BY pid is not null AND mass is not null AND px is not null AND py is not null AND pz is not null AND temp is not null ...; regionA = FILTER gas BY temp > udfs.VirialTemp (Rvir,Mvir,pid) AND px >= -0.5 AND px < -0.25 AND py >= 0 AND py < 0.5 AND pz >= -0.5 AND pz < 0; STORE regionA INTO ’result’ USING BinStorage() Lazy Evaluation for MapReduce Workflows Gas Temp a 200 b 150 c 1000 d 999 All other fields with pointers to PigLatin scripts to generate them on the fly (i.e. the “closures” part We generate the following materialized view + metadata: Project 2 work to do Previous project generated simple metadata for on demand fields Naïve approach: each field pointed to full workload script (i.e. computed all fields) Need to have fields point to appropriate subset to compute only the data for the field not all fields Need to generate subset scripts to compute individual fields Involves looking at level of execution plan tree and pruning off subtrees Will need to hack Pig query compiler, which is a nontrivial task
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This note was uploaded on 02/24/2012 for the course CSE 503 taught by Professor Davidnotikin during the Spring '11 term at University of Washington.

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lect-13-project-proposals - 5/20/2011 Lazy Evaluation for...

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