Paper Info
Title
PigOut: Making multiple Hadoop clusters work together
Abstract
This paper presents PigOut, a system that enables federated data processing over multiple Hadoop clusters. Using PigOut, a user (such as a data analyst) can write a single script in a high-level language to efficiently use multiple Hadoop clusters. There is no need to manually write multiple scripts and coordinate the execution for different clusters. PigOut accomplishes this by automatically partitioning a single, user-supplied script into multiple scripts that run on different clusters. Additionally, PigOut generates workflow descriptions to coordinate execution across clusters. In doing so, PigOut leverages existing tools built around Hadoop, avoiding extra effort required from users or administrators. For example, PigOut uses Pig Latin, a popular query language for Hadoop MapReduce, in a (virtually) unmodified form. Through our evaluation with PigMix, the standard benchmark for Pig, we demonstrate that PigOut's automatically-generated scripts and workflow definitions have comparable performance to manual, hand-tuned ones. We also report our experience with manually writing multiple scripts for a set of federated clusters, and compare the process with PigOut's automated approach.
Year
DOI
Venue
2014
10.1109/BigData.2014.7004218
BigData Conference
Keywords
Field
DocType
parallel processing,pattern clustering,pig latin,high-level language,query languages,pigmix,workflow descriptions,high level languages,pigout automatically-generated scripts,federated data processing,hadoop clusters,user-supplied script,data handling,query language,hadoop mapreduce
Data mining,Cluster (physics),Query language,Data processing,Computer science,Workflow,Database,Scripting language
Conference
ISSN
Citations 
PageRank 
2639-1589
2
0.40
References 
Authors
10
6
Name
Order
Citations
PageRank
Kyungho Jeon1785.68
Sharath Chandrashekhara253.22
Feng Shen391.28
Shikhar Mehra420.40
Oliver Kennedy530.77
Steven Y. Ko647145.08