Design Insights for MapReduce from Diverse Production Workloads
Yanpei Chen and Sara Alspaugh and Randy H. Katz
EECS Department, University of California, Berkeley
Technical Report No. UCB/EECS-2012-17
January 25, 2012
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-17.pdf
In this paper, we analyze seven MapReduce workload traces from production clusters at Facebook and at Cloudera customers in e-commerce, telecommunications, media, and retail. Cumulatively, these traces comprise over a year’s worth of data logged from over 5000 machines, and contain over two million jobs that perform 1.6 exabytes of I/O. Key observations include input data forms up to 77% of all bytes, 90% of jobs access KB to GB sized files that make up less than 16% of stored bytes, up to 60% of jobs re-access data that has been touched within the past 6 hours, peak-to-median job submission rates are 9:1 or greater, an average of 68% of all compute time is spent in map, task-seconds-per-byte is a key metric for balancing compute and data bandwidth, task durations range from seconds to hours, and five out of seven workloads contain map-only jobs. We have also deployed a public workload repository with workload replay tools so that the researchers can systematically assess design priorities and compare performance across diverse MapReduce workloads.
BibTeX citation:
@techreport{Chen:EECS-2012-17, Author= {Chen, Yanpei and Alspaugh, Sara and Katz, Randy H.}, Title= {Design Insights for MapReduce from Diverse Production Workloads}, Year= {2012}, Month= {Jan}, Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-17.html}, Number= {UCB/EECS-2012-17}, Abstract= {In this paper, we analyze seven MapReduce workload traces from production clusters at Facebook and at Cloudera customers in e-commerce, telecommunications, media, and retail. Cumulatively, these traces comprise over a year’s worth of data logged from over 5000 machines, and contain over two million jobs that perform 1.6 exabytes of I/O. Key observations include input data forms up to 77% of all bytes, 90% of jobs access KB to GB sized files that make up less than 16% of stored bytes, up to 60% of jobs re-access data that has been touched within the past 6 hours, peak-to-median job submission rates are 9:1 or greater, an average of 68% of all compute time is spent in map, task-seconds-per-byte is a key metric for balancing compute and data bandwidth, task durations range from seconds to hours, and five out of seven workloads contain map-only jobs. We have also deployed a public workload repository with workload replay tools so that the researchers can systematically assess design priorities and compare performance across diverse MapReduce workloads.}, }
EndNote citation:
%0 Report %A Chen, Yanpei %A Alspaugh, Sara %A Katz, Randy H. %T Design Insights for MapReduce from Diverse Production Workloads %I EECS Department, University of California, Berkeley %D 2012 %8 January 25 %@ UCB/EECS-2012-17 %U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-17.html %F Chen:EECS-2012-17