I\'ve created a Elastic MapReduce job, and I\'m trying to optimize its performance. At this moment I\'m trying to increase the number of mappers per instance. I am 开发者_运维问答doing this via mapre
I have a mapper that, while processing data, classifies output into 3 different types (type is the output key). My goal is to create 3 different csv files via the reducers, each with all of the data f
What should I change to fix following error: I\'m trying to start a job on Elastic Mapreduce, and it crashes every time with message:
Through the UI Amazon\'s framework allows me to create jobs with multiple inputs by specifying multiple --input lines. e.g.:
I\'m trying to run a job on Elastic MapReduce (EMR) with a custom jar. I\'m trying to process about a 1000 files in a single directory. When I submit my job with the parameter s3n://bucketname/compres
Given I need to process input of 20 Gb with the use of 10 instances. Is it different to have 10 input files of 2Gb compare to 4 input files of 5Gb?
So, it is easy enough to handle external jars when using hadoop straight up. You have -libjars option that will do this for you. The question is how do you do this with EMR. There must be an easy way
I\'m setting up a Hadoop cluster on EC2 and I\'m wondering how to do the DFS. All my data is currently in s3 and all map/reduce applications use s3 file pa开发者_运维百科ths to access the data. Now I\
I\'m using Pig on Amazon\'s Elastic Map-Reduce to do batch analytics.My input files are on S3 and contain events that are represented by one JSON dictionary per line.I use the elephantbird JsonLoader
There are some large datasets (25gb+, downloadable on the Internet) that I want to play around with using Amazon EMR. Instead of downloading the datasets onto my own computer, and then re-uploading th
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