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From Jeff Quinn <>
Subject Non Deterministic Record Drops
Date Tue, 28 Jul 2015 00:11:30 GMT

We have observed and replicated strange behavior with our crunch
application while running on MapReduce via the AWS ElasticMapReduce
service. Running a very simple job which is mostly map only, we see that an
undetermined subset of records are getting dropped. Specifically, we
expect 30,136,686 output records and have seen output on different trials
(running over the same data with the same binary):

22,177,119 records
26,435,670 records
22,362,986 records
29,798,528 records

These are all the things about our application which might be unusual and

- We use a custom file input format, via From.formattedFile. It looks like
this (basically a carbon copy
of org.apache.hadoop.mapreduce.lib.input.TextInputFormat):

import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.LineRecordReader;


public class ByteOffsetInputFormat extends FileInputFormat<LongWritable, Text> {

  public RecordReader<LongWritable, Text> createRecordReader(
      InputSplit split, TaskAttemptContext context) throws IOException,
      InterruptedException {
    return new LineRecordReader();

- We call org.apache.crunch.Pipeline#read using this InputFormat many
times, for the job in question it is called ~160 times as the input is
~100 different files. Each file ranges in size from 100MB-8GB. Our job
only uses this input format for all input files.

- For some files org.apache.crunch.Pipeline#read is called twice one
the same file, and the resulting PTables are processed in different

- It is only the data from these files which
org.apache.crunch.Pipeline#read has been called on more than once
during a job that have dropped records, all other files consistently
do not have dropped records

Curious if any Crunch users have experienced similar behavior before,
or if any of these details about my job raise any red flags.


Jeff Quinn

Data Engineer


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