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From "Ratner, Alan S (IS)" <Alan.Rat...@ngc.com>
Subject RE: Fetch Failures
Date Mon, 01 Nov 2010 14:17:44 GMT
Reverting to Hadoop 0.20.2 from 0.21.0 fixed my fetch failure problem.
I suspect that 0.21.0 has a bug or some configuration parameter needs to
be changed between the 2 versions.  The bottom line is that my Hadoop
programs take 3 times as long to run on 0.21.0 compared to 0.20.2
because of the time required for Hadoop to recover from the fetch
failures.  For example, one program ran in 320 seconds in 0.20.2 versus
1047 seconds in 0.21.0; another ran in 30 seconds versus 96 seconds.

So the question is: What do I have to do to get 0.21.0 running properly
or should I wait for 0.21.1?


Alan 


-----Original Message-----
From: Ratner, Alan S (IS) [mailto:Alan.Ratner@ngc.com] 
Sent: Thursday, October 28, 2010 10:31 AM
To: mapreduce-user@hadoop.apache.org
Subject: EXTERNAL:Fetch Failures

After making two changes to my Hadoop cluster I now see Fetch Failures
that I previously did not see when running the same MR programs.  The
two changes were:
a) moving from Hadoop 0.20.2 to 0.21.0, and
b) downsizing my cluster from 39 slave nodes to 5 slave nodes (plus 1
dedicated master node).
The failure mechanism seems to be consistent on many different MR
programs: Map reaches 100% and Reduce reaches 32-33% and then the errors
are reported.  Any ideas on how I can fix this problem?  Thanks.

For the record, each node has 2 quad-core Nehalems with 24 or 48 GB RAM
and 4 1TB hard drives.


10/10/28 08:51:11 INFO mapred.FileInputFormat: Total input paths to
process : 47
10/10/28 08:51:11 WARN conf.Configuration: mapred.map.tasks is
deprecated. Instead, use mapreduce.job.maps
10/10/28 08:51:11 INFO mapreduce.JobSubmitter: number of splits:54
10/10/28 08:51:11 INFO mapreduce.JobSubmitter: adding the following
namenodes' delegation tokens:null
10/10/28 08:51:12 INFO mapreduce.Job: Running job: job_201010280812_0004
10/10/28 08:51:13 INFO mapreduce.Job:  map 0% reduce 0%
10/10/28 08:51:20 INFO mapreduce.Job:  map 20% reduce 0%
10/10/28 08:51:21 INFO mapreduce.Job:  map 31% reduce 0%
10/10/28 08:51:22 INFO mapreduce.Job:  map 64% reduce 0%
10/10/28 08:51:23 INFO mapreduce.Job:  map 85% reduce 0%
10/10/28 08:51:24 INFO mapreduce.Job:  map 94% reduce 0%
10/10/28 08:51:25 INFO mapreduce.Job:  map 96% reduce 0%
10/10/28 08:51:27 INFO mapreduce.Job:  map 100% reduce 0%
10/10/28 08:51:29 INFO mapreduce.Job:  map 100% reduce 28%
10/10/28 08:51:32 INFO mapreduce.Job:  map 100% reduce 32%
10/10/28 08:52:04 INFO mapreduce.Job: Task Id :
attempt_201010280812_0004_m_000000_0, Status : FAILED
Too many fetch-failures
10/10/28 08:52:04 WARN mapreduce.Job: Error reading task
outputConnection refused
10/10/28 08:52:04 WARN mapreduce.Job: Error reading task
outputConnection refused
10/10/28 08:52:37 INFO mapreduce.Job: Task Id :
attempt_201010280812_0004_m_000001_0, Status : FAILED
Too many fetch-failures
10/10/28 08:52:37 WARN mapreduce.Job: Error reading task
outputConnection refused
10/10/28 08:52:37 WARN mapreduce.Job: Error reading task
outputConnection refused
10/10/28 08:52:50 INFO mapreduce.Job:  map 100% reduce 100%
10/10/28 08:52:52 INFO mapreduce.Job: Job complete:
job_201010280812_0004
10/10/28 08:52:52 INFO mapreduce.Job: Counters: 34
	FileInputFormatCounters
		BYTES_READ=9370328
	FileSystemCounters
		FILE_BYTES_READ=1670448
		FILE_BYTES_WRITTEN=3342942
		HDFS_BYTES_READ=9766272
		HDFS_BYTES_WRITTEN=670
	Shuffle Errors
		BAD_ID=0
		CONNECTION=0
		IO_ERROR=5
		WRONG_LENGTH=0
		WRONG_MAP=0
		WRONG_REDUCE=0
	Job Counters 
		Data-local map tasks=42
		Total time spent by all maps waiting after reserving
slots (ms)=0
		Total time spent by all reduces waiting after reserving
slots (ms)=0
		Rack-local map tasks=14
		SLOTS_MILLIS_MAPS=160676
		SLOTS_MILLIS_REDUCES=88121
		Launched map tasks=56
		Launched reduce tasks=2
	Map-Reduce Framework
		Combine input records=193132
		Combine output records=54
		Failed Shuffles=5
		GC time elapsed (ms)=2293
		Map input records=258047
		Map output bytes=4426979
		Map output records=193132
		Merged Map outputs=54
		Reduce input groups=47
		Reduce input records=54
		Reduce output records=8
		Reduce shuffle bytes=1670766
		Shuffled Maps =54
		Spilled Records=108
		SPLIT_RAW_BYTES=5540

...

Task Logs: 'attempt_201010280812_0004_m_000000_1'



stdout logs


stderr logs


syslog logs
2010-10-28 04:58:18,855 INFO org.apache.hadoop.metrics.jvm.JvmMetrics:
Initializing JVM Metrics with processName=MAP, sessionId=
2010-10-28 04:58:18,861 WARN org.apache.hadoop.conf.Configuration:
user.name is deprecated. Instead, use mapreduce.job.user.name
2010-10-28 04:58:18,923 WARN org.apache.hadoop.conf.Configuration:
mapred.task.id is deprecated. Instead, use mapreduce.task.attempt.id
2010-10-28 04:58:18,991 INFO org.apache.hadoop.mapred.MapTask:
numReduceTasks: 1
2010-10-28 04:58:19,132 INFO org.apache.hadoop.mapred.MapTask: (EQUATOR)
0 kvi 65535996(262143984)
2010-10-28 04:58:19,132 INFO org.apache.hadoop.mapred.MapTask:
mapreduce.task.io.sort.mb: 250
2010-10-28 04:58:19,132 INFO org.apache.hadoop.mapred.MapTask: soft
limit at 209715200
2010-10-28 04:58:19,132 INFO org.apache.hadoop.mapred.MapTask: bufstart
= 0; bufvoid = 262144000
2010-10-28 04:58:19,132 INFO org.apache.hadoop.mapred.MapTask: kvstart =
65535996; length = 16384000
2010-10-28 04:58:19,373 INFO org.apache.hadoop.mapred.MapTask: Starting
flush of map output
2010-10-28 04:58:19,373 INFO org.apache.hadoop.mapred.MapTask: Spilling
map output
2010-10-28 04:58:19,373 INFO org.apache.hadoop.mapred.MapTask: bufstart
= 0; bufend = 165350; bufvoid = 262144000
2010-10-28 04:58:19,373 INFO org.apache.hadoop.mapred.MapTask: kvstart =
65535996(262143984); kvend = 65506324(262025296); length =
29673/16384000
2010-10-28 04:58:19,768 INFO org.apache.hadoop.mapred.MapTask: Finished
spill 0
2010-10-28 04:58:19,772 INFO org.apache.hadoop.mapred.Task:
Task:attempt_201010280812_0004_m_000000_1 is done. And is in the process
of commiting
2010-10-28 04:58:19,780 INFO org.apache.hadoop.mapred.Task: Task
'attempt_201010280812_0004_m_000000_1' done.

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