Hi I have a mapreduce jar that runs perfectly fine for small input files. When I say small I mean sample input files that I've created with less than 10 lines of input. But when I try to run mapreduce on an input file of size 1.8GB, I get the OutOfMemoryError. I'm not sure what i'm supposed to be doing.
Is there anyway that I can limit the number of tasks being spawned? And have few tasks run for longer durations?
Around 20 tasks are spawned on the large input file before I get this error. Here's part of the log that's generated for the first two tasks.
13/12/13 12:00:22 INFO mapreduce.Job: The url to track the job: http://localhost:8080/
13/12/13 12:00:22 INFO mapreduce.Job: Running job: job_local1170901099_0001
13/12/13 12:00:22 INFO mapred.LocalJobRunner: OutputCommitter set in config null
13/12/13 12:00:22 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
13/12/13 12:00:22 INFO mapred.LocalJobRunner: Waiting for map tasks
13/12/13 12:00:22 INFO mapred.LocalJobRunner: Starting task: attempt_local1170901099_0001_m_000000_0
13/12/13 12:00:22 INFO util.ProcfsBasedProcessTree: ProcfsBasedProcessTree currently is supported only on Linux.
13/12/13 12:00:22 INFO mapred.Task: Using ResourceCalculatorProcessTree : null
13/12/13 12:00:22 INFO mapred.MapTask: Processing split: hdfs://localhost:9000/user/chaitanya.nadig/friendship.txt:0+134217728
13/12/13 12:00:22 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
13/12/13 12:00:23 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
13/12/13 12:00:23 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
13/12/13 12:00:23 INFO mapred.MapTask: soft limit at 83886080
13/12/13 12:00:23 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
13/12/13 12:00:23 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
13/12/13 12:00:23 INFO mapreduce.Job: Job job_local1170901099_0001 running in uber mode : false
13/12/13 12:00:23 INFO mapreduce.Job: map 0% reduce 0%
13/12/13 12:00:24 INFO mapred.MapTask: Starting flush of map output
13/12/13 12:00:24 INFO mapred.LocalJobRunner: Starting task: attempt_local1170901099_0001_m_000001_0
13/12/13 12:00:24 INFO util.ProcfsBasedProcessTree: ProcfsBasedProcessTree currently is supported only on Linux.
13/12/13 12:00:24 INFO mapred.Task: Using ResourceCalculatorProcessTree : null
13/12/13 12:00:24 INFO mapred.MapTask: Processing split: hdfs://localhost:9000/user/chaitanya.nadig/friendship.txt:134217728+134217728
13/12/13 12:00:24 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
13/12/13 12:00:24 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
13/12/13 12:00:24 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
13/12/13 12:00:24 INFO mapred.MapTask: soft limit at 83886080
13/12/13 12:00:24 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
13/12/13 12:00:24 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
13/12/13 12:00:25 INFO mapred.MapTask: Starting flush of map output
This is the tail of the log which is generated when the error occurs.
13/12/13 12:00:43 INFO mapred.MapTask: Starting flush of map output
13/12/13 12:00:43 INFO mapred.Task: Task:attempt_local1170901099_0001_m_000020_0 is done. And is in the process of committing
13/12/13 12:00:43 INFO mapred.LocalJobRunner: map
13/12/13 12:00:43 INFO mapred.Task: Task 'attempt_local1170901099_0001_m_000020_0' done.
13/12/13 12:00:43 INFO mapred.LocalJobRunner: Finishing task: attempt_local1170901099_0001_m_000020_0
13/12/13 12:00:43 INFO mapred.LocalJobRunner: Map task executor complete.
13/12/13 12:00:43 WARN mapred.LocalJobRunner: job_local1170901099_0001
java.lang.Exception: java.lang.OutOfMemoryError: Java heap space
at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:403)
Caused by: java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:2786)
at org.apache.hadoop.io.Text.setCapacity(Text.java:266)
at org.apache.hadoop.io.Text.append(Text.java:236)
at org.apache.hadoop.util.LineReader.readDefaultLine(LineReader.java:238)
at org.apache.hadoop.util.LineReader.readLine(LineReader.java:174)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.nextKeyValue(LineRecordReader.java:164)
at org.apache.hadoop.mapred.MapTask$NewTrackingRecordReader.nextKeyValue(MapTask.java:532)
at org.apache.hadoop.mapreduce.task.MapContextImpl.nextKeyValue(MapContextImpl.java:80)
at org.apache.hadoop.mapreduce.lib.map.WrappedMapper$Context.nextKeyValue(WrappedMapper.java:91)
at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:144)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:763)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:339)
at org.apache.hadoop.mapred.LocalJobRunner$Job$MapTaskRunnable.run(LocalJobRunner.java:235)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:439)
at java.util.concurrent.FutureTask$Sync.innerRun(FutureTask.java:303)
at java.util.concurrent.FutureTask.run(FutureTask.java:138)
at java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:895)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:918)
at java.lang.Thread.run(Thread.java:695)
13/12/13 12:00:44 INFO mapreduce.Job: map 100% reduce 0%
13/12/13 12:00:44 INFO mapreduce.Job: Job job_local1170901099_0001 failed with state FAILED due to: NA
13/12/13 12:00:44 INFO mapreduce.Job: Counters: 22
File System Counters
FILE: Number of bytes read=27635962
FILE: Number of bytes written=28018656
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=5338170260
HDFS: Number of bytes written=0
HDFS: Number of read operations=25
HDFS: Number of large read operations=0
HDFS: Number of write operations=1
Map-Reduce Framework
Map input records=0
Map output records=0
Map output bytes=0
Map output materialized bytes=6
Input split bytes=122
Combine input records=0
Spilled Records=0
Failed Shuffles=0
Merged Map outputs=0
GC time elapsed (ms)=5
Total committed heap usage (bytes)=530186240
File Input Format Counters
Bytes Read=118909386
This answer is late, but posting it in case it helps someone else. The problem was that the file I was trying to process was corrupted. I got different copy of the file and ran my MR job on it and everything worked fine.
My first impulse would be to ask what your startup parameters are. Typically, when you run MapReduce and experience an out-of-memory error, you would use something like the following as your startup params:
-Dmapred.map.child.java.opts=-Xmx1G -Dmapred.reduce.child.java.opts=-Xmx1G
The key here is that these two amounts are cumulative. So, the amounts you specificy added together should not come close to exceeding the memory available on your system after you start MapReduce.
Might be late but i solved this by setting the following parameter to 0.2
mapred.job.shuffle.input.buffer.percent
This tells the reducer JVM in the shuffle space to ask only 0.2 % of the heap space,rather than 0.7%.You are getting "Out of heap space" error because the shuffle space is asking the JVM for memory which is not available to it.Rather than spilling it just throws the exception.But if you ask only for 0.2% chances are you will get the memory.Also once you exceed the alloted memory the spilling logic comes into picture.
Ofcourse the downside is the slowless.
You can also calculate at run-time the amount of memory available and then reset the buffer.
Related
I've recently started learning how to use the Hadoop system, and decided it's time to try writing some code. Before that, I wanted to try running the examples seen in the Getting Started page. However, it does not seem to produce any visible results.
I'm currently using Hadoop version 3.3.1 using a single-node setup,
and using jdk 11.0.11. I am running this on Windows 10 (due to current development requirements).
I've used the following command on cmd:
hadoop jar %hadoop_home%/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.1.jar grep input /output 'dfs[a-z.]+'
The output to the command:
C:\Windows\system32>hadoop jar %hadoop_home%/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.1.jar grep input /output 'dfs[a-z.]+'
2021-12-15 00:33:10,486 INFO client.DefaultNoHARMFailoverProxyProvider: Connecting to ResourceManager at /0.0.0.0:8032
2021-12-15 00:33:10,800 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/E/.staging/job_1639519343908_0005
2021-12-15 00:33:11,029 INFO input.FileInputFormat: Total input files to process : 10
2021-12-15 00:33:11,108 INFO mapreduce.JobSubmitter: number of splits:10
2021-12-15 00:33:11,281 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1639519343908_0005
2021-12-15 00:33:11,281 INFO mapreduce.JobSubmitter: Executing with tokens: []
2021-12-15 00:33:11,442 INFO conf.Configuration: resource-types.xml not found
2021-12-15 00:33:11,443 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
2021-12-15 00:33:11,497 INFO impl.YarnClientImpl: Submitted application application_1639519343908_0005
2021-12-15 00:33:11,527 INFO mapreduce.Job: The url to track the job: http://DESKTOP-S15C716:8088/proxy/application_1639519343908_0005/
2021-12-15 00:33:11,528 INFO mapreduce.Job: Running job: job_1639519343908_0005
2021-12-15 00:33:19,611 INFO mapreduce.Job: Job job_1639519343908_0005 running in uber mode : false
2021-12-15 00:33:19,615 INFO mapreduce.Job: map 0% reduce 0%
2021-12-15 00:33:31,178 INFO mapreduce.Job: map 50% reduce 0%
2021-12-15 00:33:32,263 INFO mapreduce.Job: map 60% reduce 0%
2021-12-15 00:33:39,624 INFO mapreduce.Job: map 90% reduce 0%
2021-12-15 00:33:40,632 INFO mapreduce.Job: map 100% reduce 0%
2021-12-15 00:33:41,636 INFO mapreduce.Job: map 100% reduce 100%
2021-12-15 00:33:41,648 INFO mapreduce.Job: Job job_1639519343908_0005 completed successfully
2021-12-15 00:33:41,760 INFO mapreduce.Job: Counters: 51
File System Counters
FILE: Number of bytes read=6
FILE: Number of bytes written=3021766
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=31877
HDFS: Number of bytes written=86
HDFS: Number of read operations=35
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
HDFS: Number of bytes read erasure-coded=0
Job Counters
Killed map tasks=1
Launched map tasks=10
Launched reduce tasks=1
Data-local map tasks=10
Total time spent by all maps in occupied slots (ms)=89653
Total time spent by all reduces in occupied slots (ms)=8222
Total time spent by all map tasks (ms)=89653
Total time spent by all reduce tasks (ms)=8222
Total vcore-milliseconds taken by all map tasks=89653
Total vcore-milliseconds taken by all reduce tasks=8222
Total megabyte-milliseconds taken by all map tasks=91804672
Total megabyte-milliseconds taken by all reduce tasks=8419328
Map-Reduce Framework
Map input records=819
Map output records=0
Map output bytes=0
Map output materialized bytes=60
Input split bytes=1139
Combine input records=0
Combine output records=0
Reduce input groups=0
Reduce shuffle bytes=60
Reduce input records=0
Reduce output records=0
Spilled Records=0
Shuffled Maps =10
Failed Shuffles=0
Merged Map outputs=10
GC time elapsed (ms)=90
CPU time spent (ms)=0
Physical memory (bytes) snapshot=0
Virtual memory (bytes) snapshot=0
Total committed heap usage (bytes)=2952790016
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=30738
File Output Format Counters
Bytes Written=86
2021-12-15 00:33:41,790 INFO client.DefaultNoHARMFailoverProxyProvider: Connecting to ResourceManager at /0.0.0.0:8032
2021-12-15 00:33:41,814 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/E/.staging/job_1639519343908_0006
2021-12-15 00:33:41,855 INFO input.FileInputFormat: Total input files to process : 1
2021-12-15 00:33:41,913 INFO mapreduce.JobSubmitter: number of splits:1
2021-12-15 00:33:41,950 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1639519343908_0006
2021-12-15 00:33:41,950 INFO mapreduce.JobSubmitter: Executing with tokens: []
2021-12-15 00:33:42,179 INFO impl.YarnClientImpl: Submitted application application_1639519343908_0006
2021-12-15 00:33:42,190 INFO mapreduce.Job: The url to track the job: http://DESKTOP-S15C716:8088/proxy/application_1639519343908_0006/
2021-12-15 00:33:42,191 INFO mapreduce.Job: Running job: job_1639519343908_0006
2021-12-15 00:33:55,301 INFO mapreduce.Job: Job job_1639519343908_0006 running in uber mode : false
2021-12-15 00:33:55,302 INFO mapreduce.Job: map 0% reduce 0%
2021-12-15 00:34:00,336 INFO mapreduce.Job: map 100% reduce 0%
2021-12-15 00:34:06,366 INFO mapreduce.Job: map 100% reduce 100%
2021-12-15 00:34:07,375 INFO mapreduce.Job: Job job_1639519343908_0006 completed successfully
2021-12-15 00:34:07,404 INFO mapreduce.Job: Counters: 50
File System Counters
FILE: Number of bytes read=6
FILE: Number of bytes written=548197
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=212
HDFS: Number of bytes written=0
HDFS: Number of read operations=9
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
HDFS: Number of bytes read erasure-coded=0
Job Counters
Launched map tasks=1
Launched reduce tasks=1
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=3232
Total time spent by all reduces in occupied slots (ms)=3610
Total time spent by all map tasks (ms)=3232
Total time spent by all reduce tasks (ms)=3610
Total vcore-milliseconds taken by all map tasks=3232
Total vcore-milliseconds taken by all reduce tasks=3610
Total megabyte-milliseconds taken by all map tasks=3309568
Total megabyte-milliseconds taken by all reduce tasks=3696640
Map-Reduce Framework
Map input records=0
Map output records=0
Map output bytes=0
Map output materialized bytes=6
Input split bytes=126
Combine input records=0
Combine output records=0
Reduce input groups=0
Reduce shuffle bytes=6
Reduce input records=0
Reduce output records=0
Spilled Records=0
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=13
CPU time spent (ms)=0
Physical memory (bytes) snapshot=0
Virtual memory (bytes) snapshot=0
Total committed heap usage (bytes)=536870912
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=86
File Output Format Counters
Bytes Written=0
Yet when viewing the contents of the now-made 'output' folder,
I receive the following result:
hdfs dfs -ls /output
Found 2 items
-rw-r--r-- 1 E supergroup 0 2021-12-15 00:34 /output/_SUCCESS
-rw-r--r-- 1 E supergroup 0 2021-12-15 00:34 /output/part-r-00000
I.e. there's no data written to those files!
May anyone please assist me?
If you have no data in your HDFS input folder that matches the grep pattern 'dfs[a-z.]+', then the output will be empty
From the linked docs (which are for Unix, not Windows), make sure this command completed
bin/hdfs dfs -put %HADOOP_HOME%/etc/hadoop/*.xml input
And you can grep dfs $HADOOP_HOME/etc/hadoop/*.xml (at least on Unix) as well, locally, to verify there should be data output
While transferring data from HDFS to MySQL, a MapReduce job gets spawned. But, it gets stuck and does not get completed.
sqoop export --connect jdbc:mysql://crxy2:3306/test --username root --password 19911130 --table info --export-dir sqoop_export
I see following in the logs:
Warning: /software/sqoop-1.4.6.alpha/../hbase does not exist! HBase imports will fail.
Please set $HBASE_HOME to the root of your HBase installation.
Warning: /software/sqoop-1.4.6.alpha/../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /software/sqoop-1.4.6.alpha/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
Warning: /software/sqoop-1.4.6.alpha/../zookeeper does not exist! Accumulo imports will fail.
Please set $ZOOKEEPER_HOME to the root of your Zookeeper installation.
15/12/02 01:17:37 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
15/12/02 01:17:37 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
15/12/02 01:17:37 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
15/12/02 01:17:37 INFO tool.CodeGenTool: Beginning code generation
15/12/02 01:17:38 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `info` AS t LIMIT 1
15/12/02 01:17:38 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `info` AS t LIMIT 1
15/12/02 01:17:38 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /software/hadoop-2.6.0
Note: /tmp/sqoop-root/compile/344126e97612def1e3976c1978c2e75e/info.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
15/12/02 01:17:42 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-root/compile/344126e97612def1e3976c1978c2e75e/info.jar
15/12/02 01:17:42 INFO mapreduce.ExportJobBase: Beginning export of info
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/software/hadoop-2.6.0/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/software/hbase-0.98.8-hadoop2/lib/slf4j-log4j12-1.6.4.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
15/12/02 01:17:43 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
15/12/02 01:17:45 INFO Configuration.deprecation: mapred.reduce.tasks.speculative.execution is deprecated. Instead, use mapreduce.reduce.speculative
15/12/02 01:17:45 INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative
15/12/02 01:17:45 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
15/12/02 01:17:46 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
15/12/02 01:17:50 INFO input.FileInputFormat: Total input paths to process : 1
15/12/02 01:17:50 INFO input.FileInputFormat: Total input paths to process : 1
15/12/02 01:17:50 INFO mapreduce.JobSubmitter: number of splits:4
15/12/02 01:17:50 INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative
15/12/02 01:17:50 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1449047829255_0001
15/12/02 01:17:51 INFO impl.YarnClientImpl: Submitted application application_1449047829255_0001
15/12/02 01:17:52 INFO mapreduce.Job: The url to track the job: http://crxy2:8088/proxy/application_1449047829255_0001/
15/12/02 01:17:52 INFO mapreduce.Job: Running job: job_1449047829255_0001
15/12/02 01:18:12 INFO mapreduce.Job: Job job_1449047829255_0001 running in uber mode : false
15/12/02 01:18:12 INFO mapreduce.Job: map 0% reduce 0%
15/12/02 01:19:10 INFO mapreduce.Job: map 75% reduce 0%
15/12/02 01:19:12 INFO mapreduce.Job: map 100% reduce 0%
15/12/02 01:29:41 INFO mapreduce.Job: Task Id : attempt_1449047829255_0001_m_000001_0, Status : FAILED
AttemptID:attempt_1449047829255_0001_m_000001_0 Timed out after 600 secs
15/12/02 01:29:42 INFO mapreduce.Job: map 75% reduce 0%
15/12/02 01:29:58 INFO mapreduce.Job: map 100% reduce 0%
15/12/02 01:40:11 INFO mapreduce.Job: Task Id : attempt_1449047829255_0001_m_000001_1, Status : FAILED
AttemptID:attempt_1449047829255_0001_m_000001_1 Timed out after 600 secs
15/12/02 01:40:12 INFO mapreduce.Job: map 75% reduce 0%
15/12/02 01:40:28 INFO mapreduce.Job: map 100% reduce 0%
15/12/02 01:50:41 INFO mapreduce.Job: Task Id : attempt_1449047829255_0001_m_000001_2, Status : FAILED
AttemptID:attempt_1449047829255_0001_m_000001_2 Timed out after 600 secs
15/12/02 01:50:42 INFO mapreduce.Job: map 75% reduce 0%
15/12/02 01:51:00 INFO mapreduce.Job: map 100% reduce 0%
15/12/02 02:01:13 INFO mapreduce.Job: Job job_1449047829255_0001 failed with state FAILED due to: Task failed task_1449047829255_0001_m_000001
Job failed as tasks failed. failedMaps:1 failedReduces:0
15/12/02 02:01:13 INFO mapreduce.Job: Counters: 32
File System Counters
FILE: Number of bytes read=0
FILE: Number of bytes written=370395
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=556
HDFS: Number of bytes written=0
HDFS: Number of read operations=15
HDFS: Number of large read operations=0
HDFS: Number of write operations=0
Job Counters
Failed map tasks=4
Launched map tasks=7
Other local map tasks=3
Data-local map tasks=4
Total time spent by all maps in occupied slots (ms)=2732612
Total time spent by all reduces in occupied slots (ms)=0
Total time spent by all map tasks (ms)=2732612
Total vcore-seconds taken by all map tasks=2732612
Total megabyte-seconds taken by all map tasks=2798194688
Map-Reduce Framework
Map input records=0
Map output records=0
Input split bytes=504
Spilled Records=0
Failed Shuffles=0
Merged Map outputs=0
GC time elapsed (ms)=759
CPU time spent (ms)=5170
Physical memory (bytes) snapshot=245080064
Virtual memory (bytes) snapshot=2529026048
Total committed heap usage (bytes)=46792704
File Input Format Counters
Bytes Read=0
File Output Format Counters
Bytes Written=0
15/12/02 02:01:13 INFO mapreduce.ExportJobBase: Transferred 556 bytes in 2,607.4894 seconds (0.2132 bytes/sec)
15/12/02 02:01:13 INFO mapreduce.ExportJobBase: Exported 0 records.
15/12/02 02:01:13 ERROR tool.ExportTool: Error during export: Export job failed!
2015-12-02 08:01:15,791 INFO org.apache.hadoop.yarn.server.resourcemanager.RMAuditLogger: USER=root OPERATION=Application Finished - Succeeded TARGET=RMAppManager RESULT=SUCCESS APPID=application_1449047829255_0002
2015-12-02 08:01:15,793 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler: Application Attempt appattempt_1449047829255_0002_000001 is done. finalState=FINISHED
2015-12-02 08:01:15,793 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.AppSchedulingInfo: Application application_1449047829255_0002 requests cleared
2015-12-02 08:01:15,794 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.LeafQueue: Application removed - appId: application_1449047829255_0002 user: root queue: default #user-pending-applications: 0 #user-active-applications: 0 #queue-pending-applications: 0 #queue-active-applications: 0
2015-12-02 08:01:15,794 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.ParentQueue: Application removed - appId: application_1449047829255_0002 user: root leaf-queue of parent: root #applications: 0
2015-12-02 08:01:15,794 INFO org.apache.hadoop.yarn.server.resourcemanager.RMAppManager$ApplicationSummary: appId=application_1449047829255_0002,name=info.jar,user=root,queue=default,state=FINISHED,trackingUrl=http://crxy2:8088/proxy/application_1449047829255_0002/jobhistory/job/job_1449047829255_0002,appMasterHost=crxy2,startTime=1449069503787,finishTime=1449072069229,finalStatus=FAILED
2015-12-02 08:01:15,796 INFO org.apache.hadoop.yarn.server.resourcemanager.amlauncher.AMLauncher: Cleaning master appattempt_1449047829255_0002_000001
2015-12-02 08:01:15,873 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler: Null container completed...
2015-12-02 08:01:15,873 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler: Null container completed...
2015-12-02 08:01:16,879 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler: Null container completed...
Questioner was looking at incorrect logs. He is able to troubleshoot the issue by going through failed task logs as per the suggestion in the comments section.
My job gets stuck once the first mapper (Reducemapper2) gets complete at "map 50% reduce 0%". I tried to debug a lot and googled it as well, but I'm not able to figure out the reason. Below is the driver class.
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.MultipleInputs;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class Reducedriver {
public static void main(String args[]) throws Exception
{
if(args.length!=3)
{
System.err.println("Usage: Worddrivernewapi <input path1> <inputpath2> <output path>");
System.exit(-1);
}
Configuration conf=new Configuration();
Job job=new Job(conf,"Reducesideexample");
job.setJarByClass(Reducedriver.class);
job.setJobName("Reducedriver");
Path path1=new Path(args[0]);
Path path2=new Path(args[1]); MultipleInputs.addInputPath(job,path1,TextInputFormat.class,Reducemapper1.class);
MultipleInputs.addInputPath(job,path2,TextInputFormat.class,Reducemapper2.class);
FileOutputFormat.setOutputPath(job,new Path(args[2]));
//job.setMapperClass(Reducemapper1.class);
job.setPartitionerClass(Reducepartitioner.class);
//job.setSortComparatorClass(Reducesortcomparator.class);
job.setGroupingComparatorClass(Reducegroupcomparator.class);
job.setReducerClass(Reducereducer.class);
//job.setNumReduceTasks(0);
job.setMapOutputKeyClass(ReduceWritable.class);
job.setMapOutputValueClass(Text.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setOutputFormatClass(TextOutputFormat.class);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
Could some one help me in figuring out the issue?
This is a pseudo distributed mode with 2 mapper and reducer capacity. I had multiple successful runs in my 2 node capacity.
Log for a single mapper(Jobtracker log):
2015-05-16 11:10:56,630 INFO org.apache.hadoop.util.NativeCodeLoader: Loaded the native-hadoop library
2015-05-16 11:10:57,126 WARN org.apache.hadoop.metrics2.impl.MetricsSystemImpl: Source name ugi already exists!
2015-05-16 11:10:57,288 INFO org.apache.hadoop.util.ProcessTree: setsid exited with exit code 0
2015-05-16 11:10:57,309 INFO org.apache.hadoop.mapred.Task: Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin#42f93a98
2015-05-16 11:10:57,484 INFO org.apache.hadoop.mapred.MapTask: Processing split: hdfs://localhost:9000/user/hduser/test/mapmainfile.dat:0+40
2015-05-16 11:10:57,512 INFO org.apache.hadoop.mapred.MapTask: io.sort.mb = 100
2015-05-16 11:10:57,591 INFO org.apache.hadoop.mapred.MapTask: data buffer = 79691776/99614720
2015-05-16 11:10:57,592 INFO org.apache.hadoop.mapred.MapTask: record buffer = 262144/327680
2015-05-16 11:10:57,607 WARN org.apache.hadoop.io.compress.snappy.LoadSnappy: Snappy native library not loaded
2015-05-16 11:10:57,666 INFO org.apache.hadoop.mapred.MapTask: Starting flush of map output
2015-05-16 11:10:57,669 INFO org.apache.hadoop.mapred.MapTask: Starting flush of map output
From the terminal:
15/05/16 11:10:50 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
15/05/16 11:10:50 INFO input.FileInputFormat: Total input paths to process : 1
15/05/16 11:10:50 INFO util.NativeCodeLoader: Loaded the native-hadoop library
15/05/16 11:10:50 WARN snappy.LoadSnappy: Snappy native library not loaded
15/05/16 11:10:50 INFO input.FileInputFormat: Total input paths to process : 1
15/05/16 11:10:51 INFO mapred.JobClient: Running job: job_201505161109_0001
15/05/16 11:10:52 INFO mapred.JobClient: map 0% reduce 0%
15/05/16 11:11:04 INFO mapred.JobClient: map 100% reduce 0%.
When I tried to debug through localhost I could see that the first mapper completes and the map progress stops at 50%.
Localjobrunner log:
15/05/16 11:36:08 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/05/16 11:36:08 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
15/05/16 11:36:08 WARN mapred.JobClient: No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
15/05/16 11:36:08 INFO input.FileInputFormat: Total input paths to process : 1
15/05/16 11:36:08 WARN snappy.LoadSnappy: Snappy native library not loaded
15/05/16 11:36:08 INFO input.FileInputFormat: Total input paths to process : 1
15/05/16 11:36:08 INFO mapred.JobClient: Running job: job_local815502428_0001
15/05/16 11:36:09 INFO mapred.LocalJobRunner: Waiting for map tasks
15/05/16 11:36:09 INFO mapred.LocalJobRunner: Starting task: attempt_local815502428_0001_m_000000_0
15/05/16 11:36:09 INFO util.ProcessTree: setsid exited with exit code 0
15/05/16 11:36:09 INFO mapred.Task: Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin#11507b87
15/05/16 11:36:09 INFO mapred.MapTask: Processing split: file:/home/hduser/hadoop/myexamples/mainmapdatafile.dat:0+137
15/05/16 11:36:09 INFO mapred.MapTask: io.sort.mb = 100
15/05/16 11:36:09 INFO mapred.MapTask: data buffer = 79691776/99614720
15/05/16 11:36:09 INFO mapred.MapTask: record buffer = 262144/327680
15/05/16 11:36:09 INFO mapred.JobClient: map 0% reduce 0%
15/05/16 11:36:18 INFO mapred.LocalJobRunner:
15/05/16 11:36:18 INFO mapred.JobClient: map 6% reduce 0%
15/05/16 11:36:27 INFO mapred.LocalJobRunner:
15/05/16 11:36:28 INFO mapred.JobClient: map 12% reduce 0%
15/05/16 11:36:36 INFO mapred.LocalJobRunner:
15/05/16 11:36:37 INFO mapred.JobClient: map 18% reduce 0%
15/05/16 11:36:45 INFO mapred.LocalJobRunner:
15/05/16 11:36:46 INFO mapred.JobClient: map 25% reduce 0%
15/05/16 11:36:51 INFO mapred.LocalJobRunner:
15/05/16 11:36:52 INFO mapred.JobClient: map 31% reduce 0%
15/05/16 11:36:57 INFO mapred.LocalJobRunner:
15/05/16 11:36:58 INFO mapred.JobClient: map 37% reduce 0%
15/05/16 11:37:03 INFO mapred.LocalJobRunner:
15/05/16 11:37:04 INFO mapred.JobClient: map 43% reduce 0%
15/05/16 11:37:09 INFO mapred.LocalJobRunner:
15/05/16 11:37:10 INFO mapred.JobClient: map 50% reduce 0%
15/05/16 11:37:12 INFO mapred.MapTask: Starting flush of map output
15/05/16 11:37:12 INFO mapred.MapTask: Starting flush of map output
15/05/16 11:37:18 INFO mapred.LocalJobRunner:
Recently I set up the Hadoop cluster to test, the cluster has two nodes for tasks, and is based on Yarn.
I know that Hadoop is not suitable for the examples, it has a good performance in very large data level, but it's still too slow. I mean extremely slow. My input file is a document of 500,000 words, and reduce number is 2.
Here is the log:
hadoop jar /home/hadoop/hadoopTest.jar com.hadoop.WordCountJob /wordcountest /wordcountresult
Job started: Mon Dec 23 12:38:13 CST 2013
13/12/23 12:38:13 INFO service.AbstractService: Service:org.apache.hadoop.yarn.client.YarnClientImpl is inited.
13/12/23 12:38:14 INFO service.AbstractService: Service:org.apache.hadoop.yarn.client.YarnClientImpl is started.
13/12/23 12:38:14 WARN mapreduce.JobSubmitter: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
13/12/23 12:38:27 INFO input.FileInputFormat: Total input paths to process : 1
13/12/23 12:38:27 INFO mapreduce.JobSubmitter: number of splits:1
13/12/23 12:38:27 WARN conf.Configuration: mapred.jar is deprecated. Instead, use mapreduce.job.jar
13/12/23 12:38:27 WARN conf.Configuration: mapred.reduce.tasks is deprecated. Instead, use mapreduce.job.reduces
13/12/23 12:38:27 WARN conf.Configuration: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class
13/12/23 12:38:27 WARN conf.Configuration: mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class
13/12/23 12:38:27 WARN conf.Configuration: mapred.job.name is deprecated. Instead, use mapreduce.job.name
13/12/23 12:38:27 WARN conf.Configuration: mapreduce.reduce.class is deprecated. Instead, use mapreduce.job.reduce.class
13/12/23 12:38:27 WARN conf.Configuration: mapreduce.inputformat.class is deprecated. Instead, use mapreduce.job.inputformat.class
13/12/23 12:38:27 WARN conf.Configuration: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir
13/12/23 12:38:27 WARN conf.Configuration: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir
13/12/23 12:38:27 WARN conf.Configuration: mapreduce.outputformat.class is deprecated. Instead, use mapreduce.job.outputformat.class
13/12/23 12:38:27 WARN conf.Configuration: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
13/12/23 12:38:27 WARN conf.Configuration: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class
13/12/23 12:38:27 WARN conf.Configuration: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir
13/12/23 12:38:29 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1383617275312_0021
13/12/23 12:38:30 INFO client.YarnClientImpl: Submitted application application_1383617275312_0021 to ResourceManager at Hadoop1/111.11.11.11:8032
13/12/23 12:38:30 INFO mapreduce.Job: The url to track the job: http://kmHadoop1:8088/proxy/application_1383617275312_0021/
13/12/23 12:38:30 INFO mapreduce.Job: Running job: job_1383617275312_0021
13/12/23 12:43:22 INFO mapreduce.Job: Job job_1383617275312_0021 running in uber mode : false
13/12/23 12:43:22 INFO mapreduce.Job: map 0% reduce 0%
13/12/23 13:03:37 INFO mapreduce.Job: map 67% reduce 0%
13/12/23 13:03:43 INFO mapreduce.Job: map 100% reduce 0%
13/12/23 13:07:04 INFO mapreduce.Job: map 100% reduce 37%
13/12/23 13:07:07 INFO mapreduce.Job: map 100% reduce 51%
13/12/23 13:07:10 INFO mapreduce.Job: map 100% reduce 67%
13/12/23 13:07:51 INFO mapreduce.Job: map 100% reduce 69%
13/12/23 13:07:52 INFO mapreduce.Job: map 100% reduce 70%
13/12/23 13:07:54 INFO mapreduce.Job: map 100% reduce 85%
13/12/23 13:07:54 INFO mapreduce.Job: map 100% reduce 100%
13/12/23 13:07:54 INFO mapreduce.Job: Job job_1383617275312_0021 completed successfully
13/12/23 13:07:55 INFO mapreduce.Job: Counters: 43
File System Counters
FILE: Number of bytes read=519233
FILE: Number of bytes written=1254635
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=2356520
HDFS: Number of bytes written=427594
HDFS: Number of read operations=9
HDFS: Number of large read operations=0
HDFS: Number of write operations=4
Job Counters
Launched map tasks=1
Launched reduce tasks=2
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=1225928
Total time spent by all reduces in occupied slots (ms)=495508
Map-Reduce Framework
Map input records=8646
Map output records=420146
Map output bytes=4187027
Map output materialized bytes=519225
Input split bytes=122
Combine input records=0
Combine output records=0
Reduce input groups=35430
Reduce shuffle bytes=519225
Reduce input records=420146
Reduce output records=35430
Spilled Records=840292
Shuffled Maps =2
Failed Shuffles=0
Merged Map outputs=2
GC time elapsed (ms)=263996
CPU time spent (ms)=222750
Physical memory (bytes) snapshot=529215488
Virtual memory (bytes) snapshot=4047876096
Total committed heap usage (bytes)=479268864
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=2356398
File Output Format Counters
Bytes Written=427594
Job ended: Mon Dec 23 13:07:55 CST 2013
The job took 1782 seconds.
We can see the timestamp before each line of the log.
It seems it is slow at every step : init, check the input path, launch on the Yarn , Mapreduce, etc.
And it took 1783 seconds for the entire process.
What happened ? Did I do something wrong ?
My hadoop version is CDH4.3.0 , 2 nodes for the cluster. And there are thousands small files in the Hdfs, Is that a problem?
I see from your output
Map output bytes=4187027
Map output materialized bytes=519225
...
that you are doing compression on (at least) the intermediate map output data. You might try rerunning your example with compression turned off; GZIP compression is notoriously taxing on your machines' processors. Maybe before you turn off compression you might consider monitoring your CPU load to verify that this is indeed your bottleneck.
I've seen excessively long job times when running clusters that are 2 or 3 nodes with GZIP compression turned on. This changes as you start adding nodes. When I scaled that cluster up to 10 nodes and reran the same job, compression had actually become highly beneficial (to the tune of about a 40% improvement on a 100GB Terasort's overall job time versus not using compression).
This is my first SO post so please let me know if I've missed out anything important. I am a Mahout/Hadoop beginner, and am trying to put together a distributed recommendation engine.
In order to simulate working on a remote cluster, I have set up hadoop on my machine to communicate with a Ubuntu VM (using VirtualBox), also located on my machine, which has hadoop installed on it. This setup seems to be working fine and I am now trying to run Mahout's 'RecommenderJob' on a (very!) small trial dataset as a test.
The input consists of a .csv file (saved on the hadoop dfs) containing around 50 user preferences in the format: userID, itemID, preference ... and the command I am running is:
hadoop jar /Users/MyName/src/trunk/core/target/mahout-core-0.8-SNAPSHOT-job.jar org.apache.mahout.cf.taste.hadoop.item.RecommenderJob -Dmapred.input.dir=/user/MyName/Recommendations/input/TestRatings.csv -Dmapred.output.dir=/user/MyName/Recommendations/output -s SIMILARITY_PEARSON_CORELLATION
where TestRatings.csv is the file containing the preferences and output is the desired output directory.
At first the job looks like it's running fine, and I get the following output:
12/12/11 12:26:21 INFO common.AbstractJob: Command line arguments: {--booleanData=[false], --endPhase=[2147483647], --maxPrefsPerUser=[10], --maxPrefsPerUserInItemSimilarity=[1000], --maxSimilaritiesPerItem=[100], --minPrefsPerUser=[1], --numRecommendations=[10], --similarityClassname=[SIMILARITY_PEARSON_CORELLATION], --startPhase=[0], --tempDir=[temp]}
12/12/11 12:26:21 INFO common.AbstractJob: Command line arguments: {--booleanData=[false], --endPhase=[2147483647], --input=[/user/Naaman/Delphi/input/TestRatings.csv], --maxPrefsPerUser=[1000], --minPrefsPerUser=[1], --output=[temp/preparePreferenceMatrix], --ratingShift=[0.0], --startPhase=[0], --tempDir=[temp]}
12/12/11 12:26:21 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
12/12/11 12:26:21 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
12/12/11 12:26:22 INFO input.FileInputFormat: Total input paths to process : 1
12/12/11 12:26:22 WARN snappy.LoadSnappy: Snappy native library not loaded
12/12/11 12:26:22 INFO mapred.JobClient: Running job: job_local_0001
12/12/11 12:26:22 INFO mapred.Task: Using ResourceCalculatorPlugin : null
12/12/11 12:26:22 INFO mapred.MapTask: io.sort.mb = 100
12/12/11 12:26:22 INFO mapred.MapTask: data buffer = 79691776/99614720
12/12/11 12:26:22 INFO mapred.MapTask: record buffer = 262144/327680
12/12/11 12:26:22 INFO mapred.MapTask: Starting flush of map output
12/12/11 12:26:22 INFO compress.CodecPool: Got brand-new compressor
12/12/11 12:26:22 INFO mapred.MapTask: Finished spill 0
12/12/11 12:26:22 INFO mapred.Task: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
12/12/11 12:26:22 INFO mapred.LocalJobRunner:
12/12/11 12:26:22 INFO mapred.Task: Task 'attempt_local_0001_m_000000_0' done.
12/12/11 12:26:22 INFO mapred.Task: Using ResourceCalculatorPlugin : null
12/12/11 12:26:22 INFO mapred.ReduceTask: ShuffleRamManager: MemoryLimit=1491035776, MaxSingleShuffleLimit=372758944
12/12/11 12:26:22 INFO compress.CodecPool: Got brand-new decompressor
12/12/11 12:26:22 INFO compress.CodecPool: Got brand-new decompressor
12/12/11 12:26:22 INFO compress.CodecPool: Got brand-new decompressor
12/12/11 12:26:22 INFO compress.CodecPool: Got brand-new decompressor
12/12/11 12:26:22 INFO compress.CodecPool: Got brand-new decompressor
12/12/11 12:26:22 INFO mapred.ReduceTask: attempt_local_0001_r_000000_0 Thread started: Thread for merging on-disk files
12/12/11 12:26:22 INFO mapred.ReduceTask: attempt_local_0001_r_000000_0 Thread started: Thread for merging in memory files
12/12/11 12:26:22 INFO mapred.ReduceTask: attempt_local_0001_r_000000_0 Thread waiting: Thread for merging on-disk files
12/12/11 12:26:22 INFO mapred.ReduceTask: attempt_local_0001_r_000000_0 Need another 1 map output(s) where 0 is already in progress
12/12/11 12:26:22 INFO mapred.ReduceTask: attempt_local_0001_r_000000_0 Thread started: Thread for polling Map Completion Events
12/12/11 12:26:22 INFO mapred.ReduceTask: attempt_local_0001_r_000000_0 Scheduled 0 outputs (0 slow hosts and0 dup hosts)
12/12/11 12:26:23 INFO mapred.JobClient: map 100% reduce 0%
12/12/11 12:26:28 INFO mapred.LocalJobRunner: reduce > copy >
12/12/11 12:26:31 INFO mapred.LocalJobRunner: reduce > copy >
12/12/11 12:26:37 INFO mapred.LocalJobRunner: reduce > copy >
But then the last three lines repeat indefinitely (I left it overnight...), with the two lines:
12/12/11 12:27:22 INFO mapred.ReduceTask: attempt_local_0001_r_000000_0 Need another 1 map output(s) where 0 is already in progress
12/12/11 12:27:22 INFO mapred.ReduceTask: attempt_local_0001_r_000000_0 Scheduled 0 outputs (0 slow hosts and0 dup hosts)
repeating every twelve rows.
I'm not sure whether there's something wrong with my input, or whether the tiny size of the trial data is messing things up. Any help and/or advice on the best way to go about this would be much appreciated.
p.s. I was trying to follow the instructions from https://www.box.com/s/041rdjeh7sny128r2uki
This is really a Hadoop or cluster issue. It is waiting on mapper output that is not coming. Look for earlier failures, in the mapping phase.