I'm running Nutch 2.3 on EMR (AMI version 2.4.2). The crawl steps are working fine in local and distributed mode (hadoop -jar apache-nutch-2.3.job <MainClass> <args>), and am able to call the steps by spinning up the rest service in local mode. But, when I try to run the rest in distributed mode (hadoop -jar apache-nutch-2.3.job org.apache.nutch.api.NutchServer), the rest is receiving the calls, but is not getting the job done. What is the correct way to run nutch in distributed mode?
Info
When the InjectorJob is run offline in a distributed mode, the output is as follows:
COMMAND:
hadoop jar ./apache-nutch-2.3.job org.apache.nutch.crawl.InjectorJob s3://myemrbucket/urls -crawlId 2
15/11/19 09:55:06 INFO crawl.InjectorJob: InjectorJob: starting at 2015-11-19 09:55:06
15/11/19 09:55:06 INFO crawl.InjectorJob: InjectorJob: Injecting urlDir: s3://myemrbucket/urls
15/11/19 09:55:06 INFO s3native.NativeS3FileSystem: Created AmazonS3 with InstanceProfileCredentialsProvider
15/11/19 09:55:08 WARN store.HBaseStore: Mismatching schema's names. Mappingfile schema: 'webpage'. PersistentClass schema's name: '2_webpage'Assuming they are the same.
15/11/19 09:55:08 INFO crawl.InjectorJob: InjectorJob: Using class org.apache.gora.hbase.store.HBaseStore as the Gora storage class.
15/11/19 09:55:08 INFO mapred.JobClient: Default number of map tasks: null
15/11/19 09:55:08 INFO mapred.JobClient: Setting default number of map tasks based on cluster size to : 4
15/11/19 09:55:08 INFO mapred.JobClient: Default number of reduce tasks: 0
15/11/19 09:55:10 INFO security.ShellBasedUnixGroupsMapping: add hadoop to shell userGroupsCache
15/11/19 09:55:10 INFO mapred.JobClient: Setting group to hadoop
15/11/19 09:55:10 INFO input.FileInputFormat: Total input paths to process : 1
15/11/19 09:55:10 INFO lzo.GPLNativeCodeLoader: Loaded native gpl library
15/11/19 09:55:10 WARN lzo.LzoCodec: Could not find build properties file with revision hash
15/11/19 09:55:10 INFO lzo.LzoCodec: Successfully loaded & initialized native-lzo library [hadoop-lzo rev UNKNOWN]
15/11/19 09:55:10 WARN snappy.LoadSnappy: Snappy native library is available
15/11/19 09:55:10 INFO snappy.LoadSnappy: Snappy native library loaded
15/11/19 09:55:10 INFO mapred.JobClient: Running job: job_201511182052_0037
15/11/19 09:55:11 INFO mapred.JobClient: map 0% reduce 0%
15/11/19 09:55:38 INFO mapred.JobClient: map 100% reduce 0%
15/11/19 09:55:43 INFO mapred.JobClient: Job complete: job_201511182052_0037
15/11/19 09:55:43 INFO mapred.JobClient: Counters: 20
15/11/19 09:55:43 INFO mapred.JobClient: Job Counters
15/11/19 09:55:43 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=16424
15/11/19 09:55:43 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0
15/11/19 09:55:43 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0
15/11/19 09:55:43 INFO mapred.JobClient: Rack-local map tasks=1
15/11/19 09:55:43 INFO mapred.JobClient: Launched map tasks=1
15/11/19 09:55:43 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0
15/11/19 09:55:43 INFO mapred.JobClient: File Output Format Counters
15/11/19 09:55:43 INFO mapred.JobClient: Bytes Written=0
15/11/19 09:55:43 INFO mapred.JobClient: injector
15/11/19 09:55:43 INFO mapred.JobClient: urls_injected=1
15/11/19 09:55:43 INFO mapred.JobClient: FileSystemCounters
15/11/19 09:55:43 INFO mapred.JobClient: HDFS_BYTES_READ=98
15/11/19 09:55:43 INFO mapred.JobClient: S3_BYTES_READ=61
15/11/19 09:55:43 INFO mapred.JobClient: FILE_BYTES_WRITTEN=36254
15/11/19 09:55:43 INFO mapred.JobClient: File Input Format Counters
15/11/19 09:55:43 INFO mapred.JobClient: Bytes Read=61
15/11/19 09:55:43 INFO mapred.JobClient: Map-Reduce Framework
15/11/19 09:55:43 INFO mapred.JobClient: Map input records=1
15/11/19 09:55:43 INFO mapred.JobClient: Physical memory (bytes) snapshot=193712128
15/11/19 09:55:43 INFO mapred.JobClient: Spilled Records=0
15/11/19 09:55:43 INFO mapred.JobClient: CPU time spent (ms)=3960
15/11/19 09:55:43 INFO mapred.JobClient: Total committed heap usage (bytes)=298319872
15/11/19 09:55:43 INFO mapred.JobClient: Virtual memory (bytes) snapshot=1525059584
15/11/19 09:55:43 INFO mapred.JobClient: Map output records=1
15/11/19 09:55:43 INFO mapred.JobClient: SPLIT_RAW_BYTES=98
15/11/19 09:55:44 INFO crawl.InjectorJob: InjectorJob: total number of urls rejected by filters: 0
15/11/19 09:55:44 INFO crawl.InjectorJob: InjectorJob: total number of urls injected after normalization and filtering: 1
15/11/19 09:55:44 INFO crawl.InjectorJob: Injector: finished at 2015-11-19 09:55:44, elapsed: 00:00:38
By calling it through the REST, the job gets stuck after giving out the following output:
POST ARGS:
{
"crawlId":"11",
"confId":"default",
"type":"INJECT",
"args":{"seedDir":"s3://myemrbucket/urls"}
}
15/11/19 09:46:14 INFO api.NutchServer: Starting NutchServer on port: 8081 with logging level: INFO ...
Nov 19, 2015 9:46:14 AM org.restlet.engine.connector.NetServerHelper start
INFO: Starting the internal [HTTP/1.1] server on port 8081
15/11/19 09:46:14 INFO api.NutchServer: Started NutchServer on port 8081
Nov 19, 2015 9:46:25 AM org.restlet.engine.log.LogFilter afterHandle
INFO: 2015-11-19 09:46:25 1xx.xx.x.xx - - 8081 POST /job/create - 200 28 110 498 http://ec2-xx-xxx-xxx-xx.compute-1.amazonaws.com:8081 Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.80 Safari/537.36-
15/11/19 09:46:25 INFO s3native.NativeS3FileSystem: Created AmazonS3 with InstanceProfileCredentialsProvider
15/11/19 09:46:27 WARN store.HBaseStore: Mismatching schema's names. Mappingfile schema: 'webpage'. PersistentClass schema's name: '11_webpage'Assuming they are the same.
15/11/19 09:46:28 INFO crawl.InjectorJob: InjectorJob: Using class org.apache.gora.hbase.store.HBaseStore as the Gora storage class.
15/11/19 09:46:28 INFO mapred.JobClient: Default number of map tasks: null
15/11/19 09:46:28 INFO mapred.JobClient: Setting default number of map tasks based on cluster size to : 4
15/11/19 09:46:28 INFO mapred.JobClient: Default number of reduce tasks: 0
15/11/19 09:46:28 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
and does not move ahead.
Related
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:
I have a small JSON file in two separate folders in my S3 bucket. I ran the same command with the same mapper on those two separately.
NORMAL JSON
$ hadoop jar /home/hadoop/contrib/streaming/hadoop-streaming-1.0.3.jar -Dmapred.reduce.tasks=0 -file ./mapper.py -mapper ./mapper.py -input s3://mybucket/normaltest -output smalltest-output
14/08/28 08:33:53 WARN conf.Configuration: DEPRECATED: hadoop-site.xml found in the classpath. Usage of hadoop-site.xml is deprecated. Instead use core-site.xml, mapred-site.xml and hdfs-site.xml to override properties of core-default.xml, mapred-default.xml and hdfs-default.xml respectively
packageJobJar: [./mapper.py, /mnt/var/lib/hadoop/tmp/hadoop-unjar6225144044327095484/] [] /tmp/streamjob6947060448653690043.jar tmpDir=null
14/08/28 08:33:56 INFO mapred.JobClient: Default number of map tasks: null
14/08/28 08:33:56 INFO mapred.JobClient: Setting default number of map tasks based on cluster size to : 160
14/08/28 08:33:56 INFO mapred.JobClient: Default number of reduce tasks: 0
14/08/28 08:33:56 INFO security.ShellBasedUnixGroupsMapping: add hadoop to shell userGroupsCache
14/08/28 08:33:56 INFO mapred.JobClient: Setting group to hadoop
14/08/28 08:33:56 INFO lzo.GPLNativeCodeLoader: Loaded native gpl library
14/08/28 08:33:56 WARN lzo.LzoCodec: Could not find build properties file with revision hash
14/08/28 08:33:56 INFO lzo.LzoCodec: Successfully loaded & initialized native-lzo library [hadoop-lzo rev UNKNOWN]
14/08/28 08:33:56 WARN snappy.LoadSnappy: Snappy native library is available
14/08/28 08:33:56 INFO snappy.LoadSnappy: Snappy native library loaded
14/08/28 08:33:58 INFO mapred.FileInputFormat: Total input paths to process : 1
14/08/28 08:33:58 INFO streaming.StreamJob: getLocalDirs(): [/mnt/var/lib/hadoop/mapred]
14/08/28 08:33:58 INFO streaming.StreamJob: Running job: job_201408260907_0053
14/08/28 08:33:58 INFO streaming.StreamJob: To kill this job, run:
14/08/28 08:33:58 INFO streaming.StreamJob: /home/hadoop/bin/hadoop job -Dmapred.job.tracker=10.165.13.124:9001 -kill job_201408260907_0053
14/08/28 08:33:58 INFO streaming.StreamJob: Tracking URL: http://ip-10-165-13-124.ec2.internal:9100/jobdetails.jsp?jobid=job_201408260907_0053
14/08/28 08:33:59 INFO streaming.StreamJob: map 0% reduce 0%
14/08/28 08:34:23 INFO streaming.StreamJob: map 1% reduce 0%
14/08/28 08:34:26 INFO streaming.StreamJob: map 2% reduce 0%
14/08/28 08:34:29 INFO streaming.StreamJob: map 9% reduce 0%
14/08/28 08:34:32 INFO streaming.StreamJob: map 45% reduce 0%
14/08/28 08:34:35 INFO streaming.StreamJob: map 56% reduce 0%
14/08/28 08:34:36 INFO streaming.StreamJob: map 57% reduce 0%
14/08/28 08:34:38 INFO streaming.StreamJob: map 84% reduce 0%
14/08/28 08:34:39 INFO streaming.StreamJob: map 85% reduce 0%
14/08/28 08:34:41 INFO streaming.StreamJob: map 99% reduce 0%
14/08/28 08:34:44 INFO streaming.StreamJob: map 100% reduce 0%
14/08/28 08:34:50 INFO streaming.StreamJob: map 100% reduce 100%
14/08/28 08:34:50 INFO streaming.StreamJob: Job complete: job_201408260907_0053
14/08/28 08:34:50 INFO streaming.StreamJob: Output: smalltest-output
In smalltest-output, I get several small files containing a part of the processed JSON.
GZIPed JSON
$ hadoop jar /home/hadoop/contrib/streaming/hadoop-streaming-1.0.3.jar -Dmapred.reduce.tasks=0 -file ./mapper.py -mapper ./mapper.py -input s3://weblablatency/gztest -output smalltest-output
14/08/28 08:39:45 WARN conf.Configuration: DEPRECATED: hadoop-site.xml found in the classpath. Usage of hadoop-site.xml is deprecated. Instead use core-site.xml, mapred-site.xml and hdfs-site.xml to override properties of core-default.xml, mapred-default.xml and hdfs-default.xml respectively
packageJobJar: [./mapper.py, /mnt/var/lib/hadoop/tmp/hadoop-unjar2539293594337011579/] [] /tmp/streamjob301144784484156113.jar tmpDir=null
14/08/28 08:39:48 INFO mapred.JobClient: Default number of map tasks: null
14/08/28 08:39:48 INFO mapred.JobClient: Setting default number of map tasks based on cluster size to : 160
14/08/28 08:39:48 INFO mapred.JobClient: Default number of reduce tasks: 0
14/08/28 08:39:48 INFO security.ShellBasedUnixGroupsMapping: add hadoop to shell userGroupsCache
14/08/28 08:39:48 INFO mapred.JobClient: Setting group to hadoop
14/08/28 08:39:48 INFO lzo.GPLNativeCodeLoader: Loaded native gpl library
14/08/28 08:39:48 WARN lzo.LzoCodec: Could not find build properties file with revision hash
14/08/28 08:39:48 INFO lzo.LzoCodec: Successfully loaded & initialized native-lzo library [hadoop-lzo rev UNKNOWN]
14/08/28 08:39:48 WARN snappy.LoadSnappy: Snappy native library is available
14/08/28 08:39:48 INFO snappy.LoadSnappy: Snappy native library loaded
14/08/28 08:39:50 INFO mapred.FileInputFormat: Total input paths to process : 1
14/08/28 08:39:51 INFO streaming.StreamJob: getLocalDirs(): [/mnt/var/lib/hadoop/mapred]
14/08/28 08:39:51 INFO streaming.StreamJob: Running job: job_201408260907_0055
14/08/28 08:39:51 INFO streaming.StreamJob: To kill this job, run:
14/08/28 08:39:51 INFO streaming.StreamJob: /home/hadoop/bin/hadoop job -Dmapred.job.tracker=10.165.13.124:9001 -kill job_201408260907_0055
14/08/28 08:39:51 INFO streaming.StreamJob: Tracking URL: http://ip-10-165-13-124.ec2.internal:9100/jobdetails.jsp?jobid=job_201408260907_0055
14/08/28 08:39:52 INFO streaming.StreamJob: map 0% reduce 0%
14/08/28 08:40:20 INFO streaming.StreamJob: map 100% reduce 0%
14/08/28 08:40:26 INFO streaming.StreamJob: map 100% reduce 100%
14/08/28 08:40:26 INFO streaming.StreamJob: Job complete: job_201408260907_0055
In smalltest-output I get a correctly parsed file, but as a single file.
Why this difference and what is happening? Is my job not being distributed properly in the gz case?
In my actual use case I need to process ~2000 gz files totalling to around 4GB uncompressed; every 4 hours. So I can't afford any performance issues because of compression.
Gzip is not splittable. You will find bazillions of articles and questions speaking about this issue so I won't go into details.
Your options are:
Don't use Gzip (don't compress or use another splittable compression format)
Use a hack to make GZip splittable, like https://github.com/nielsbasjes/splittablegzip. Each mapper will still have to read the file from the beginning so it's a trade-off. Read the documentation to learn more.
It depends on what you do, but for most processing 4GB of data is nothing. I would make sure that I really need an elephant like Hadoop for my use case. It is scalable but complex, painful to work and usually slow for small data sets.
I have successfully configured Hadoop 2.4 on my Ubuntu 14.04 using this tutorial.
http://dogdogfish.com/2014/04/26/installing-hadoop-2-4-on-ubuntu-14-04/
Now after completing installtion how can I perform test on it?
How and where can I get the test data or jar files?
You have some example jars in your hadoop installation directory.
Simplest thing you can do is run the teragen example(or wordcount).
It is the first step in perform terasort.
Steps:
Go to the hadoop installation directory.
Run "hadoop jar hadoop-examples-0.20.2-cdh3u0.jar" to see all the jars you can run.
Go to home/[user] directory and create a file "example.txt" with the following data
"This is a file to test Hadoop Installation example
For the sake of the experiment, consider it to be 1TB"
While you are in that directory, run "hadoop dfs -put examples.txt /" this uploads the file onto your HDFS
Run "hadoop dfs -ls /" to check it is on there
Go to your Hadoop installation directory and run "hadoop jar hadoop-examples-0.20.2-cdh3u0.jar teragen 1000 /user/teragendata" - 1000 is the size data is to be broken into and the other param is the output directory.
On successful execution, you will see something like the text at the bottom.
Now to see how your MR job was run, in your browser open JobTracker and see the completed jobs. "localhost50030/jobtracker.jsp"
cloudera#cloudera-vm:/usr/lib/hadoop$ hadoop jar hadoop-examples-0.20.2-cdh3u0.jar teragen 600 /user/teragendata
Generating 600 using 2 maps with step of 300
14/07/24 09:02:44 INFO mapred.JobClient: Running job: job_201407230030_0008
14/07/24 09:02:45 INFO mapred.JobClient: map 0% reduce 0%
14/07/24 09:02:57 INFO mapred.JobClient: map 100% reduce 0%
14/07/24 09:03:00 INFO mapred.JobClient: Job complete: job_201407230030_0008
14/07/24 09:03:00 INFO mapred.JobClient: Counters: 13
14/07/24 09:03:00 INFO mapred.JobClient: Job Counters
14/07/24 09:03:00 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=22008
14/07/24 09:03:00 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0
14/07/24 09:03:00 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0
14/07/24 09:03:00 INFO mapred.JobClient: Launched map tasks=2
14/07/24 09:03:00 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=0
14/07/24 09:03:00 INFO mapred.JobClient: FileSystemCounters
14/07/24 09:03:00 INFO mapred.JobClient: HDFS_BYTES_READ=164
14/07/24 09:03:00 INFO mapred.JobClient: FILE_BYTES_WRITTEN=105150
14/07/24 09:03:00 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=60000
14/07/24 09:03:00 INFO mapred.JobClient: Map-Reduce Framework
14/07/24 09:03:00 INFO mapred.JobClient: Map input records=600
14/07/24 09:03:00 INFO mapred.JobClient: Spilled Records=0
14/07/24 09:03:00 INFO mapred.JobClient: Map input bytes=600
14/07/24 09:03:00 INFO mapred.JobClient: Map output records=600
14/07/24 09:03:00 INFO mapred.JobClient: SPLIT_RAW_BYTES=164
I'm running Apache's Hadoop, and using the grep example provided by that installation. I'm wondering why map reduce percentages show up running twice? I thought they only had to run once; which makes me doubt my understanding of map reduce. I looked it up (http://grokbase.com/t/gg/mongodb-user/125ay1eazq/map-reduce-percentage-seems-running-twice) but there really wasn't an explanation and this link was for MongoDB.
hduser#ubse1:/usr/local/hadoop$ bin/hadoop jar hadoop*examples*.jar grep /user/hduser/grep /user/hduser/grep-output4 ".*woe is me.*"
I'm running this on a project gutenberg .txt file. The output file is correct.
Here is the output for running the command if needed:
12/08/06 06:56:57 INFO util.NativeCodeLoader: Loaded the native-hadoop library
12/08/06 06:56:57 WARN snappy.LoadSnappy: Snappy native library not loaded
12/08/06 06:56:57 INFO mapred.FileInputFormat: Total input paths to process : 1
12/08/06 06:56:58 INFO mapred.JobClient: Running job: job_201208030925_0011
12/08/06 06:56:59 INFO mapred.JobClient: map 0% reduce 0%
12/08/06 06:57:18 INFO mapred.JobClient: map 100% reduce 0%
12/08/06 06:57:30 INFO mapred.JobClient: map 100% reduce 100%
12/08/06 06:57:35 INFO mapred.JobClient: Job complete: job_201208030925_0011
12/08/06 06:57:35 INFO mapred.JobClient: Counters: 30
12/08/06 06:57:35 INFO mapred.JobClient: Job Counters
12/08/06 06:57:35 INFO mapred.JobClient: Launched reduce tasks=1
12/08/06 06:57:35 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=31034
12/08/06 06:57:35 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0
12/08/06 06:57:35 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0
12/08/06 06:57:35 INFO mapred.JobClient: Rack-local map tasks=2
12/08/06 06:57:35 INFO mapred.JobClient: Launched map tasks=2
12/08/06 06:57:35 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=11233
12/08/06 06:57:35 INFO mapred.JobClient: File Input Format Counters
12/08/06 06:57:35 INFO mapred.JobClient: Bytes Read=5592666
12/08/06 06:57:35 INFO mapred.JobClient: File Output Format Counters
12/08/06 06:57:35 INFO mapred.JobClient: Bytes Written=391
12/08/06 06:57:35 INFO mapred.JobClient: FileSystemCounters
12/08/06 06:57:35 INFO mapred.JobClient: FILE_BYTES_READ=281
12/08/06 06:57:35 INFO mapred.JobClient: HDFS_BYTES_READ=5592862
12/08/06 06:57:35 INFO mapred.JobClient: FILE_BYTES_WRITTEN=65331
12/08/06 06:57:35 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=391
12/08/06 06:57:35 INFO mapred.JobClient: Map-Reduce Framework
12/08/06 06:57:35 INFO mapred.JobClient: Map output materialized bytes=287
12/08/06 06:57:35 INFO mapred.JobClient: Map input records=124796
12/08/06 06:57:35 INFO mapred.JobClient: Reduce shuffle bytes=287
12/08/06 06:57:35 INFO mapred.JobClient: Spilled Records=10
12/08/06 06:57:35 INFO mapred.JobClient: Map output bytes=265
12/08/06 06:57:35 INFO mapred.JobClient: Total committed heap usage (bytes)=336404480
12/08/06 06:57:35 INFO mapred.JobClient: CPU time spent (ms)=7040
12/08/06 06:57:35 INFO mapred.JobClient: Map input bytes=5590193
12/08/06 06:57:35 INFO mapred.JobClient: SPLIT_RAW_BYTES=196
12/08/06 06:57:35 INFO mapred.JobClient: Combine input records=5
12/08/06 06:57:35 INFO mapred.JobClient: Reduce input records=5
12/08/06 06:57:35 INFO mapred.JobClient: Reduce input groups=5
12/08/06 06:57:35 INFO mapred.JobClient: Combine output records=5
12/08/06 06:57:35 INFO mapred.JobClient: Physical memory (bytes) snapshot=464568320
12/08/06 06:57:35 INFO mapred.JobClient: Reduce output records=5
12/08/06 06:57:35 INFO mapred.JobClient: Virtual memory (bytes) snapshot=1539559424
12/08/06 06:57:35 INFO mapred.JobClient: Map output records=5
12/08/06 06:57:35 INFO mapred.FileInputFormat: Total input paths to process : 1
12/08/06 06:57:35 INFO mapred.JobClient: Running job: job_201208030925_0012
12/08/06 06:57:36 INFO mapred.JobClient: map 0% reduce 0%
12/08/06 06:57:50 INFO mapred.JobClient: map 100% reduce 0%
12/08/06 06:58:05 INFO mapred.JobClient: map 100% reduce 100%
12/08/06 06:58:10 INFO mapred.JobClient: Job complete: job_201208030925_0012
12/08/06 06:58:10 INFO mapred.JobClient: Counters: 30
12/08/06 06:58:10 INFO mapred.JobClient: Job Counters
12/08/06 06:58:10 INFO mapred.JobClient: Launched reduce tasks=1
12/08/06 06:58:10 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=15432
12/08/06 06:58:10 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0
12/08/06 06:58:10 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0
12/08/06 06:58:10 INFO mapred.JobClient: Rack-local map tasks=1
12/08/06 06:58:10 INFO mapred.JobClient: Launched map tasks=1
12/08/06 06:58:10 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=14264
12/08/06 06:58:10 INFO mapred.JobClient: File Input Format Counters
12/08/06 06:58:10 INFO mapred.JobClient: Bytes Read=391
12/08/06 06:58:10 INFO mapred.JobClient: File Output Format Counters
12/08/06 06:58:10 INFO mapred.JobClient: Bytes Written=235
12/08/06 06:58:10 INFO mapred.JobClient: FileSystemCounters
12/08/06 06:58:10 INFO mapred.JobClient: FILE_BYTES_READ=281
12/08/06 06:58:10 INFO mapred.JobClient: HDFS_BYTES_READ=505
12/08/06 06:58:10 INFO mapred.JobClient: FILE_BYTES_WRITTEN=42985
12/08/06 06:58:10 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=235
12/08/06 06:58:10 INFO mapred.JobClient: Map-Reduce Framework
12/08/06 06:58:10 INFO mapred.JobClient: Map output materialized bytes=281
12/08/06 06:58:10 INFO mapred.JobClient: Map input records=5
12/08/06 06:58:10 INFO mapred.JobClient: Reduce shuffle bytes=0
12/08/06 06:58:10 INFO mapred.JobClient: Spilled Records=10
EDIT Driver Class for Grep:
Grep.java
/**
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.hadoop.examples;
import java.util.Random;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.mapred.lib.*;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/* Extracts matching regexs from input files and counts them. */
public class Grep extends Configured implements Tool {
private Grep() {} // singleton
public int run(String[] args) throws Exception {
if (args.length < 3) {
System.out.println("Grep <inDir> <outDir> <regex> [<group>]");
ToolRunner.printGenericCommandUsage(System.out);
return -1;
}
Path tempDir =
new Path("grep-temp-"+
Integer.toString(new Random().nextInt(Integer.MAX_VALUE)));
JobConf grepJob = new JobConf(getConf(), Grep.class);
try {
grepJob.setJobName("grep-search");
FileInputFormat.setInputPaths(grepJob, args[0]);
grepJob.setMapperClass(RegexMapper.class);
grepJob.set("mapred.mapper.regex", args[2]);
if (args.length == 4)
grepJob.set("mapred.mapper.regex.group", args[3]);
grepJob.setCombinerClass(LongSumReducer.class);
grepJob.setReducerClass(LongSumReducer.class);
FileOutputFormat.setOutputPath(grepJob, tempDir);
grepJob.setOutputFormat(SequenceFileOutputFormat.class);
grepJob.setOutputKeyClass(Text.class);
grepJob.setOutputValueClass(LongWritable.class);
JobClient.runJob(grepJob);
JobConf sortJob = new JobConf(getConf(), Grep.class);
sortJob.setJobName("grep-sort");
FileInputFormat.setInputPaths(sortJob, tempDir);
sortJob.setInputFormat(SequenceFileInputFormat.class);
sortJob.setMapperClass(InverseMapper.class);
sortJob.setNumReduceTasks(1); // write a single file
FileOutputFormat.setOutputPath(sortJob, new Path(args[1]));
sortJob.setOutputKeyComparatorClass // sort by decreasing freq
(LongWritable.DecreasingComparator.class);
JobClient.runJob(sortJob);
}
finally {
FileSystem.get(grepJob).delete(tempDir, true);
}
return 0;
}
public static void main(String[] args) throws Exception {
int res = ToolRunner.run(new Configuration(), new Grep(), args);
System.exit(res);
}
}
In the file there are the statistics of two jobs: job: job_201208030925_0011 and job: job_201208030925_0012. The percentages belong to these two jobs, hence there are 2 map progress percentages.
I have been using Hadoop for the last week or so (trying to get to grips with it), and although I have been able to set up a multinode cluster (2 machines: 1 laptop and a small desktop) and retrieve results, I always seem to encounter "Too many fetch failures" when I run a hadoop job.
An example output (on a trivial wordcount example) is:
hadoop#ap200:/usr/local/hadoop$ bin/hadoop jar hadoop-examples-0.20.203.0.jar wordcount sita sita-output3X
11/05/20 15:02:05 INFO input.FileInputFormat: Total input paths to process : 7
11/05/20 15:02:05 INFO mapred.JobClient: Running job: job_201105201500_0001
11/05/20 15:02:06 INFO mapred.JobClient: map 0% reduce 0%
11/05/20 15:02:23 INFO mapred.JobClient: map 28% reduce 0%
11/05/20 15:02:26 INFO mapred.JobClient: map 42% reduce 0%
11/05/20 15:02:29 INFO mapred.JobClient: map 57% reduce 0%
11/05/20 15:02:32 INFO mapred.JobClient: map 100% reduce 0%
11/05/20 15:02:41 INFO mapred.JobClient: map 100% reduce 9%
11/05/20 15:02:49 INFO mapred.JobClient: Task Id : attempt_201105201500_0001_m_000003_0, Status : FAILED
Too many fetch-failures
11/05/20 15:02:53 INFO mapred.JobClient: map 85% reduce 9%
11/05/20 15:02:57 INFO mapred.JobClient: map 100% reduce 9%
11/05/20 15:03:10 INFO mapred.JobClient: Task Id : attempt_201105201500_0001_m_000002_0, Status : FAILED
Too many fetch-failures
11/05/20 15:03:14 INFO mapred.JobClient: map 85% reduce 9%
11/05/20 15:03:17 INFO mapred.JobClient: map 100% reduce 9%
11/05/20 15:03:25 INFO mapred.JobClient: Task Id : attempt_201105201500_0001_m_000006_0, Status : FAILED
Too many fetch-failures
11/05/20 15:03:29 INFO mapred.JobClient: map 85% reduce 9%
11/05/20 15:03:32 INFO mapred.JobClient: map 100% reduce 9%
11/05/20 15:03:35 INFO mapred.JobClient: map 100% reduce 28%
11/05/20 15:03:41 INFO mapred.JobClient: map 100% reduce 100%
11/05/20 15:03:46 INFO mapred.JobClient: Job complete: job_201105201500_0001
11/05/20 15:03:46 INFO mapred.JobClient: Counters: 25
11/05/20 15:03:46 INFO mapred.JobClient: Job Counters
11/05/20 15:03:46 INFO mapred.JobClient: Launched reduce tasks=1
11/05/20 15:03:46 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=72909
11/05/20 15:03:46 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0
11/05/20 15:03:46 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0
11/05/20 15:03:46 INFO mapred.JobClient: Launched map tasks=10
11/05/20 15:03:46 INFO mapred.JobClient: Data-local map tasks=10
11/05/20 15:03:46 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=76116
11/05/20 15:03:46 INFO mapred.JobClient: File Output Format Counters
11/05/20 15:03:46 INFO mapred.JobClient: Bytes Written=1412473
11/05/20 15:03:46 INFO mapred.JobClient: FileSystemCounters
11/05/20 15:03:46 INFO mapred.JobClient: FILE_BYTES_READ=4462381
11/05/20 15:03:46 INFO mapred.JobClient: HDFS_BYTES_READ=6950740
11/05/20 15:03:46 INFO mapred.JobClient: FILE_BYTES_WRITTEN=7546513
11/05/20 15:03:46 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=1412473
11/05/20 15:03:46 INFO mapred.JobClient: File Input Format Counters
11/05/20 15:03:46 INFO mapred.JobClient: Bytes Read=6949956
11/05/20 15:03:46 INFO mapred.JobClient: Map-Reduce Framework
11/05/20 15:03:46 INFO mapred.JobClient: Reduce input groups=128510
11/05/20 15:03:46 INFO mapred.JobClient: Map output materialized bytes=2914947
11/05/20 15:03:46 INFO mapred.JobClient: Combine output records=201001
11/05/20 15:03:46 INFO mapred.JobClient: Map input records=137146
11/05/20 15:03:46 INFO mapred.JobClient: Reduce shuffle bytes=2914947
11/05/20 15:03:46 INFO mapred.JobClient: Reduce output records=128510
11/05/20 15:03:46 INFO mapred.JobClient: Spilled Records=507835
11/05/20 15:03:46 INFO mapred.JobClient: Map output bytes=11435785
11/05/20 15:03:46 INFO mapred.JobClient: Combine input records=1174986
11/05/20 15:03:46 INFO mapred.JobClient: Map output records=1174986
11/05/20 15:03:46 INFO mapred.JobClient: SPLIT_RAW_BYTES=784
11/05/20 15:03:46 INFO mapred.JobClient: Reduce input records=201001
I did a google on the problem, and the people at apache seem to suggest it could be anything from a networking problem (or something to do with /etc/hosts files) or could be a corrupt disk on the slave nodes.
Just to add: I do see 2 "live nodes" on namenode Admin panel (localhost:50070/dfshealth) and under Map/reduce Admin, I see 2 nodes aswell.
Any clues as to how I can avoid these errors?
Thanks in advance.
Edit:1:
The tasktracker log is on: http://pastebin.com/XMkNBJTh
The datanode log is on: http://pastebin.com/ttjR7AYZ
Many thanks.
Modify datanode node/etc/hosts file.
Each line is divided into three parts. The first part is the network IP address, the second part is the host name or domain name, the third part is the host alias detailed steps are as follows:
First check the host name:
cat / proc / sys / kernel / hostname
You will see a HOSTNAME attribute. Change the value of the IP behind on OK and then exit.
Use the command:
hostname ***. ***. ***. ***
Asterisk is replaced by the corresponding IP.
Modify the the hosts configuration similarly, as follows:
127.0.0.1 localhost.localdomain localhost
:: 1 localhost6.localdomain6 localhost6
10.200.187.77 10.200.187.77 hadoop-datanode
If the IP address is configured and successfully modified, or show host name there is a problem, continue to modify the hosts file.
Following solution will definitely work
1.Remove or comment line with Ip 127.0.0.1 and 127.0.1.1
2.use host name not alias for referring node in host file and Master/slave file present in hadoop directory
-->in Host file 172.21.3.67 master-ubuntu
-->in master/slave file master-ubuntu
3. see for NameSpaceId of namenode = NameSpaceId of Datanode
I had the same problem: "Too many fetch failures" and very slow Hadoop performance (the simple wordcount example took more than 20 minutes to run on a 2-node cluster of powerful servers). I also got "WARN mapred.JobClient: Error reading task outputConnection refused" errors.
The problem was fixed, when I followed the instruction by Thomas Jungblut: I removed my master node from the slaves configuration file. After this, the errors disappeared and the wordcount example took only 1 minute.