I started observing below validation error on EMR console,
Upon checking the status of the instance controller service, observed that
sudo systemctl status instnace-controller.service output is not consistent, it varies between running and auto-restart.
Master node system logs shows;
(console) 2023-02-03 21:55:23 About to start instance controller.
(console) 2023-02-03 21:55:23 Listing currently running instance controllers:
hadoop 8439 1 0 21:55 ? 00:00:00 /bin/bash -l /usr/bin/instance-controller
hadoop 8510 8439 0 21:55 ? 00:00:00 /etc/alternatives/jre/bin/java -Xmx1024m -XX:+ExitOnOutOfMemoryError -XX:MinHeapFreeRatio=10 -server -cp /usr/share/aws/emr/instance-controller/lib/*:/home/hadoop/conf -Dlog4j.defaultInitOverride aws157.instancecontroller.Main
hadoop 8541 8439 0 21:55 ? 00:00:00 grep -i instance
root 8542 8439 0 21:55 ? 00:00:00 sudo tee -a /emr/instance-state/console.log-2023-02-03-21-55 /dev/console
oozie 26477 1 26 21:53 ? 00:00:22 /etc/alternatives/jre/bin/java -Xmx1024m -Xmx1024m -Doozie.home.dir=/usr/lib/oozie -Doozie.config.dir=/etc/oozie/conf -Doozie.log.dir=/var/log/oozie -Doozie.data.dir=/var/lib/oozie -Doozie.instance.id=ip-10-111-24-159.pvt.lp192.cazena.com -Doozie.config.file=oozie-site.xml -Doozie.log4j.file=oozie-log4j.properties -Doozie.log4j.reload=10 -Djava.library.path= -cp /usr/lib/oozie/embedded-oozie-server/*:/usr/lib/oozie/embedded-oozie-server/dependency/*:/usr/lib/oozie/lib/*:/usr/lib/oozie/libtools/*:/usr/lib/oozie/libext/*:/usr/lib/oozie/embedded-oozie-server:/usr/share/aws/emr/emrfs/lib/*:/usr/share/aws/emr/emrfs/conf/*:/usr/share/aws/emr/emrfs/auxlib/* org.apache.oozie.server.EmbeddedOozieServer
root 27455 1 42 21:54 ? 00:00:35 /etc/alternatives/jre/bin/java -Xmx1024m -XX:+ExitOnOutOfMemoryError -XX:MinHeapFreeRatio=10 -server -cp /usr/share/aws/emr/instance-controller/lib/*:/home/hadoop/conf -Dlog4j.defaultInitOverride aws157.logpusher.Main /etc/logpusher/logpusher.properties
(console) 2023-02-03 21:55:23 Displaying last 10 lines of instance controller logfile:
2023-02-03 21:55:17,719 INFO main: isV2FrameworkEnabled: false, extraInstanceData.numCandidates: 1
2023-02-03 21:55:17,735 WARN main: Invalid metrics information null fetched from checkpoint, will start continuing from current moment instead.
2023-02-03 21:55:17,735 INFO main: Initialized YARN checkpointing state with ckpFileAvl: true, ckpInfo: [ lastCkpTs(0), totalHdfsBytesReadCompletedApps(0), totalHdfsBytesWrittenCompletedApps(0), totalS3BytesReadCompletedApps(0), totalS3BytesWrittenCompletedApps(0)]
2023-02-03 21:55:17,745 ERROR main: Thread + 'main' failed with error
java.lang.RuntimeException: LocalStartupState is FAILED, so not allowing instance controller to start
at aws157.instancecontroller.common.InstanceConfigurator.hasAlreadyBeenConfigured(InstanceConfigurator.java:124)
at aws157.instancecontroller.common.InstanceConfigurator.<init>(InstanceConfigurator.java:100)
at aws157.instancecontroller.InstanceController.<init>(InstanceController.java:223)
at aws157.instancecontroller.Main.runV1Framework(Main.java:239)
at aws157.instancecontroller.Main.main(Main.java:222)
I tried restarting service multiple time with
sudo systemctl start instance-controller.service, rebooted the node hoping that service will start back after reboot. But it is not working. (Btw, this worked on lower environment)
Jobs on the cluster are running fine though without any issues, but I am not able to see application logs pushed to S3 or on console.
Need inputs on how to restart instance controller service.
I followed this tutorial as well as 2 others trying to host my project using Azure. https://learn.microsoft.com/en-us/azure/app-service/containers/tutorial-python-postgresql-app?tabs=bash#clone-the-sample-app I managed to host the sample web app used in the tutorial, but could not host my own project
**I keep getting "Server Error 500". I've spent around 36 hours trying to fix the problem.**
I checked the application logs - nothing
I checked the kudu/scm logs - nothing
I looked under "App Service logs" and checked the ftp logs - nothing
I checked to see if all the files had been uploaded at this location "<>.scm.azurewebsites.net/wwwroot/" The staticfiles successfully uploaded.
I went to "Web SSH" and installed all the dependencies** "pip install -r requirements.txt"
then did "python manage.py runserver" AND NO ERRORS, but it did not want to connect to "127.0.0.1:8000" or "localhost:8000" ???
I spend around 6 hours searching for answers - tried everything - nothing worked
WEBSITES_PORT set to 8000 (tried different ports and removed this setting after no luck)
I changed DEBUG to False and True - didn't work
I did set all the necessary environment variables (eg, DB_HOST, DB_PASSWORD ...)
The App Service plan is F1 (free)
I went to all the pages on my web app and got server error 500 on all the pages except when logging into admin, after logging into admin I got the error again.
Possible Solutions I thought might work
I might be missing an important "Application setting" ???
One of the dependencies might be causing the problem - but I highly doubt it
I dont know pls help sir
This was about what the logs kept saying
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
2020-06-24T08:28:13.331Z INFO - Starting container for site
2020-06-24T08:28:13.331Z INFO - docker run -d -p 5480:8000 --name forexflowcom_0_136ed024 -e WEBSITE_SITE_NAME=forexflowcom -e WEBSITE_AUTH_ENABLED=False -e WEBSITE_ROLE_INSTANCE_ID=0 -e WEBSITE_HOSTNAME=forexflowcom.azurewebsites.net -e WEBSITE_INSTANCE_ID=9072c805cf2bc663ced034398777a5d5f6115a51e64a73b6fc69b73f64c8660e -e HTTP_LOGGING_ENABLED=1 appsvc/python:3.7_20200101.1
2020-06-24T08:28:16.751Z INFO - Initiating warmup request to container forexflowcom_0_136ed024 for site forexflowcom
2020-06-24T08:28:28.970Z INFO - Container forexflowcom_0_136ed024 for site forexflowcom initialized successfully and is ready to serve requests.
2020-06-24T09:34:28.003Z INFO - Starting container for site
2020-06-24T09:34:28.010Z INFO - docker run -d -p 5757:8000 --name forexflowcom_1_86357e3d -e WEBSITE_SITE_NAME=forexflowcom -e WEBSITE_AUTH_ENABLED=False -e WEBSITE_ROLE_INSTANCE_ID=0 -e WEBSITE_HOSTNAME=forexflowcom.azurewebsites.net -e WEBSITE_INSTANCE_ID=9072c805cf2bc663ced034398777a5d5f6115a51e64a73b6fc69b73f64c8660e -e HTTP_LOGGING_ENABLED=1 appsvc/python:3.7_20200101.1
2020-06-24T09:34:31.507Z INFO - Initiating warmup request to container forexflowcom_1_86357e3d for site forexflowcom
2020-06-24T09:34:49.002Z INFO - Container forexflowcom_1_86357e3d for site forexflowcom initialized successfully and is ready to serve requests.
2020-06-24T09:38:04.238Z INFO - Starting container for site
2020-06-24T09:38:04.240Z INFO - docker run -d -p 7958:8000 --name forexflowcom_2_79f5bea0 -e WEBSITE_SITE_NAME=forexflowcom -e WEBSITE_AUTH_ENABLED=False -e WEBSITE_ROLE_INSTANCE_ID=0 -e WEBSITE_HOSTNAME=forexflowcom.azurewebsites.net -e WEBSITE_INSTANCE_ID=9072c805cf2bc663ced034398777a5d5f6115a51e64a73b6fc69b73f64c8660e -e HTTP_LOGGING_ENABLED=1 appsvc/python:3.7_20200101.1
2020-06-24T09:38:08.317Z INFO - Initiating warmup request to container forexflowcom_2_79f5bea0 for site forexflowcom
2020-06-24T09:38:23.838Z INFO - Waiting for response to warmup request for container forexflowcom_2_79f5bea0. Elapsed time = 15.5210597 sec
2020-06-24T09:38:41.054Z INFO - Container forexflowcom_2_79f5bea0 for site forexflowcom initialized successfully and is ready to serve requests.
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
EDIT // EDIT // EDIT // EDIT // EDIT
I found The solution
in settings.py I had:
try:
from .local_settings import *
except ImportError:
print("No local file, your in production")
after removing this It worked
I'm trying to start a local Ray cluster but the initialization and setup commands are raising errors and I'm not sure what they mean.
For each command, the following message is shown after it is executed (the full logs are shown further down):
bash: cannot set terminal process group (-1): Inappropriate ioctl for device
bash: no job control in this shell
They don't appear to be stopping some commands from executing successfully, but I'm unable to activate a conda environment on each node using:
# List of shell commands to run to set up each nodes.
setup_commands:
- conda activate pytorch-dev
Any help or explanation would be greatly appreciated.
My cluster configuration file (cluster_config_local.yaml) contains:
# An unique identifier for the head node and workers of this cluster.
cluster_name: default
## NOTE: Typically for local clusters, min_workers == initial_workers == max_workers.
# The minimum number of workers nodes to launch in addition to the head
# node. This number should be >= 0.
# Typically, min_workers == initial_workers == max_workers.
min_workers: 12
# The initial number of worker nodes to launch in addition to the head node.
# Typically, min_workers == initial_workers == max_workers.
initial_workers: 12
# The maximum number of workers nodes to launch in addition to the head node.
# This takes precedence over min_workers.
# Typically, min_workers == initial_workers == max_workers.
max_workers: 12
# Autoscaling parameters.
# Ignore this if min_workers == initial_workers == max_workers.
autoscaling_mode: default
target_utilization_fraction: 0.8
idle_timeout_minutes: 5
# This executes all commands on all nodes in the docker container,
# and opens all the necessary ports to support the Ray cluster.
# Empty string means disabled. Assumes Docker is installed.
docker:
image: "" # e.g., tensorflow/tensorflow:1.5.0-py3
container_name: "" # e.g. ray_docker
run_options: [] # Extra options to pass into "docker run"
# Local specific configuration.
provider:
type: local
head_ip: cs19090bs #Lab 3, machine 311
worker_ips: [
cs19091bs, cs19093bs, cs19094bs, cs19095bs, cs19096bs,
cs19103bs, cs19102bs, cs19101bs, cs19100bs, cs19099bs, cs19098bs, cs19097bs
]
# How Ray will authenticate with newly launched nodes.
auth:
ssh_user: user
ssh_private_key: ~/.ssh/id_rsa
# Leave this empty.
head_node: {}
# Leave this empty.
worker_nodes: {}
# Files or directories to copy to the head and worker nodes. The format is a
# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.
file_mounts: {
# "/path1/on/remote/machine": "/path1/on/local/machine",
# "/path2/on/remote/machine": "/path2/on/local/machine",
}
# List of commands that will be run before `setup_commands`. If docker is
# enabled, these commands will run outside the container and before docker
# is setup.
initialization_commands: []
# List of shell commands to run to set up each nodes.
setup_commands:
- conda activate pytorch-dev
# Custom commands that will be run on the head node after common setup.
head_setup_commands: []
# Custom commands that will be run on worker nodes after common setup.
worker_setup_commands: []
# Command to start ray on the head node. You don't need to change this.
head_start_ray_commands:
- ray stop
- ulimit -c unlimited && ray start --head --redis-port=6379 --autoscaling-config=~/ray_bootstrap_config.yaml
# Command to start ray on worker nodes. You don't need to change this.
worker_start_ray_commands:
- ray stop
- ray start --redis-address=$RAY_HEAD_IP:6379
The full logs that are shown when I execute ray up cluster_config_local.yaml are:
2019-11-11 10:18:06,930 INFO node_provider.py:41 -- ClusterState: Loaded cluster state: ['cs19091bs', 'cs19093bs', 'cs19094bs', 'cs19095bs', 'cs19096bs', 'cs19090bs', 'cs19103bs', 'cs19102bs', 'cs19101bs', 'cs19100bs', 'cs19099bs', 'cs19098bs', 'cs19097bs']
This will create a new cluster [y/N]: y
2019-11-11 10:18:08,413 INFO commands.py:201 -- get_or_create_head_node: Launching new head node...
2019-11-11 10:18:08,414 INFO node_provider.py:85 -- ClusterState: Writing cluster state: ['cs19091bs', 'cs19093bs', 'cs19094bs', 'cs19095bs', 'cs19096bs', 'cs19090bs', 'cs19103bs', 'cs19102bs', 'cs19101bs', 'cs19100bs', 'cs19099bs', 'cs19098bs', 'cs19097bs']
2019-11-11 10:18:08,416 INFO commands.py:214 -- get_or_create_head_node: Updating files on head node...
2019-11-11 10:18:08,417 INFO updater.py:356 -- NodeUpdater: cs19090bs: Updating to 345f31e4c980153f1c40ae2c0be26b703d4bbfde
2019-11-11 10:18:08,419 INFO node_provider.py:85 -- ClusterState: Writing cluster state: ['cs19091bs', 'cs19093bs', 'cs19094bs', 'cs19095bs', 'cs19096bs', 'cs19090bs', 'cs19103bs', 'cs19102bs', 'cs19101bs', 'cs19100bs', 'cs19099bs', 'cs19098bs', 'cs19097bs']
2019-11-11 10:18:08,419 INFO updater.py:398 -- NodeUpdater: cs19090bs: Waiting for remote shell...
2019-11-11 10:18:08,420 INFO updater.py:210 -- NodeUpdater: cs19090bs: Waiting for IP...
2019-11-11 10:18:08,429 INFO log_timer.py:21 -- NodeUpdater: cs19090bs: Got IP [LogTimer=9ms]
2019-11-11 10:18:08,442 INFO updater.py:262 -- NodeUpdater: cs19090bs: Running uptime on 132.181.15.173...
bash: cannot set terminal process group (-1): Inappropriate ioctl for device
bash: no job control in this shell
10:18:10 up 4 days, 22:41, 1 user, load average: 1.14, 0.56, 0.38
2019-11-11 10:18:10,178 INFO log_timer.py:21 -- NodeUpdater: cs19090bs: Got remote shell [LogTimer=1759ms]
2019-11-11 10:18:10,181 INFO node_provider.py:85 -- ClusterState: Writing cluster state: ['cs19091bs', 'cs19093bs', 'cs19094bs', 'cs19095bs', 'cs19096bs', 'cs19090bs', 'cs19103bs', 'cs19102bs', 'cs19101bs', 'cs19100bs', 'cs19099bs', 'cs19098bs', 'cs19097bs']
2019-11-11 10:18:10,182 INFO updater.py:262 -- NodeUpdater: cs19090bs: Running mkdir -p ~ on 132.181.15.173...
bash: cannot set terminal process group (-1): Inappropriate ioctl for device
bash: no job control in this shell
2019-11-11 10:18:11,640 INFO updater.py:460 -- NodeUpdater: cs19090bs: Syncing /tmp/ray-bootstrap-aomvoo_d to ~/ray_bootstrap_config.yaml...
sending incremental file list
ray-bootstrap-aomvoo_d
sent 120 bytes received 47 bytes 111.33 bytes/sec
total size is 1,063 speedup is 6.37
2019-11-11 10:18:12,147 INFO log_timer.py:21 -- NodeUpdater: cs19090bs: Synced /tmp/ray-bootstrap-aomvoo_d to ~/ray_bootstrap_config.yaml [LogTimer=1964ms]
2019-11-11 10:18:12,147 INFO updater.py:262 -- NodeUpdater: cs19090bs: Running mkdir -p ~ on 132.181.15.173...
bash: cannot set terminal process group (-1): Inappropriate ioctl for device
bash: no job control in this shell
2019-11-11 10:18:13,610 INFO updater.py:460 -- NodeUpdater: cs19090bs: Syncing /home/cosc/student/atu31/.ssh/id_rsa to ~/ray_bootstrap_key.pem...
sending incremental file list
sent 60 bytes received 12 bytes 48.00 bytes/sec
total size is 3,243 speedup is 45.04
2019-11-11 10:18:14,131 INFO log_timer.py:21 -- NodeUpdater: cs19090bs: Synced /home/cosc/student/atu31/.ssh/id_rsa to ~/ray_bootstrap_key.pem [LogTimer=1984ms]
2019-11-11 10:18:14,133 INFO node_provider.py:85 -- ClusterState: Writing cluster state: ['cs19091bs', 'cs19093bs', 'cs19094bs', 'cs19095bs', 'cs19096bs', 'cs19090bs', 'cs19103bs', 'cs19102bs', 'cs19101bs', 'cs19100bs', 'cs19099bs', 'cs19098bs', 'cs19097bs']
2019-11-11 10:18:14,134 INFO log_timer.py:21 -- NodeUpdater: cs19090bs: Initialization commands completed [LogTimer=0ms]
2019-11-11 10:18:14,134 INFO updater.py:262 -- NodeUpdater: cs19090bs: Running conda activate pytorch-dev on 132.181.15.173...
bash: cannot set terminal process group (-1): Inappropriate ioctl for device
bash: no job control in this shell
2019-11-11 10:18:15,740 INFO log_timer.py:21 -- NodeUpdater: cs19090bs: Setup commands completed [LogTimer=1605ms]
2019-11-11 10:18:15,740 INFO updater.py:262 -- NodeUpdater: cs19090bs: Running ray stop on 132.181.15.173...
bash: cannot set terminal process group (-1): Inappropriate ioctl for device
bash: no job control in this shell
2019-11-11 10:18:17,809 INFO updater.py:262 -- NodeUpdater: cs19090bs: Running ulimit -c unlimited && ray start --head --redis-port=6379 --autoscaling-config=~/ray_bootstrap_config.yaml on 132.181.15.173...
bash: cannot set terminal process group (-1): Inappropriate ioctl for device
bash: no job control in this shell
2019-11-11 10:18:19,923 INFO scripts.py:303 -- Using IP address 132.181.15.173 for this node.
2019-11-11 10:18:19,924 INFO resource_spec.py:205 -- Starting Ray with 7.62 GiB memory available for workers and up to 3.81 GiB for objects. You can adjust these settings with ray.init(memory=<bytes>, object_store_memory=<bytes>).
2019-11-11 10:18:20,169 INFO scripts.py:333 --
Started Ray on this node. You can add additional nodes to the cluster by calling
ray start --redis-address 132.181.15.173:6379
from the node you wish to add. You can connect a driver to the cluster from Python by running
import ray
ray.init(redis_address="132.181.15.173:6379")
If you have trouble connecting from a different machine, check that your firewall is configured properly. If you wish to terminate the processes that have been started, run
ray stop
2019-11-11 10:18:20,221 INFO log_timer.py:21 -- NodeUpdater: cs19090bs: Ray start commands completed [LogTimer=4480ms]
2019-11-11 10:18:20,222 INFO log_timer.py:21 -- NodeUpdater: cs19090bs: Applied config 345f31e4c980153f1c40ae2c0be26b703d4bbfde [LogTimer=11804ms]
2019-11-11 10:18:20,224 INFO node_provider.py:85 -- ClusterState: Writing cluster state: ['cs19091bs', 'cs19093bs', 'cs19094bs', 'cs19095bs', 'cs19096bs', 'cs19090bs', 'cs19103bs', 'cs19102bs', 'cs19101bs', 'cs19100bs', 'cs19099bs', 'cs19098bs', 'cs19097bs']
2019-11-11 10:18:20,226 INFO commands.py:281 -- get_or_create_head_node: Head node up-to-date, IP address is: 132.181.15.173
To monitor auto-scaling activity, you can run:
ray exec cluster/cluster_config_local.yaml 'tail -n 100 -f /tmp/ray/session_*/logs/monitor*'
To open a console on the cluster:
ray attach cluster_config_local.yaml
To get a remote shell to the cluster manually, run:
ssh -i ~/.ssh/id_rsa user#132.181.15.173
bash: cannot set terminal process group (-1): Inappropriate ioctl for device
bash: no job control in this shell
This error message is harmless (and should be muted in Ray). See How to tell bash not to issue warnings "cannot set terminal process group" and "no job control in this shell" when it can't assert job control?.
I have downloaded (since I don't have space for running CDH or Sandbox) Hadoop 2.6.0 and hadoop streaming from here
I ran the command of
bin/hadoop jar contrib/hadoop-streaming-2.6.0.jar \
-file ${HADOOP_HOME}/py_mapred/mapper.py -mapper ${HADOOP_HOME}/py_mapred/mapper.py \
-file ${HADOOP_HOME}/py_mapred/reducer.py -reducer ${HADOOP_HOME}/py_mapred/reducer.py \
-input /input/davinci/* -output /input/davinci-output
where I stored the downloaded streaming jar in ${HADOOP_HOME}/contrib, and the other files in py_mapred. At the same time, I copyFromLocal to /input directory on hdfs. Now, when I run the command, the following lines show up:
15/08/14 17:35:45 WARN streaming.StreamJob: -file option is deprecated, please use generic option -files instead.
15/08/14 17:35:46 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
packageJobJar: [/usr/local/cellar/hadoop/2.6.0/py_mapred/mapper.py, /usr/local/cellar/hadoop/2.6.0/py_mapred/reducer.py, /var/folders/c5/4xfj65v15g91f71c_b9whnpr0000gn/T/hadoop-unjar3313567263260134566/] [] /var/folders/c5/4xfj65v15g91f71c_b9whnpr0000gn/T/streamjob9165494241574343777.jar tmpDir=null
15/08/14 17:35:47 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
15/08/14 17:35:47 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
15/08/14 17:35:48 INFO mapred.FileInputFormat: Total input paths to process : 1
15/08/14 17:35:48 INFO mapreduce.JobSubmitter: number of splits:2
15/08/14 17:35:48 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1439538212023_0002
15/08/14 17:35:49 INFO impl.YarnClientImpl: Submitted application application_1439538212023_0002
15/08/14 17:35:49 INFO mapreduce.Job: The url to track the job: http://Jonathans-MacBook-Pro.local:8088/proxy/application_1439538212023_0002/
15/08/14 17:35:49 INFO mapreduce.Job: Running job: job_1439538212023_0002
It looks like the command has been accepted. I checked on localhost:8088 and the job does register. However it's not running, despite the fact that it says Running job: job_1439538212023_0002. Is there something wrong with my command? Is it due to permission setting? Why isn't the job running?
Thank you
Here is right way for streaming:
bin/hadoop jar contrib/hadoop-streaming-2.6.0.jar \
-file ${HADOOP_HOME}/py_mapred/mapper.py -mapper '/usr/bin/python mapper.py' -file ${HADOOP_HOME}/py_mapred/reducer.py -reducer '/usr/bin/python reducer.py' -input /input/davinci/* -output /input/davinci-output
I am trying to simulate the Hadoop environment using latest Hadoop version 2.6.0, Java SDK 1.70 on my Ubuntu desktop. I configured the hadoop with necessary environment parameters and all its processes are up and running and they can be seen with the following jps command:
nandu#nandu-Desktop:~$ jps
2810 NameNode
3149 SecondaryNameNode
3416 NodeManager
3292 ResourceManager
2966 DataNode
4805 Jps
I could also see the above information, plus the dfs files through the Firefox browser. However, when I tried to run a simple WordCound MapReduce job, it hangs and it doesn't produce any output or shows any error message(s). After a while I killed the process using the "hadoop job -kill " command. Can you please guide me, to find the cause of this issue and how to resolve it? I am giving below the Job start and kill(end) screenshot.
If you need additional information, please let me know.
Your help will be highly appreciated.
Thanks,
===================================================================
nandu#nandu-Desktop:~/dev$ hadoop jar wc.jar WordCount /user/nandu/input /user/nandu/output
15/02/27 10:35:20 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/02/27 10:35:20 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
15/02/27 10:35:21 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
15/02/27 10:35:21 INFO input.FileInputFormat: Total input paths to process : 2
15/02/27 10:35:21 INFO mapreduce.JobSubmitter: number of splits:2
15/02/27 10:35:22 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1425048764581_0003
15/02/27 10:35:22 INFO impl.YarnClientImpl: Submitted application application_1425048764581_0003
15/02/27 10:35:22 INFO mapreduce.Job: The url to track the job: http://nandu-Desktop:8088/proxy/application_1425048764581_0003/
15/02/27 10:35:22 INFO mapreduce.Job: Running job: job_1425048764581_0003
==================== at this point the job was killed ===================
15/02/27 10:38:23 INFO mapreduce.Job: Job job_1425048764581_0003 running in uber mode : false
15/02/27 10:38:23 INFO mapreduce.Job: map 0% reduce 0%
15/02/27 10:38:23 INFO mapreduce.Job: Job job_1425048764581_0003 failed with state KILLED due to: Application killed by user.
15/02/27 10:38:23 INFO mapreduce.Job: Counters: 0
I encountered similar problem while running provided MapReduce sample in hadoop package. In my case it was hanging due to low disk space on my VM (about 1.5 GB was empty). When I freed some disk space it ran pretty fine. Also, please check other system resource requirements are fulfilled.