Hadoop Pig job not running - hadoop

I am testing hadoop, as of now I have :
1) localhost:8088 working
2) localhost:50070 working
3) I created a few files on hdfs
Then I launch pig, and do a LOAD on a file, and then a FILTER, and then a DUMP.
When I DUMP, pig display info about the starting of the mapreduce.
It ends with a sentence like :
"MapReduceLauncher - 0% complete" + "Running Jobs are [job_xxx]".
So I think the job is launched. I even see it as an ACCEPTED App on the hadoop interface on localhost:8088. But then nothing happens : it is stucked at 0% complete :-(
So, the job is "ACCEPTED" but never goes to RUNNING :-(
Should I do something to make my grunt/pig command lines instructions to run ??
Thanks.
JR.
PS: I can't make any copy paste from my job environment.

I unblocked the situation while getting aware that my hard drive was 90% full. At this level, hadoop refuses to write anymore logs. I just had to delete some (big !) files to get it running again...

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Hadoop Mapreduce stuck and never completes

I am using Pig to store data into hive. I have a problem that when I execute program, it shows 0% complete and stuck and never completed. I run around 3 hours but never showed any problem. I started searching and found the problem might be in yarn.xml and map_reduce.xml configuration. I changed configuration, but it never effected at all.

IPython Notebook with Spark on EC2 : Initial job has not accepted any resources

I am trying to run the simple WordCount job in IPython notebook with Spark connected to an AWS EC2 cluster. The program works perfectly when I use Spark in the local standalone mode but throws the problem when I try to connect it to the EC2 cluster.
I have taken the following steps
I have followed instructions given in this Supergloo blogpost.
No errors are found until the last line where I try to write the output to a file. [The lazyloading feature of Spark means that this when the program really starts to execute]
This is where I get the error
[Stage 0:> (0 + 0) / 2]16/08/05 15:18:03 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
Actually there is no error, we have this warning and the program goes into an indefinite wait state. Nothing happens until I kill the IPython notebook.
I have seen this Stackoverflow post and have reduced the number of cores to 1 and memory to 512 by using this options after the main command
--total-executor-cores 1 --executor-memory 512m
The screen capture from the SparkUI is as follows
sparkUI
This clearly shows that both core and UI is not being fully utilized.
Finally, I see from this StackOverflow post that
The spark-ec2 script configure the Spark Cluster in EC2 as standalone,
which mean it can not work with remote submits. I've been struggled
with this same error you described for days before figure out it's not
supported. The message error is unfortunately incorrect.
So you have to copy your stuff and log into the master to execute your
spark task.
If this is indeed the case, then there is nothing more to be done, but since this statement was made in 2014, I am hoping that in the last 2 years the script has been rectified or there is a workaround. If there is any workaround, I would be grateful if someone can point it out to me please.
Thank you for your reading till this point and for any suggestions offered.
You can not submit jobs except on the Master - as you see - unless you set up a REST based Spark job server.

Get status when running job without hadoop

When I run a hadoop job with the hadoop application it prints a lot of stuff. Among them, It show the relative progress of the job ("map: 30%, reduce: 0%" and stuff like that). But, when running a job without the application it does not print anything, not even errors. Is there a way to get that level of logging without the application? That is, without running [hadoop_folder]/bin/hadoop jar <my_jar> <indexer> <args>....
You can get this information from Application Master (assuming you use YARN and not MR1 where you would get it from Job Tracker). There is usually web UI where you can find this information. Details will depend on your Hadoop installation / distribution.
In case of Hadoop v1 check Job tracker web URL and in case of Hadoop v2 check Application Master web UI

Has anyone use mapred.job.tracker=local in hadoop streaming job?

in last weeks, we use hadoop streaming to calculate some reports everyday. Recently we made a change to our program, if the input size is smaller than 10MB, we will set mapred.job.tracker=local in the JobConf, then the job will run locally.
But last night, many jobs failed, with status 3 returned by runningJob.getJobState().
I don't know why, and there is nothing in the stderr.
I can google nothing related about this question. So I'm wondering if I should use mapred.job.tracker=local in production mode? Maybe it's just a debug solution in developing supplied by hadoop.
Has anyone know something about it? Anything, Any infomation, Thank you.
I believe setting mapred.job.tracker=local has nothing to do with your error as local is the default value.
This config parameter defines the host and port that the MapReduce job tracker runs at. If it is set to be "local", then jobs are run in-process as a single map and reduce task.
Refer here.

Hadoop Mapper not running my class

Using Hadoop version 0.20.. I am creating a chain of jobs job1 and job2 (mappers of which are in x.jar, there is no reducer) , with dependency and submitting to hadoop cluster using JobControl. Note I have setJarByClass and getJar gives the correct jar file, when checked before submission.
Submission goes through and there seem to be no errors in user logs and jobtracker. But I dont see my Mapper getting executed (no sysouts or log output), but default output seems to be coming to the output folder (input file is read as is and output). I am able to run the job directly using x.jar, but I am really out of clues as to why it is not running with Jobcontrol.
Please help !
This issue bugged me for quite some days. Finally I found that it was the UsedGenericOptionsParser which created the issue. Set this to true and everything started working fine.

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