I just started to practice AWS EMR.
I have a sample word-count application set-up, run and completed from the web interface.
Following the guideline here, I have setup the command-line interface.
so when I run the command:
./elastic-mapreduce --list
I receive
j-27PI699U14QHH COMPLETED ec2-54-200-169-112.us-west-2.compute.amazonaws.comWord count
COMPLETED Setup hadoop debugging
COMPLETED Word count
Now, I want to see the log files. I run the command
./elastic-mapreduce --ssh --jobflow j-27PI699U14QHH
Then I receive the following error:
Error: Jobflow entered COMPLETED while waiting to ssh
Can someone please help me understand what's going on here?
Thanks,
When you setup a job on EMR, this means that Amazon is going to provision a cluster on-demand for you for a limited amount of time. During that time, you are free to ssh to your cluster and look at the logs as much as you want, but by the time your job has finished running, then your cluster is going to be taken down ! At that point, you won't be able to ssh anymore because your cluster simply won't exist.
The workflow typically looks like this:
Create your jobflow
It will be for a few minutes in status STARTING. At that point if you try to run ./elastic-mapreduce --ssh --jobflow <jobid> it will simply wait because the cluster is not available yet.
After a while the status will switch to RUNNING. If you had already started the ssh command above it should automatically connect you to your cluster. Otherwise you can initiate your ssh command now and it should connect you directly without any wait.
Depending on the nature of your job, the RUNNING step could take a while or be very short, it depends what amount of data you're processing and the nature of your computations.
Once all your data has been processed, the status will switch to SHUTTING_DOWN. At that point, if you already sshed before you will get disconnected. If you try to use the ssh command at that point, it will not connect.
Once the cluster has finished shutting down it will enter a terminal state of either COMPLETED or FAILED depending on whether your job succeeded or not. At that point your cluster is no longer available, and if you try to ssh you will get the error you are seeing.
Of course there are exceptions, you could setup an EMR cluster in interactive mode, for example you just want to have Hive setup and then ssh there and run Hive queries and you would have to take your cluster down manually. But if you just want a MapReduce job to run, then you will only be able to ssh for the duration of the job.
That being said, if all you want to do is debugging, there is not even a need to ssh in the first place ! When you create your jobflow, you have the option to enable debugging, so you could do something like that:
./elastic-mapreduce --create --enable-debugging --log-uri s3://myawsbucket
What that means is that all the logs for your job will end up being written to the S3 bucket specified (you have to own this bucket of course and have permission to write to it). Also if you do that, you can go into the AWS console afterwards in the EMR section, and you will be able to see next to your job a button to debug as shown below in the screenshot, this should make your life much easier:
Related
I need to get the id of a specific hadoop job.
In my case, I lunch a sqoop commande remotely and I went to verify the job status with this commande :
hadoop job -status job_id | grep -w 'state'
I can get this information from the GUI but i went to do something
can any one help me !!!
You can use the Yarn REST apis, via your browser or curl from the command line. It will list all the currently running and previously running jobs, including sqoop and the mapreduce jobs that sqoop generates and executes. Use the UI first, if you have it up and running just point your browser to http:<host>:8088/cluster (not sure if the port is the same on all hadoop distributions. I believe 8088 is the default on apache). Alternatively you can use yarn commands directly, e.g, yarn application -list.
I have been trying to run a pig job with multiple steps on Amazon EMR. Here are the details of my environment:
Number of nodes: 20
AMI Version: 3.1.0
Hadoop Distribution: 2.4.0
The pig script has multiple steps and it spawns a long-running map reduce job that has both a map phase and reduce phase. After running for sometime (sometimes an hour, sometimes three or four), the job is killed. The information on the resource manager for the job is:
Kill job received from hadoop (auth:SIMPLE) at
Job received Kill while in RUNNING state.
Obviously, I did not kill it :)
My question is: how do I go about trying to identify what exactly happened? How do I diagnose the issue? Which log files to look at (what to grep for)? Any help on even where the appropriate log files would be greatly helpful. I am new to YARN/Hadoop 2.0
There can be number of reasons. Enable debugging on your cluster and see in the stderr logs for more information.
aws emr create-cluster --name "Test cluster" --ami-version 3.9 --log-uri s3://mybucket/logs/ \
--enable-debugging --applications Name=Hue Name=Hive Name=Pig
More details here:
http://docs.aws.amazon.com/ElasticMapReduce/latest/DeveloperGuide/emr-plan-debugging.html
When running spark 1.4.0 in a single machine, I can add worker by using this command "./bin/spark-class org.apache.spark.deploy.worker.Worker myhostname:7077". The official documentation points out another way by adding "myhostname:7077" to the "conf/slaves" file followed by executing the command "sbin/start-all.sh" which invoke the master and all workers listed in conf/slaves file. However, the later method doesn't work for me (with time-out error). Can anyone help me with this?
Here is my conf/slaves file (assume the master URL is myhostname:700):
myhostname:700
The conf.slaves file should just be the list of the hostnames, you don't need to include the port # that spark runs on (I think if you do it will try and ssh on that port which is probably where the timeout comes from).
I am trying to setup hadoop cluster in Google Compute Engine through "Launch click-to-deploy software" feature .I have created 1 master and 1 slave node and tried to start the cluster using start-all.sh script from master node and i got error "permission denied(publickey)" .
I have generated public and private keys in both slave and master nodes .
currently i logged into the master with my username, is it mandatory to login into master as "hadoop" user .If so ,what is the password for that userid .
please let me know how to overcome this problem .
The deployment creates a user hadoop which owns Hadoop-specific SSH keys which were generated dynamically at deployment time; this means since start-all.sh uses SSH under the hood, you must do the following:
sudo su hadoop
/home/hadoop/hadoop-install/bin/start-all.sh
Otherwise, your "normal" username doesn't have SSH keys properly set up so you won't be able to launch the Hadoop daemons, as you saw.
Another thing to note is that the deployment should have already started all the Hadoop daemons automatically, so you shouldn't need to manually run start-all.sh unless you're rebooting the daemons after some manual configuration updates. If the daemons weren't running after the deployment ran, you may have encountered some unexpected error during initialization.
Hadoop streaming will run the process in "local" mode when there is no hadoop instance running on the box. I have a shell script that is controlling a set of hadoop streaming jobs in sequence and I need to condition copying files from HDFS to local depending on whether the jobs have been running locally or not. Is there a standard way to accomplish this test? I could do a "ps aux | grep something" but that seems ad-hoc.
Hadoop streaming will run the process in "local" mode when there is no hadoop instance running on the box.
Can you pl point to the reference for this?
A regular or a streaming job will run the way it is configured, so we know ahead of time in which mode a Job is run. Check the documentation for configuring Hadoop on a Single Node and Cluster in different modes.
Rather than trying to detect at run time which mode the process is operating, it is probably better to wrap the tool you are developing in a bash script that explicitly selects local vs cluster operatide. The O'Reilly Hadoop describes how to explicitly choose local using a configuration file override:
hadoop v2.MaxTemperatureDriver -conf conf/hadoop-local.xml input/ncdc/micro max-temp
where conf-local.xml is an XML file configured for local operation.
I haven't tried this yet, but I think you can just read out the mapred.job.tracker configuration setting.