I want to run a hadoop job remotely from a windows machine. The cluster is running on Ubuntu.
Basically, I want to do two things:
Execute the hadoop job remotely.
Retrieve the result from hadoop output directory.
I don't have any idea how to achieve this. I am using hadoop version 1.1.2
I tried passing jobtracker/namenode URL in the Job configuration but it fails.
I have tried the following example : Running java hadoop job on local/remote cluster
Result: Getting error consistently as cannot load directory. It is similar to this post:
Exception while submitting a mapreduce job from remote system
Welcome to a world of pain. I've just implemented this exact use case, but using Hadoop 2.2 (the current stable release) patched and compiled from source.
What I did, in a nutshell, was:
Download the Hadoop 2.2 sources tarball to a Linux machine and decompress it to a temp dir.
Apply these patches which solve the problem of connecting from a Windows client to a Linux server.
Build it from source, using these instructions. It will also ensure that you have 64-bit native libs if you have a 64-bit Linux server. Make sure you fix the build files as the post instructs or the build would fail. Note that after installing protobuf 2.5, you have to run sudo ldconfig, see this post.
Deploy the resulted dist tar from hadoop-2.2.0-src/hadoop-dist/target on the server node(s) and configure it. I can't help you with that since you need to tweak it to your cluster topology.
Install Java on your client Windows machine. Make sure that the path to it has no spaces in it, e.g. c:\java\jdk1.7.
Deploy the same Hadoop dist tar you built on your Windows client. It will contain the crucial fix for the Windox/Linux connection problem.
Compile winutils and Windows native libraries as described in this Stackoverflow answer. It's simpler than building entire Hadoop on Windows.
Set up JAVA_HOME, HADOOP_HOME and PATH environment variables as described in these instructions
Use a text editor or unix2dos (from Cygwin or standalone) to convert all .cmd files in the bin and etc\hadoop directories, otherwise you'll get weird errors about labels when running them.
Configure the connection properties to your cluster in your config XML files, namely fs.default.name, mapreduce.jobtracker.address, yarn.resourcemanager.hostname and the alike.
Add the rest of the configuration required by the patches from item 2. This is required for the client side only. Otherwise the patch won't work.
If you've managed all of that, you can start your Linux Hadoop cluster and connect to it from your Windows command prompt. Joy!
Related
I am new to Hadoop and am trying to execute the WordCount Problem.
Things I did so far -
Setting up the Hadoop Single Node cluster referring the below link.
http://www.bogotobogo.com/Hadoop/BigData_hadoop_Install_on_ubuntu_single_node_cluster.php
Write the word count problem referring the below link
https://kishorer.in/2014/10/22/running-a-wordcount-mapreduce-example-in-hadoop-2-4-1-single-node-cluster-in-ubuntu-14-04-64-bit/
Problem is when I execute the last line to run the program -
hadoop jar wordcount.jar /usr/local/hadoop/input /usr/local/hadoop/output
Following is the error I get -
The directory seems to be present
The file is also present in the directory with contents
Finally, on a side note I also tried the following directory sturcture in the jar command.
No avail! :/
I would really appreciate if someone could guide me here!
Regards,
Paul Alwin
Your first image is using input from the local Hadoop installation directory, /usr
If you want to use that data on your local filesystem, you can specify file:///usr/...
Otherwise, if you're running pseudo distributed mode, HDFS has been setup, and /usr does not exist in HDFS unless you explicitly created it there.
Based on the stacktrace, I believe the error comes from the /app/hadoop/ staging directory path not existing, or the permissions for it are not allowing your current user to run commands against that path
Suggestion: Hortonworks and Cloudera offer pre-built VirtualBox images and lots of tutorial resources. Most companies will have Hadoop from one of those vendors, so it's better to get familiar with that rather than mess around with having to install Hadoop yourself from scratch, in my opinion
I'm working with Windows and trying to set up Spark.
Previously I installed Hadoop in addition to Spark, edited the config files, run the hadoop namenode -format and away we went.
I'm now trying to achieve the same by using the bundled version of Spark that is pre built with hadoop - spark-1.6.1-bin-hadoop2.6.tgz
So far it's been a much cleaner, simpler process however I no longer have access to the command that creates the hdfs, the config files for the hdfs are no longer present and I've no 'hadoop' in any of the bin folders.
There wasn't an Hadoop folder in the spark install, I created one for the purpose of winutils.exe.
It feels like I've missed something. Do the pre-built versions of spark not include hadoop? Is this functionality missing from this variant or is there something else that I'm overlooking?
Thanks for any help.
By saying that Spark is built with Hadoop, it is meant that Spark is built with the dependencies of Hadoop, i.e. with the clients for accessing Hadoop (or HDFS, to be more precise).
Thus, if you use a version of Spark which is built for Hadoop 2.6 you will be able to access HDFS filesystem of a cluster with the version 2.6 of Hadoop via Spark.
It doesn't mean that Hadoop is part of the pakage and downloading it Hadoop is installed as well. You have to install Hadoop separately.
If you download a Spark release without Hadoop support, you'll need to include the Hadoop client libraries in all the applications you write wiìhich are supposed to access HDFS (by a textFile for instance).
I am also using same spark in my windows 10. What I have done create C:\winutils\bin directory and put winutils.exe there. Than create HADOOP_HOME=C:\winutils variable. If you have set all
env variables and PATH like SPARK_HOME,HADOOP_HOME etc than it should work.
I have deployed an Amazon EC2 cluster with Spark like so:
~/spark-ec2 -k spark -i ~/.ssh/spark.pem -s 2 --region=eu-west-1 --spark-version=1.3.1 launch spark-cluster
I copy a file I need first to the master and then from master to HDFS using:
ephemeral-hdfs/bin/hadoop fs -put ~/ANTICOR_2_10000.txt ~/user/root/ANTICOR_2_10000.txt
I have a jar I want to run which was compiled with JDK 8 (I am using a lot of Java 8 features) so I copy it over with scp and run it with:
spark/bin/spark-submit --master spark://public_dns_with_port --class package.name.to.Main job.jar -f hdfs://public_dns:~/ANTICOR_2_10000.txt
The problem is that spark-ec2 loads the cluster with JDK7 so I am getting the Unsupported major.minor version 52.0
My question is, which are all the places where I need to change JDK7 to JDK8?
The steps I am doing thus far on master are:
Install JDK8 with yum
Use sudo alternatives --config java and change prefered java to java-8
export JAVA_HOME=/usr/lib/jvm/openjdk-8
Do I have to do that for all the nodes? Also do I need to change the java path that hadoop uses at ephemeral-hdfs/conf/hadoop-env.sh or are there any other spots I missed?
Unfortunately, Amazon doesn't offer out-of-the-box Java 8 installations, yet: see available versions.
Have you seen this post on how to install it on running instances?
Here is what i have been doing for all java installations which are different from versions provided by default installations: -
Configure the JAVA_HOME environment variable on each machine/ node: -
export JAVA_HOME=/home/ec2-user/softwares/jdk1.7.0_25
Modify the default PATH and place the "java/bin" directory before the rest of the PATH on all Nodes/ machines.
export PATH=/home/ec2-user/softwares/jdk1.7.0_25/bin/:$M2:$SCALA_HOME/bin/:$HIVE_HOME/bin/:$PATH:
And the above needs to be done with the same "OS user" which is used to execute/ own the spark master/ worker process.
Hi I am new to Hadoop.
Hadoop Version (2.2.0)
Goals:
Setup Hadoop standalone - Ubuntu 12 (Completed)
Setup Hadoop standalone - Windows 7 (cygwin being used for only sshd) (Completed)
Setup cluster with Ubuntu Master and Windows 7 slave (This is mostly for learning purposes and setting up a env for development) (Stuck)
Setup in relationship with the questions below:
Master running on Ubuntu with hadoop 2.2.0
Slaves running on Windows 7 with a self compiled version from hadoop 2.2.0 source. I am using cygwin only for the sshd
password less login setup and i am able to login both ways using ssh
from outside hadoop. Since my Ubuntu and Windows machine have
different usernames I have set up a config file in the .ssh folder
which maps Hosts with users
Questions:
In a cluster does the username in the master need to be same as in the slave. The reason I am asking this is that post configuration of the cluster when I try to use start-dfs.sh the logs say that they are able to ssh into the slave nodes but were not able to find the location "/home/xxx/hadoop/bin/hadoop-daemon.sh" in the slave. The "xxx" is my master username and not the slaveone. Also since my slave in pure Windows version the install is under C:/hadoop/... Does the master look at the env variable $HADOOP_HOME to check where the install is in the slave? Is there any other env variables that I need to set?
My goal was to use the Windows hadoop build on slave since hadoop is officially supporting windows now. But is it better to run the Linux build under cygwin to accomplish this. The question comes since I am seeing that the start-dfs.sh is trying to execute hadoop-daemon.sh and not some *.cmd.
If this setup works out in future, a possible question that I have is whether Pig, Mahout etc will run in this kind of a setup as I have not seen a build of Pig, Mahout for Windows. Does these components need to be present only on the master node or do they need to be in the slave nodes too. I saw 2 ways of running mahout when experimenting with standalone mode first using the mahout script which I was able to use in linux and second using the yarn jar command where I passed in the mahout jar while using the windows version. In the case Mahout/ Pig (when using the provided sh script) will assume that the slaves already have the jars in place then the Ubuntu + Windows combo does not seem to work. Please advice.
As I mentioned this is more as an experiment rather than an implementation plan. Our final env will be completely on linux. Thank you for your suggestions.
You may have more success going with more standard ways of deploying hadoop. Try out using ubuntu vm's for master and slaves.
You can also try to do a pseudo-distributed deployment in which all of the processes run on a single VM and thus avoid the need to even consider multiple os's.
I have only worked with the same username. In general SSH allows to login with a different login name with the -l command. But this might get tricky. You have to list your slaves in the slaves file.
At least at the manual https://hadoop.apache.org/docs/r0.19.1/cluster_setup.html#Slaves I did not find anything to add usernames. it might be worth trying to add -l login_name to the slavenode in the slave conf file and see if it works.
I'm working on a hadoop cluster with CDH4.2.0 installed and ran into this error. It's been fixed in later versions of hadoop but I don't have access to update the cluster. Is there a way to tell hadoop to use this jar when running my job through the command line arguments like
hadoop jar MyJob.jar -D hadoop.mapreduce.client=hadoop-mapreduce-client-core-2.0.0-cdh4.2.0.jar
where the new mapreduce-client-core.jar file is the patched jar from the ticket. Or must hadoop be completely recompiled with this new jar? I'm new to hadoop so I don't know all the command line options that are possible.
I'm not sure how that would work as when you're executing the hadoop command you're actually executing code in the client jar.
Can you not use MR1? The issue says this issue only occurs when you're using MR2, so unless you really need Yarn you're probably better using the MR1 library to run your map/reduce.