I am running a hadoop job on a cluster and passing some jars using -libjars option while running a hadoop job. I am not sure where I can find these jars on cluster. One more thing whether these jars are copied from localmachine to cluster. Where I can find these jars on cluster
According to the Hadoop - The Definitive Guide
Copies the specified JAR files from the local filesystem (or any filesystem if a scheme is
specified)to the shared filesystem used bythe jobtracker (usually HDFS), and adds them
to the MapReduce task’s classpath. This option is a useful way of shipping JAR files that
a job is dependent on.
So, the specified files are copied from the local file system to HDFS and then to the mapper/reducer nodes in the classpath. Also, these files are replicated mapreduce.client.submit.file.replication number of times, which is defaulted to 10. The reason why it's replicated more than 3 times is because the file has to be distributed to all the required nodes.
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I have an Apache Spark cluster consisting of a master and multiple slave nodes. In the jars folder of each node I require the jar file for a program I run on Spark.
There are regular updates to this jar so I find myself constantly copying the updated jar file.
Is there a quick and easy way that an updated jar file can be replicated from master to all slave nodes or any other way of distributing this each time the jar is updated?
When you running your Spark job with spark-submit use --jars option. Using this option you can write path to jar file that you need.
Also, jars in --jars option will be automatically transferred to the cluster, so you need this jar only on the master node.
Read about how to use this option here.
I am parsing xml file using XMLInputFormat.class which is present in mahout-exmaples jar. but while running the jar file of map reduce i am getting below error
Error: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.mahout.classifier.bayes.XmlInputFormat not found
Please let me know how can i make these jars available while running on multinode hadoop cluster.
Include the all mahout-examples JARs in the “-libjars” command line option of the hadoop jar ... command. The jar will be placed in distributed cache and will be made available to all of the job’s task attempts. More specifically, you will find the JAR in one of the ${mapred.local.dir}/taskTracker/archive/${user.name}/distcache/… subdirectories on local nodes.
Please refer this link for more details.
I have a MapReduce job which uses a 3rd party jar and for passing a jar file to the task nodes I know that there are 2 ways to do it which is hadoop jar -archive /custom.jar or hadoop jar -libjars /custom.jar provided my Job uses GenericOptionsParser.
My Question is which is the best option to choose, as jar files can be passes by both -archive and -libjars options ?
-libjar is mostly suited to ship jars as documentation says. -archive is a general purpose and the option unarchives them(this might not be needed for jar usage, as you will never want the jar to be unzipped) at the task node. archive is mostly for shipping any other files and making them available at the task node.
I am running hadoop jobs on a distributed cluster using oozie. I give a setting 'oozie.libpath' for the oozie jobs.
Recently, I have deleted few of my older version jar files from the library path oozie uses and I have replaced them with newer versions. However, when I run my hadoop job, my older version of jar files and newer version of jar files both get loaded and the mapreduce is still using the older version.
I am not sure where zookeeper is loading the jar files from. Are there any default settings that it loads the jar files from ? There is only one library path in my HDFS and it does not have those jar files.
I have found what is going wrong. The wrong jar is shipped with my job file. Should have check here first
The mapred task is a very simple 'wordcount' implemented by Java (plz, see http://wiki.apache.org/hadoop/WordCount ).
after the last line, "job.waitForCompletion(true);"
I add some code implemented by Jython.
It means the libraries for Jythoon is only needed on namenode.
However, I added all libraries for Jython to a single jar, and then
executed it
hadoop jar wordcount.jar in out
The wordcount is done without any problem.
The problem I want to solve is I have to heavy libraries for Jython that is not needed for the slave nodes(mappers and reducers). the jar is almost 15M (upper than 14M is for Jython).
Can I split them, and get the same results?
Nobody knows this question.
I've solved this problem as follows: even if it's not the best.
Simply, copy jython.jar to /usr/local/hadoop (or path of hadoop installed) which is the default classpath of hadoop, and make a jar without jython.jar
If you need very big libraries to mapreduce task, then
upload jython.jar to hdfs
hadoop fs -put jython.jar Lib/jython.jar
add the follow line to your main code
DistributedCache.addFileToClassPath(new URI("Lib/jython.jar"));