I've a UDF which I use to do custom processing on the records. In the eval function I am using a third party jar for processing. I saw the job jar file, but it does not include this dependency. Is there any way to include dependent jar in the job jar ?
(For testing I am running the cluster in the local mode).
Or can I use distributed cache to make the dependent jar available to the UDF ?
I've tried registering the dependent jars in the pig. For the first registered jar (all udfs are bundled in this jar) I do not face the issues. But for the second jar, I am facing issues when UDF tries to access the class from it.
REGISTER '/home/user/pig/udfrepository/projectUDF.jar'
REGISTER '/home/user/thridpartyjars/xyz.jar';
The logs I get on the console are like this :
2013-08-11 10:35:02,485 [Thread-14] WARN org.apache.hadoop.mapred.LocalJobRunner - job_local_0001
java.lang.NoSuchMethodError: org.xyz.abc.convertToOtherFormat(Lorg/DateTimeZone;)Lorg/DateTime;
at com.myproject.MyUDF.exec(MyUDF.java:70)
Any help on this is highly appreciated.
Thanks in advance.
The same issue I found resolved and documented here :
http://hadooptips.wordpress.com/2013/08/13/nosuchmethoderror-while-using-joda-time-2-2-jar-in-pig/
Related
I am trying to understand on what is the correct way of submitting a MR (for that matter a Spark based Java) job to YARN cluster.
Consider the situation below:
Using client machine develop code (MR or Spark) jobs, and say the codes uses 3rd party jar's. Now, when a developer has to submit the job to the YARN cluster, what is the correct way of submitting the job to cluster so that there is no run time exception of class not found. Since job is submitted as jar file, how can a developer "put" the 3rd party jars?
I am having difficulty in understanding this, can anyone help me understand this?
You have to simply build a "fat jar," with Gradle or Maven, that contains not only your compiled code but also all transitive dependencies.
You can use either the Maven Assembly Plugin or any of the Gradle plugins like the Shadow Plugin.
The output of these is what you should supply to spark-submit.
So, i would like to create an apache spark integration in my spring application by following this guide provided by spring (http://docs.spring.io/spring-hadoop/docs/current/reference/html/springandhadoop-spark.html). Now i have a few questions as it seems that sparks 2.0.1 does not include the spark-assembly jar.
What are my options in proceeding with this as it seems that the integration is dependant on the jar?
If i am able to find the old jar would i be able to use it with apache 2.0.1?
Is there a way to get the jar with apache 2.0.1?
Yes you are right - spark 2.0.1 does not include uber jar with itself like in 1.6.x and below (eg. spark-1.6.2-bin-hadoop2.6\lib\spark-assembly-1.6.2-hadoop2.6.0.jar)
Spark 2.0.0+ spark-release-2-0-0.html doesn't require a fat assembly uber jar. However when you compare content of spark-assembly-1.6.2-hadoop2.6.0 and libs (content of jar files) in spark-2.0.0-bin-hadoop2.7\jars\ you can see almost the same content with same classes, packages etc.
If i am able to find the old jar would i be able to use it with apache 2.0.1?
Personally I dont think so. There might be potentionally some problems with backward compatibility and it is weird to have something that was removed in latest version.
You are right that SparkYarnTasklet need assembly jar because there is some postPropertiesSet validation:
#Override
public void afterPropertiesSet() throws Exception {
Assert.hasText(sparkAssemblyJar, "sparkAssemblyJar property was not set. " +
"You must specify the path for the spark-assembly jar file. " +
"It can either be a local file or stored in HDFS using an 'hdfs://' prefix.");
But, this sparkAssemblyJar is only used in sparkConf.set("spark.yarn.jar", sparkAssemblyJar);
when you will use SparkYarnTasklet, the program will probably fail on validation (You can try to extend SparkYarnTasklet and Override afterPropertiesSet without validation)
And documentation about "spark.yarn.jar:"
To make Spark runtime jars accessible from YARN side, you can specify
spark.yarn.archive or spark.yarn.jars. For details please refer to
Spark Properties. If neither spark.yarn.archive nor spark.yarn.jars is
specified, Spark will create a zip file with all jars under
$SPARK_HOME/jars and upload it to the distributed cache.
so take a look into properties: spark.yarn.jars and spark.yarn.archive.
So compare what is spark.yarn.jar in 1.6.x- and 2.0.0+
spark.yarn.jar in 1.6.2 :
The location of the Spark jar file, in case overriding the default location is desired. By default, Spark on YARN will use a Spark jar installed locally, but the Spark jar can also be in a world-readable location on HDFS. This allows YARN to cache it on nodes so that it doesn't need to be distributed each time an application runs. To point to a jar on HDFS, for example, set this configuration to hdfs:///some/path.
spark.yarn.jar in 2.0.1:
List of libraries containing Spark code to distribute to YARN
containers. By default, Spark on YARN will use Spark jars installed
locally, but the Spark jars can also be in a world-readable location
on HDFS. This allows YARN to cache it on nodes so that it doesn't need
to be distributed each time an application runs. To point to jars on
HDFS, for example, set this configuration to hdfs:///some/path. Globs
are allowed.
but this seems to set all jars one by one.
But in 2.0.0+ there is spark.yarn.archive that replaces spark.yarn.jars and provide a way how to avoid passing jars one by one - create archive with all jars in root "dir".
I think spring-hadoop will reflect changes in 2.0.0+ in a few weeks, but for "quick fix" I will probably try to override SparkYarnTasklet and reflect changes for 2.0.1 - as I saw exactly execute and afterPropertiesSet methods.
I want to write some sort of "bootstrap" class, which will watch MQ for incoming messages and submit map/reduce jobs to Hadoop. These jobs use some external libraries heavily. For the moment I have the implementation of these jobs, packaged as ZIP file with bin,lib and log folders (I'm using maven-assembly-plugin to tie things together).
Now I want to provide small wrappers for Mapper and Reducer, which will use parts of the existing application.
As far as I learned, when a job is submitted, Hadoop tries to find out JAR file, which has the mapper/reducer classes, and copy this jar over network to data node, which will be used to process the data. But it's not clear how do I tell Hadoop to copy all dependencies?
I could use maven-shade-plugin to create an uber-jar with the job and dependencies, And another jar for bootstrap (which jar would be executed with hadoop shell-script).
Please advice.
One way could be to put the required jars in distributed cache. Another alternative would be to install all the required jars on the Hadoop nodes and tell TaskTrackers about their location. I would suggest you to go through this post once. Talks about the same issue.
Use maven to manage the dependencies and ensure the correct versions are used during builds and deployment. Popular IDE's have maven support that makes it so you don't have to worry about building class paths for edit and build. Finally, you can instruct maven to build a single jar (a "jar-with-dependencies") containing your app and all dependencies, making deployment very easy.
As for dependencies, like hadoop, which are guaranteed to be in the runtime class path, you can define them with a scope of "provided" so they're not included in the uber jar.
Use -libjars option of hadoop launcher script for specify dependencies for jobs running on remotes JVMs;
Use $HADOOP_CLASSPATH variable for set dependencies for JobClient running on local JVM
Detailed discussion is here: http://grepalex.com/2013/02/25/hadoop-libjars/
I'm new with Hadoop. I'm using Hadoop 0.22.
In the Driver I'used this code:
Job job = Job.getInstance(configuration);
...
job.addArchiveToClassPath(new Path(JAR_DIR);
...
In the Map class what code I have to use to add the jar in the local classpath?
More details
I have a job that need in the map and reduce phases htmlunit.jar.I add this jar to classpath with the code above, but when I submit the job, I have a ClassNotFoundException at line that I use htmlunit references.If the code above it's ok, and the DistributedCache add automatically the jar in the tasktrackers classpath, what could be the problem?
I also have to use the option -libjars htmlunit.jar when I submit the job? I have to use another hadoop component?
You don't need to do anything.
Once you add a jar to the job classpath what you're saying is
"include this in the class path of the map and reduce jobs"
So long as your mappers and reducers extend from the Mapper and Reducer base classes then it will 'just work'.
Worth noting, you should probably instead use addFileToClassPath for each individual Jar you need.
An alternative (we do this) is to create one single jar containing your source and your dependencies.
Build your code jar as usual, then create a subdirectory in the jar called 'lib', and add all of your dependency jars in here. Then your entire job is self contained and you don't need to worry about adding other jars to the distributed cache.
So for example you'd have a jar with the following contents:
/com/example/Something.class
/com/example/SomethingElse.class
/lib/dependency.jar
/lib/dependency2.jar
(a jar is just a zip file, so you can use regular zip creation utilities to build it)
For various reasons this also performs better than adding the .class files of your dependency to the jar directly.
I wrote some mapreduce jobs that reference a few external jars.
so I added them into the CLASSPATH of the "running" cluster in order to run jobs.
Once I tried to run them, I got class not found exceptions.
I Googled for ways to fix it and I found that I needed to restart the cluster for applying
the changed CLASSPATH, and it actually worked.
Oh, yuck!
Should I really need to restart a cluster every time I add new jars into the CLASSPATH?
I don't think that it makes sense.
Does anyone know how to apply the changes without restarting them?
I think I need to add some detail to beg your advice.
I wrote a custom hbase filter class and packed it in a jar.
And I wrote a mapreduce job that uses the custom filter class and packed it in an another jar.
Because the filter class jar wasn't in the class path of my "running" cluster, I added it.
But I couldn't succeed to run the job until I restarted the cluster.
Of course, I know I could packed the filter class and the job in a single jar together.
But I didn't mean it.
And I'm curious I should restart the cluster again if I need to add new external jars?
Check the Cloudera article for including 3rd party libraries required for the Job. Option (1) and (2) don't require the Cluster to be restarted.
You could have such a system that dynamically resolve class names to an interface type to process your data.
Just my 2 cents.