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From Jacob Metcalf <jacob_metc...@hotmail.com>
Subject RE: Hadoop 0.23, Avro Specific 1.6.3 and "org.apache.avro.generic.GenericData$Record cannot be cast to "
Date Sun, 13 May 2012 21:03:48 GMT

Ken, thanks for getting back to me. 
1) The Avro specific classes are generated and packed in the same JAR as the mapper and reducer.
Attached is my example http://markmail.org/download.xqy?id=m6te4atgmyrrqyv5&number=1 which
in parallel I am also getting working on MRUnit so am discussing on that forum. If you want
to build it you will need to build odagio-avro.
I agree and cannot comprehend how if the mapper can serialize, the reducer cannot deserialize.
My only guess is that the reducer is running in a separate JVM and it is only this which has
classpath issues. Logically the mapper output would be deserialized before my reducer is instantiated.
I noticed that the JAR does get exploded so my only thought is that there is something going
wrong in the Cygwin/Hadoop layer at reduction.
2) Yes the latest version of avro is in my Job Jar. However I am again not sure how to manipulate
the Hadoop classpath to ensure it is first. This is possibly more a topic for the Hadoop list.

From: kkrugler_lists@transpac.com
Subject: Re: Hadoop 0.23, Avro Specific 1.6.3 and "org.apache.avro.generic.GenericData$Record
cannot be cast to "
Date: Sun, 13 May 2012 11:18:13 -0700
To: user@avro.apache.org

Hi Jacob,
On May 13, 2012, at 4:48am, Jacob Metcalf wrote:I have just spent several frustrating hours
on getting an example MR job using Avro working with Hadoop and after finally getting it working
I thought I would share my findings with everyone.
I wrote an example job trying to use Avro MR 1.6.3 to serialize between Map and Reduce then
attempted to deploy and run. I am setting up a development cluster with Hadoop 0.23 running
pseudo-distributed under cygwin. I ran my job and it failed with:
"org.apache.avro.generic.GenericData$Record cannot be cast to net.jacobmetcalf.avro.Room"

Where Room is an Avro generated class. I found two problems. The first I have partly solved,
the second one is more to do with Hadoop and is as yet unsolved:
1) Why when I am using Avro Specific does it end up going Generic?
When deserializing SpecificDatumReader.java attempts to instantiate your target class through
reflection. If it fails to create your class it defaults to a GenericData.Record. This Doug
has explained here: http://mail-archives.apache.org/mod_mbox/avro-user/201101.mbox/%3C4D2B6D56.2070108@apache.org%3E
But why it is doing it was a little harder to work out. Debugging I saw the SpecificDatumReader
could not find my class in its classpath. However in my Job Runner I had done: 
		job.setJarByClass(HouseAssemblyJob.class);	// This should ensure the JAR is distributed
around the cluster
I expected with this Hadoop would distribute my Jar around the cluster. It may be doing the
distribution but it definitely did not add it to the Reducers classpath. So to get round this
I have now set HADOOP_CLASSPATH to the directory I am running from. This is not going to work
in a real cluster where the Job Runner is on a different machine to where the Reducer so I
am keen to figure out whether the problem is Hadoop 0.23, my environment variables or the
fact I am running under Cygwin.
If your reducer is running, then Hadoop must have distributed your job jar.
In that case, any class that's actually in your job jar (in the proper position) will be distributed
and on the classpath.
Sometimes the problem is that you've got a dependent jar, which then needs to be in the "lib"
subdirectory inside of your job jar. Are you maybe building your Avro generated classes into
a separate jar, and then adding that to the job jar?
Finally, running under Cygwin is…challenging. I teach a Hadoop class, and often the hardest
part of the lab is getting everybody's Cygwin installation working with Hadoop. The fact that
you've got pseudo-distributed mode working on Cygwin is impressive in itself, but I would
suggest trying your job on a real cluster, e.g. use Elastic MapReduce.
2) How can I upgrade Hadoop 0.23 to use Avro 1.6.3 ?
Whilst debugging I realised that Hadoop is shipping with Avro 1.5.3. I however want to use
1.6.3 (and 1.7 when it comes out) because of its support for immutability & builders in
the generated classes. I probably could just hack the old Avro lib out of my Hadoop distribution
and drop the new one in. However I thought it would be cleaner to get Hadoop to distribute
my jar to all datanodes and then manipulate my classpath to get the latest version of Avro
to the top. So I have packaged Avro 1.6.3 into my job jar using Maven assembly
Did you ensure that it's inside of the /lib subdirectory? What does your job jar look like
(via "jar tvf <path to job jar>")?
-- Ken
and tried to do this in my JobRunner:
		job.setJarByClass( MyJob.class);	                                                      
                   // This should ensure the JAR is distributed around the cluster	      
 config.setBoolean( MRJobConfig.MAPREDUCE_JOB_USER_CLASSPATH_FIRST, true ); // ensure my version
of avro?
But it continues to use 1.5.3. I suspect it is again to do with my HADOOP_CLASSPATH which
has avro-1.5.3 in it:
                export HADOOP_CLASSPATH="$HADOOP_COMMON_HOME/share/hadoop/mapreduce/*"
If anyone has done this and has any ideas please let me know?

--------------------------Ken Kruglerhttp://www.scaleunlimited.comcustom big data solutions
& trainingHadoop, Cascading, Mahout & Solr

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