Experiments to test cascading.hbase
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Cascading.hbase is a module that adds an HBase tap to the Cascading Framework. Cascading is a Data Processing API, Process Planner, and Process Scheduler used for defining and executing complex, scale-free, and fault tolerant data processing workflows on an Apache Hadoop cluster. All without having to ‘think’ in MapReduce.

I could not find any full end-to-end examples of a cascading.hbase program that would run on a Hadoop/HBase cluster. This was a drag as I was trying to figure out how to make cascading.hbase to work at all. It looks like no one actually makes sure that the Unit Tests actually run. And all the documents say to use the Unit tests as examples of how to use cascading.hbase.

The HBase Wiki page on cascading.hbase has an example but it doesn’t show a Main, any of the import statements or what kind of input data the program expected, let alone how to run it.

This project takes that example and trys to show what you need to do to actually make it work.

Version Interactions

One of the big issues is that cascading.hbase does not have an offical maintainer at this point. In the meantime, Hadoop and HBase has been evolving and have depreciated many of the API’s that Cascading and cascading.hbase use.

There are several forks of cascading.hbase and its not clear as to what version of Cascading/Hadoop/HBase each of the forks will or will not work with. As I was stumbling around all this, I saw that Ryan Rawson had updated his fork in the last couple of days. (most of the other forks are months if not years old). I pinged Ryan and he informed me that StumbleUpon is actively using cascading.hbase. He told me what versions of Cascading, HBase and Hadoop that his fork is known to work with. This was enough to give me hope that its possible to make things work. I still didn’t have something to test it with. Thus this project.

So far I have been able to get this example working with HBase-0.90.0. I had to use the following:

Building the dependencies

You will need to build the jars for cascading and cascading.hbase from the above git repositories. For this example we’ll assume that you will create a directory cascading in your home directory. We’ll refer to this top level directory as ~/work. You can of course put it anywhere you like but all these examples will assume that all the dependencies are in this directory as parallel directories.

Also asssumes you have downloaded the Haddoop and HBase gziped tar distributions in ~/Downloads.

mkdir ~/work
cd ~/work
tar xvzf ~/Downloads/hadoop-0.20.2.tar.gz
tar xvzf ~/Downloads/hbase-0.90.0.tar.gz
git clone  https://github.com/jdcryans/cascading cascading-jdcryans
git clone https://github.com/ryanobjc/cascading.hbase cascading.hbase-ryanobjc
git clone https://github.com/rberger/cascading.hbase.experiments.git

Build cascading

cd cascading-jdcryans
ant -Dhadoop.home=../hadoop-0.20.2 jar
cd ..

You should end up with the following jars in ~/work/cascading-jdcryans/build:

  • cascading-1.0.17.jar
  • cascading-core-1.0.17.jar
  • cascading-test-1.0.17.jar
  • cascading-xml-1.0.17.jar

Build cascading.hbase

cd cascading.hbase-ryanobjc ant -Dhadoop.home=../hadoop-0.20.2 -Dhbase.home=../hbase-0.90.0 \ -Dcascading.home=../cascading-jdcryans -Dcascading.release.version=1.0.17 jar cd ..

You should end up with the following jar in ~/work/cascading.hbase-ryanobjc/build:

  • cascading-hbase-0.0.2.jar

Building jar to pass to Hadoop

The ant build.xml is based on the build.xml from the cascading.samples.wordcount build.xml.

The build.xml assumes that all the needed packages are parellel to this project. You can override them on the command line but I found it made more sense to update the build.xml to help my sanity.

When you run ant jar_, ant will build the program (which is in src/java/test_cascadinghbase/Main.java) and then assemble a jar sutiable for executing by hadoop.

cd cascading.hbase.experiments
ant jar
cd ..

You should end up with the following jar in ~/work/cascading.hbase.experiments/build:

  • test_cascading_hbase.jar

You can see what is in the jar with the jar tf command the following shows the command and its expected output:

jar tf build/test_cascading_hbase.jar 

Running it on a Hadoop/HBase cluster

Its beyond this document to explain how to get up a Hadoop/HBase cluster. It just assumes you have that.

One thing that you do have to make sure is that the hadoop jobtracker and slaves that run mapreduce jobs (tasktrackers) have things set up so that they can include the hbase Classes (hbase-0.90.0.jar) in their path.

This can most easily be done by adding the following lines into the $HADOOP_HOME/conf/hadoop_env.sh file (where HBASE_HOME is set to where your hbase is insalled and all the versions are appropriate for your environment. Also if you already have a HADOOP_CLASSPATH variable set, just make sure it has these items in it as well):

[ -z "$HBASE_HOME" ] && HBASE_HOME=/home/hadoop/hbase
export HADOOP_CLASSPATH="$HADOOP_CLASSPATH:$HBASE_HOME/hbase-0.90.0.jar:$HBASE_HOME/conf:$HBASE_HOME/lib/zookeeper-3.3.2.jar"

Copy the jar file
~/work/cascading.hbase.experiments/build/test_cascading_hbase.jar to
someplace on your Hadoop master.

Also copy the file ~/work/cascading.hbase.experiments/data/small.txt to the same directory you copied the jar to.

You may have to become the user that owns/runs hadoop. The following commands assume that the hadoop executable is in your PATH. It is usually in $HADOOP_HOME/bin/hadoop.

You’ll need to copy the small.txt file into the Hadoop File System (HDFS):

hadoop fs -mkdir /user/hadoop/data
hadoop fs -copyFromLocal small.txt /user/hadoop/data

You can then run the cascading.hbase job with the command:

hadoop jar test_cascading_hbase.jar data/small.txt

You should see an emence amount of output that hopefully does not have any Exceptions in it. It takes a few minutes and there is a point where nothing happens for a few minutes.

If all has gone well the last part of the output should be:

11/03/14 05:52:25 INFO mapred.TableInputFormatBase: split: 0->ip-10-194-10-15.ec2.internal:,
iterator.next() = fields: ['num', 'lower', 'upper'] tuple: ['1', 'c', 'C']
iterator.next() = fields: ['num', 'lower', 'upper'] tuple: ['2', 'd', 'D']
iterator.next() = fields: ['num', 'lower', 'upper'] tuple: ['3', 'c', 'C']
iterator.next() = fields: ['num', 'lower', 'upper'] tuple: ['4', 'd', 'D']
iterator.next() = fields: ['num', 'lower', 'upper'] tuple: ['5', 'e', 'E']

If there were Exceptions, you have to figure them out. If the Exception happened inside of the mapreduce job, you wan’t see much on the stdout. You’ll have to look at the hadoop jobtracker logs. The location of the hadoop logs is system dependent. Sometimes they are in $HADOOP_HOME/logs

The Future

I hope to get this to work with HBase 0.20.3 (which we are still using, though hopefuly not for long). The main next goal is to use cascading.hbase with Cascalog.