Previously, we set up a Scala application in order to execute a simple word count on Hadoop.
What comes next is uploading our application to HDInsight. So, we shall proceed in creating a Hadoop cluster on HDInsight.
Then, we will create the Hadoop cluster.
As you can see, we specify the admin console credentials and the SSH user to log into the head node.
Our Hadoop cluster will be backed by an Azure storage account.
Then, it is time to upload our text files to the Azure storage account.
For more information on managing a storage account with Azure CLI, check the official guide. Any text file will work.
azure storage blob upload mytext.txt scalahadoopexample example/data/input.txt
Now, we can SSH to our Hadoop node.
First, let’s run the examples that come packaged with the HDInsight Hadoop cluster.
hadoop jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-mapreduce-examples.jar wordcount /example/data/input.txt /example/data/results
Check the results:
hdfs dfs -text /example/data/results/part-r-00000
And then we are ready to SCP the Scala code to our Hadoop node and issue as the word count.
hadoop jar ScalaHadoop-assembly-1.0.jar /example/data/input.txt /example/data/results2
And again, check the results:
hdfs dfs -text /example/data/results2/part-r-00000
That’s it! HDinsight makes it pretty straight forward!