Run WordCount with Scala and Spark on HDInsight

Previously we tried to solve the word count problem with a Scala and Spark approach.
The next step is to deploy our solution to HDInsight using spark, hdfs, and scala

We shall provision a Sprak cluster.

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Since we are going to use HDInsight we can utilize hdfs and therefore use the azure storage.

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Then we choose our instance types.

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And we are ready to create the Spark cluster.

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Our data shall be uploaded to the hdfs file system
To do so we will upload our text files to the azure storage account which is integrated with hdfs.

For more information on managing a storage account with azure cli check the official guide. Any text file will work.

azure storage blob upload mytextfile.txt sparkclusterscala  example/data/mytextfile.txt

Since we use hdfs we shall make some changes to the original script

val text = sc.textFile("wasb:///example/data/mytextfile.txt")
val counts = text.flatMap(line => line.split(" ")).map(word => (word,1)).reduceByKey(_+_)
counts.collect

Then we can upload our scala class to the head node using ssh

scp WordCountscala.scala demon@{your cluster}-ssh.azurehdinsight.net:/home/demo/WordCountscala.scala

Again in order to run the script, things are pretty straightforward.

spark-shell -i WordCountscala.scala

And once the task is done we are presented with the spark prompt. Plus we can now save our results to the hdfs file system.

scala> counts.saveAsTextFile("/wordcount_results")

And do a quick check.

hdfs dfs -ls /wordcount_results/
hdfs dfs -text /wordcount_results/part-00000

Run Scala implemented Storm topologies on HDInsight

Previously we set up a Scala implemented storm topology in order to count words.

What comes next is uploading our topology to HDInsight.

So we shall proceed in creating a Storm topology on HDInsight.

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Then we choose the instance types.

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Next step is to upload our jar file to the head node in order to deploy it. We can use scp for this purpose.

scp target/scala-2.12/ScalaStorm-assembly-1.0.jar  {your user}@{your azure endpoint}:/home/demo

Now we can ssh to our storm cluster’s head node and issue the storm command.

storm jar ScalaStorm-assembly-1.0.jar com.gkatzioura.scala.storm.WordCountTopology word-count-stream-scala

Then we can check our topology by navigating to https://{your cluster}.azurehdinsight.net/stormui

Run Scala implemented Hadoop Jobs on HDInsight

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.

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Then we will create the hadoop cluster.

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As you can see we specify the admin console credentials and the ssh user to login to the head node.

Our hadoop cluster will be backed by an azure storage account.

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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 HInsight 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 wordcount.

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!