See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create all three files in hdfs (We will do using Hue). However, you can first create in local filesystem and then upload it to hdfs.
Step 2 : Load EmployeeName.csv file from hdfs and create PairRDDs
val name = sc.textFile("spark5/EmployeeName.csv")
val namePairRDD = name.map(x=> (x.split(",")(0),x.split('V')(1)))
Step 3 : Load EmployeeSalary.csv file from hdfs and create PairRDDs
val salary = sc.textFile("spark5/EmployeeSalary.csv")
val salaryPairRDD = salary.map(x=> (x.split(",")(0),x.split(",")(1)))
Step 4 : Join all pairRDDS
val joined = namePairRDD.join(salaryPairRDD}
Step 5 : Remove key from RDD and Salary as a Key. val keyRemoved = joined.values
Step 6 : Now swap filtered RDD.
val swapped = keyRemoved.map(item => item.swap)
Step 7 : Now groupBy keys (It will generate key and value array) val grpByKey = swapped.groupByKey().collect()
Step 8 : Now create RDD for values collection
val rddByKey = grpByKey.map{case (k,v) => k->sc.makeRDD(v.toSeq)}
Step 9 : Save the output as a Text file.
rddByKey.foreach{ case (k,rdd) => rdd.saveAsTextFile("spark5/Employee"+k)}

See the explanation for Step by Step Solution and configuration.
Explanation:
Solution : c.sortByKey(false).collect
sortByKey [Ordered] : This function sorts the input RDD's data and stores it in a new RDD.
"The output RDD is a shuffled RDD because it stores data that is output by a reducer which has been shuffled. The implementation of this function is actually very clever.
First, it uses a range partitioner to partition the data in ranges within the shuffled RDD.
Then it sorts these ranges individually with mapPartitions using standard sort mechanisms.

See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create file in hdfs (We will do using Hue). However, you can first create in local filesystem and then upload it to hdfs.
Step 2 : Load user.csv file from hdfs and create PairRDDs val csv =
sc.textFile("spark6/user.csv")
Step 3 : split and clean data
val headerAndRows = csv.map(line => line.split(",").map(_.trim))
Step 4 : Get header row
val header = headerAndRows.first
Step 5 : Filter out header (We need to check if the first val matches the first header name) val data = headerAndRows.filter(_(0) != header(O))
Step 6 : Splits to map (header/value pairs)
val maps = data.map(splits => header.zip(splits).toMap)
step 7: Filter out the user "myself
val result = maps.filter(map => mapf'id") != "myself")
Step 8 : Save the output as a Text file. result.saveAsTextFile("spark6/result.txt")

See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
step 1 : Create a file first using Hue in hdfs.
Step 2 : Load tile as an RDD
val rawlines = sc.textFile("spark10/sales.txt")
Step 3 : Create a case class, which can represent its column fileds. case class
Employee(dep: String, des: String, cost: Double, state: String)
Step 4 : Split the data and create RDD of all Employee objects.
val employees = rawlines.map(_.split(",")).map(row=>Employee(row(0), row{1), row{2).toDouble, row{3)))
Step 5 : Create a row as we needed. All group by fields as a key and value as a count for each employee as well as its cost, val keyVals = employees.map( em => ((em.dep, em.des, em.state), (1 , em.cost)))
Step 6 : Group by all the records using reduceByKey method as we want summation as well. For number of employees and their total cost, val results = keyVals.reduceByKey{
(a,b) => (a._1 + b._1, a._2 + b._2)} // (a.count + b.count, a.cost + b.cost)}
Step 7 : Save the results in a text file as below.
results.repartition(1).saveAsTextFile("spark10/group.txt")

See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create directory
hdfs dfs -mkdir hdfs_commands
Step 2 : Create a file in hdfs named data.txt in hdfs_commands. hdfs dfs -touchz hdfs_commands/data.txt
Step 3 : Now copy this data.txt file on local filesystem, however while copying file please make sure file properties are not changed e.g. file permissions.
hdfs dfs -copyToLocal -p hdfs_commands/data.txt/home/cloudera/Desktop/HadoopExam
Step 4 : Now create a file in local directory named data_local.txt and move this file to hdfs in hdfs_commands directory.
touch data_local.txt
hdfs dfs -moveFromLocal /home/cloudera/Desktop/HadoopExam/dataJocal.txt hdfs_commands/
Step 5 : Create a file data_hdfs.txt in hdfs_commands directory and copy it to local file system.
hdfs dfs -touchz hdfscommands/data hdfs.txt
hdfs dfs -getfrdfs_commands/data_hdfs.txt /home/cloudera/Desktop/HadoopExam/
Step 6 : Create a file in local filesystem named filel .txt and put it to hdfs touch filel.txt hdfs dfs -put/home/cloudera/Desktop/HadoopExam/file1.txt hdfs_commands/