CCA175 Exam Dumps : CCA Spark and Hadoop Developer Exam

Solution :

Step 1 : Create 2 files first using Hue in hdfs.

Step 2 : Load all file as an RDD

val technology = sc.textFile(Msparkl2/technology.txt").map(e => e.splitf',"))

val salary = sc.textFile("spark12/salary.txt").map(e => e.split("."))

Step 3 : Now create Key.value pair of data and join them.

val joined = technology.map(e=>((e(0),e(1)),e(2))).join(salary.map(e=>((e(0),e(1)),e(2))))

Step 4 : Save the results in a text file as below.

joined.repartition(1).saveAsTextFile("spark12/multiColumn Joined.txt")

Solution :

Step 1 : Create an RDD out of this list

val rdd = sc.parallelize(List( ("Deeapak" , "male", 4000}, ("Deepak" , "male", 2000), ("Deepika" , "female", 2000),("Deepak" , "female", 2000), ("Deepak" , "male", 1000} , ("Neeta" , "female", 2000}}}

Step 2 : Convert this RDD in pair RDD

val byKey = rdd.map({case (name,sex,cost) => (name,sex)->cost})

Step 3 : Now group by Key

val byKeyGrouped = byKey.groupByKey

Step 4 : Nowsum the cost for each group

val result = byKeyGrouped.map{case ((id1,id2),values) => (id1,id2,values.sum)}

Step 5 : Save the results result.repartition(1).saveAsTextFile("spark12/result.txt")

Solution:

Step 1 : Import Single table .

sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera -table=orders --target-dir=p89_orders - -m1

sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera -table=order_items ~target-dir=p89_ order items -m 1

Note : Please check you dont have space between before or after '=' sign. Sqoop uses the MapReduce framework to copy data from RDBMS to hdfs

Step 2 : Read the data from one of the partition, created using above command, hadoopfs -cat p89_orders/part-m-00000 hadoop fs -cat p89_order_items/part-m-00000

Step 3 : Load these above two directory as RDD using Spark and Python (Open pyspark terminal and do following). orders = sc.textFile("p89_orders") orderitems = sc.textFile("p89_order_items")

Step 4 : Convert RDD into key value as (orderjd as a key and rest of the values as a value)

#First value is orderjd

ordersKeyValue = orders.map(lambda line: (int(line.split(",")[0]), line))

#Second value as an Orderjd

orderltemsKeyValue = orderltems.map(lambda line: (int(line.split(",")[1]), line))

Step 5 : Join both the RDD using orderjd

joinedData = orderltemsKeyValue.join(ordersKeyValue)

#print the joined data

tor line in joinedData.collect():

print(line)

Format of joinedData as below.

[Orderld, 'All columns from orderltemsKeyValue', 'All columns from orders Key Value']

Step 6 : Now fetch selected values Orderld, Order date and amount collected on this order.

revenuePerOrderPerDay = joinedData.map(lambda row: (row[0]( row[1][1].split(",")[1]( f!oat(row[1][0].split('\M}[4]}}}

#printthe result

for line in revenuePerOrderPerDay.collect():

print(line)

Step 7 : Select distinct order ids for each date.

#distinct(date,order_id)

distinctOrdersDate = joinedData.map(lambda row: row[1][1].split('\")[1] + "," + str(row[0])).distinct()

for line in distinctOrdersDate.collect(): print(line)

Step 8 : Similar to word count, generate (date, 1) record for each row. newLineTuple = distinctOrdersDate.map(lambda line: (line.split(",")[0], 1))

Step 9 : Do the count for each key(date), to get total order per date. totalOrdersPerDate = newLineTuple.reduceByKey(lambda a, b: a + b}

#print results

for line in totalOrdersPerDate.collect():

print(line)

step 10 : Sort the results by date sortedData=totalOrdersPerDate.sortByKey().collect()

#print results

for line in sortedData:

print(line)