CCA175 Exam Dumps : CCA Spark and Hadoop Developer Exam

Solution :

b.foidByKey("")(_ + J.collect

foldByKey [Pair]

Very similar to fold, but performs the folding separately for each key of the RDD. This function is only available if the RDD consists of two-component tuples

Listing Variants

def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V}]

def foldByKey(zeroValue: V, numPartitions: lnt)(func: (V, V) => V): RDD[(K, V)]

def foldByKey(zeroValue: V, partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V}]

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)

Solution :

Step 1 : create employee.json tile locally.

vi employee.json (press insert) past the content.

Step 2 : Upload this tile to hdfs, default location hadoop fs -put employee.json

Step 3 : Write spark script

#lmport SQLContext

from pyspark import SQLContext

#Create instance of SQLContext sqIContext = SQLContext(sc)

#Load json file

employee = sqlContext.jsonFile("employee.json")

#Register RDD as a temp table employee.registerTempTablef'EmployeeTab"}

#Select data from Employee table

employeelnfo = sqlContext.sql("select * from EmployeeTab"}

#lterate data and print

for row in employeelnfo.collect():

print(row)

Step 4 : Write dataas a Text file employeelnfo.toJSON().saveAsTextFile("employeeJson1")

Step 5: Check whether data has been created or not hadoop fs -cat employeeJsonl/part"