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CCA175 Practice Questions

Question # 1

Problem Scenario 2 :
There is a parent organization called "ABC Group Inc", which has two child companies
named Tech Inc and MPTech.
Both companies employee information is given in two separate text file as below. Please do
the following activity for employee details.
Tech Inc.txt
1,Alok,Hyderabad
2,Krish,Hongkong
3,Jyoti,Mumbai
4,Atul,Banglore
5,Ishan,Gurgaon
MPTech.txt
6,John,Newyork
7,alp2004,California
8,tellme,Mumbai9,Gagan21,Pune
10,Mukesh,Chennai
1. Which command will you use to check all the available command line options on HDFS
and How will you get the Help for individual command.
2. Create a new Empty Directory named Employee using Command line. And also create
an empty file named in it Techinc.txt
3. Load both companies Employee data in Employee directory (How to override existing file
in HDFS).
4. Merge both the Employees data in a Single tile called MergedEmployee.txt, merged tiles
should have new line character at the end of each file content.
5. Upload merged file on HDFS and change the file permission on HDFS merged file, so
that owner and group member can read and write, other user can read the file.
6. Write a command to export the individual file as well as entire directory from HDFS to
local file System.

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Check All Available command hdfs dfs
Step 2 : Get help on Individual command hdfs dfs -help get
Step 3 : Create a directory in HDFS using named Employee and create a Dummy file in it
called e.g. Techinc.txt hdfs dfs -mkdir Employee
Now create an emplty file in Employee directory using Hue.
Step 4 : Create a directory on Local file System and then Create two files, with the given
data in problems.
Step 5 : Now we have an existing directory with content in it, now using HDFS command
line , overrid this existing Employee directory. While copying these files from local file
System to HDFS. cd /home/cloudera/Desktop/ hdfs dfs -put -f Employee
Step 6 : Check All files in directory copied successfully hdfs dfs -Is Employee
Step 7 : Now merge all the files in Employee directory, hdfs dfs -getmerge -nl Employee
MergedEmployee.txt
Step 8 : Check the content of the file. cat MergedEmployee.txt
Step 9 : Copy merged file in Employeed directory from local file ssytem to HDFS. hdfs dfs -
put MergedEmployee.txt Employee/
Step 10 : Check file copied or not. hdfs dfs -Is Employee
Step 11 : Change the permission of the merged file on HDFS hdfs dfs -chmpd 664
Employee/MergedEmployee.txt
Step 12 : Get the file from HDFS to local file system, hdfs dfs -get Employee


Question # 2

Problem Scenario 37 : ABCTECH.com has done survey on their Exam Products feedback
using a web based form. With the following free text field as input in web ui.
Name: String
Subscription Date: String
Rating : String
And servey data has been saved in a file called spark9/feedback.txt
Christopher|Jan 11, 2015|5
Kapil|11 Jan, 2015|5
Thomas|6/17/2014|5
John|22-08-2013|5
Mithun|2013|5
Jitendra||5
Write a spark program using regular expression which will filter all the valid dates and save
in two separate file (good record and bad record)

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create a file first using Hue in hdfs.
Step 2 : Write all valid regular expressions sysntex for checking whether records are having
valid dates or not.
val regl =......(\d+)\s(\w{3})(,)\s(\d{4}).......r//11 Jan, 2015
val reg2 =......(\d+)(U)(\d+)(U)(\d{4})......s II 6/17/2014
val reg3 =......(\d+)(-)(\d+)(-)(\d{4})""".r//22-08-2013
val reg4 =......(\w{3})\s(\d+)(,)\s(\d{4})......s II Jan 11, 2015
Step 3 : Load the file as an RDD.
val feedbackRDD = sc.textFile("spark9/feedback.txt"}
Step 4 : As data are pipe separated , hence split the same. val feedbackSplit =
feedbackRDD.map(line => line.split('|'))
Step 5 : Now get the valid records as well as , bad records.
val validRecords = feedbackSplit.filter(x =>
(reg1.pattern.matcher(x(1).trim).matches|reg2.pattern.matcher(x(1).trim).matches|reg3.patt
ern.matcher(x(1).trim).matches | reg4.pattern.matcher(x(1).trim).matches))
val badRecords = feedbackSplit.filter(x =>
!(reg1.pattern.matcher(x(1).trim).matches|reg2.pattern.matcher(x(1).trim).matches|reg3.pat
tern.matcher(x(1).trim).matches | reg4.pattern.matcher(x(1).trim).matches))
Step 6 : Now convert each Array to Strings
val valid =vatidRecords.map(e => (e(0),e(1),e(2)))
val bad =badRecords.map(e => (e(0),e(1),e(2)))
Step 7 : Save the output as a Text file and output must be written in a single tile,
valid.repartition(1).saveAsTextFile("spark9/good.txt")
bad.repartition(1).saveAsTextFile("sparkS7bad.txt")


Question # 3

Problem Scenario 72 : You have been given a table named "employee2" with following
detail.
first_name string
last_name string
Write a spark script in python which read this table and print all the rows and individual
column values.

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Import statements for HiveContext from pyspark.sql import HiveContext
Step 2 : Create sqIContext sqIContext = HiveContext(sc)
Step 3 : Query hive
employee2 = sqlContext.sql("select' from employee2")
Step 4 : Now prints the data for row in employee2.collect(): print(row)
Step 5 : Print specific column for row in employee2.collect(): print( row.fi rst_name)


Question # 4

Problem Scenario 15 : You have been given following mysql database details as well as
other info.
user=retail_dba
password=cloudera
database=retail_db
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following activities.
1. In mysql departments table please insert following record. Insert into departments
values(9999, '"Data Science"1);
2. Now there is a downstream system which will process dumps of this file. However,
system is designed the way that it can process only files if fields are enlcosed in(') single
quote and separate of the field should be (-} and line needs to be terminated by : (colon).
3. If data itself contains the " (double quote } than it should be escaped by \.
4. Please import the departments table in a directory called departments_enclosedby and
file should be able to process by downstream system.

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Connect to mysql database.
mysql -user=retail_dba -password=cloudera
show databases; use retail_db; show tables;
Insert record
Insert into departments values(9999, '"Data Science"');
select" from departments;
Step 2 : Import data as per requirement.
sqoop import \
-connect jdbc:mysql;//quickstart:3306/retail_db \
~username=retail_dba \
-password=cloudera \
-table departments \
-target-dir /user/cloudera/departments_enclosedby \
-enclosed-by V -escaped-by \\ -fields-terminated-by-' -lines-terminated-by :
Step 3 : Check the result.
hdfs dfs -cat/user/cloudera/departments_enclosedby/part"


Question # 5

Problem Scenario 9 : You have been given following mysql database details as well as
other info.
user=retail_dba
password=cloudera
database=retail_db
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following.
1. Import departments table in a directory.
2. Again import departments table same directory (However, directory already exist hence
it should not overrride and append the results)
3. Also make sure your results fields are terminated by '|' and lines terminated by '\n\

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solutions :
Step 1 : Clean the hdfs file system, if they exists clean out.
hadoop fs -rm -R departments
hadoop fs -rm -R categories
hadoop fs -rm -R products
hadoop fs -rm -R orders
hadoop fs -rm -R order_items
hadoop fs -rm -R customers
Step 2 : Now import the department table as per requirement.
sqoop import \
-connect jdbc:mysql://quickstart:330G/retaiI_db \
-username=retail_dba \
-password=cloudera \
-table departments \
-target-dir=departments \
-fields-terminated-by '|' \
-lines-terminated-by '\n' \
-ml
Step 3 : Check imported data.
hdfs dfs -Is departments
hdfs dfs -cat departments/part-m-00000
Step 4 : Now again import data and needs to appended.
sqoop import \
-connect jdbc:mysql://quickstart:3306/retail_db \
-username=retail_dba \
-password=cloudera \
-table departments \
-target-dir departments \
-append \
-tields-terminated-by '|' \
-lines-termtnated-by '\n' \
-ml
Step 5 : Again Check the results
hdfs dfs -Is departments
hdfs dfs -cat departments/part-m-00001


Question # 6

Problem Scenario 44 : You have been given 4 files , with the content as given below:
spark11/file1.txt
Apache Hadoop is an open-source software framework written in Java for distributed
storage and distributed processing of very large data sets on computer clusters built from
commodity hardware. All the modules in Hadoop are designed with a fundamental
assumption that hardware failures are common and should be automatically handled by the
framework
spark11/file2.txt
The core of Apache Hadoop consists of a storage part known as Hadoop Distributed File
System (HDFS) and a processing part called MapReduce. Hadoop splits files into large
blocks and distributes them across nodes in a cluster. To process data, Hadoop transfers
packaged code for nodes to process in parallel based on the data that needs to be
processed.
spark11/file3.txt
his approach takes advantage of data locality nodes manipulating the data they have
access to to allow the dataset to be processed faster and more efficiently than it would be
in a more conventional supercomputer architecture that relies on a parallel file system
where computation and data are distributed via high-speed networking
spark11/file4.txt
Apache Storm is focused on stream processing or what some call complex event
processing. Storm implements a fault tolerant method for performing a computation or
pipelining multiple computations on an event as it flows into a system. One might use
Storm to transform unstructured data as it flows into a system into a desired format
(spark11Afile1.txt)
(spark11/file2.txt)
(spark11/file3.txt)
(sparkl 1/file4.txt)
Write a Spark program, which will give you the highest occurring words in each file. With
their file name and highest occurring words.

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create all 4 file first using Hue in hdfs.
Step 2 : Load all file as an RDD
val file1 = sc.textFile("sparkl1/filel.txt")
val file2 = sc.textFile("spark11/file2.txt")
val file3 = sc.textFile("spark11/file3.txt")
val file4 = sc.textFile("spark11/file4.txt")
Step 3 : Now do the word count for each file and sort in reverse order of count.
val contentl = filel.flatMap( line => line.split(" ")).map(word => (word,1)).reduceByKey(_ +
_).map(item => item.swap).sortByKey(false).map(e=>e.swap)
val content.2 = file2.flatMap( line => line.splitf ")).map(word => (word,1)).reduceByKey(_
+ _).map(item => item.swap).sortByKey(false).map(e=>e.swap)
val content3 = file3.flatMap( line > line.split)" ")).map(word => (word,1)).reduceByKey(_
+ _).map(item => item.swap).sortByKey(false).map(e=>e.swap)
val content4 = file4.flatMap( line => line.split(" ")).map(word => (word,1)).reduceByKey(_ +
_).map(item => item.swap).sortByKey(false).map(e=>e.swap)
Step 4 : Split the data and create RDD of all Employee objects.
val filelword = sc.makeRDD(Array(file1.name+"->"+content1(0)._1+"-"+content1(0)._2)) val
file2word = sc.makeRDD(Array(file2.name+"->"+content2(0)._1+"-"+content2(0)._2)) val
file3word = sc.makeRDD(Array(file3.name+"->"+content3(0)._1+"-"+content3(0)._2)) val
file4word = sc.makeRDD(Array(file4.name+M->"+content4(0)._1+"-"+content4(0)._2))
Step 5: Union all the RDDS
val unionRDDs = filelword.union(file2word).union(file3word).union(file4word)
Step 6 : Save the results in a text file as below.
unionRDDs.repartition(1).saveAsTextFile("spark11/union.txt")


Question # 7

Problem Scenario 14 : You have been given following mysql database details as well as
other info.
user=retail_dba
password=cloudera
database=retail_db
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following activities.
1. Create a csv file named updated_departments.csv with the following contents in local file
system.
updated_departments.csv
2,fitness
3,footwear
12,fathematics
13,fcience
14,engineering
1000,management
2. Upload this csv file to hdfs filesystem,
3. Now export this data from hdfs to mysql retaildb.departments table. During upload make
sure existing department will just updated and new departments needs to be inserted.
4. Now update updated_departments.csv file with below content.
2,Fitness
3,Footwear
12,Fathematics
13,Science
14,Engineering
1000,Management
2000,Quality Check
5. Now upload this file to hdfs.
6. Now export this data from hdfs to mysql retail_db.departments table. During upload
make sure existing department will just updated and no new departments needs to be
inserted.

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create a csv tile named updateddepartments.csv with give content.
Step 2 : Now upload this tile to HDFS.
Create a directory called newdata.
hdfs dfs -mkdir new_data
hdfs dfs -put updated_departments.csv newdata/
Step 3 : Check whether tile is uploaded or not. hdfs dfs -Is new_data
Step 4 : Export this file to departments table using sqoop.
sqoop export -connect jdbc:mysql://quickstart:3306/retail_db \
-username retail_dba \
-password cloudera \
-table departments \
-export-dir new_data \
-batch \
-m 1 \
-update-key department_id \
-update-mode allowinsert
Step 5 : Check whether required data upsert is done or not. mysql -user=retail_dba -
password=cloudera
show databases;
use retail_db;
show tables;
select" from departments;
Step 6 : Update updated_departments.csv file.
Step 7 : Override the existing file in hdfs.
hdfs dfs -put updated_departments.csv newdata/
Step 8 : Now do the Sqoop export as per the requirement.
sqoop export -connect jdbc:mysql://quickstart:3306/retail_db \
-username retail_dba\-password cloudera \
-table departments \
-export-dir new_data \
-batch \
-m 1 \
-update-key-department_id \
-update-mode updateonly
Step 9 : Check whether required data update is done or not. mysql -user=retail_dba -
password=cloudera
show databases;
use retail db;
show tables;
select" from departments;


Question # 8

Problem Scenario 10 : You have been given following mysql database details as well as
other info.
user=retail_dba
password=cloudera
database=retail_db
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following.
1. Create a database named hadoopexam and then create a table named departments in
it, with following fields. department_id int,
department_name string
e.g. location should be
hdfs://quickstart.cloudera:8020/user/hive/warehouse/hadoopexam.db/departments
2. Please import data in existing table created above from retaidb.departments into hive
table hadoopexam.departments.
3. Please import data in a non-existing table, means while importing create hive table
named hadoopexam.departments_new

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Go to hive interface and create database.
hive
create database hadoopexam;
Step 2. Use the database created in above step and then create table in it. use
hadoopexam; show tables;
Step 3 : Create table in it.
create table departments (department_id int, department_name string);
show tables;
desc departments;
desc formatted departments;
Step 4 : Please check following directory must not exist else it will give error, hdfs dfs -Is
/user/cloudera/departments
If directory already exists, make sure it is not useful and than delete the same.
This is the staging directory where Sqoop store the intermediate data before pushing in
hive table.
hadoop fs -rm -R departments
Step 5 : Now import data in existing table
sqoop import \
-connect jdbc:mysql://quickstart:3306/retail_db \
~username=retail_dba \
-password=cloudera \
-table departments \
-hive-home /user/hive/warehouse \
-hive-import \
-hive-overwrite \
-hive-table hadoopexam.departments
Step 6 : Check whether data has been loaded or not.
hive;
use hadoopexam;
show tables;
select" from departments;
desc formatted departments;
Step 7 : Import data in non-existing tables in hive and create table while importing.
sqoop import \
-connect jdbc:mysql://quickstart:3306/retail_db \
-username=retail_dba \
~password=cloudera \
-table departments \
-hive-home /user/hive/warehouse \
-hive-import \
-hive-overwrite \
-hive-table hadoopexam.departments_new \
-create-hive-table
Step 8 : Check-whether data has been loaded or not.
hive;
use hadoopexam;
show tables;
select" from departments_new;
desc formatted departments_new;


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Exam Name: CCA Spark and Hadoop Developer Exam
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