Question # 1
Problem Scenario 89 : You have been given below patient data in csv format,
patientID,name,dateOfBirth,lastVisitDate
1001,Ah Teck,1991-12-31,2012-01-20
1002,Kumar,2011-10-29,2012-09-20
1003,Ali,2011-01-30,2012-10-21
Accomplish following activities.
1. Find all the patients whose lastVisitDate between current time and '2012-09-15'
2. Find all the patients who born in 2011
3. Find all the patients age
4. List patients whose last visited more than 60 days ago
5. Select patients 18 years old or younger
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Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1:
hdfs dfs -mkdir sparksql3
hdfs dfs -put patients.csv sparksql3/
Step 2 : Now in spark shell
// SQLContext entry point for working with structured data
val sqlContext = neworg.apache.spark.sql.SQLContext(sc)
// this is used to implicitly convert an RDD to a DataFrame.
import sqlContext.impIicits._
// Import Spark SQL data types and Row.
import org.apache.spark.sql._
// load the data into a new RDD
val patients = sc.textFilef'sparksqIS/patients.csv")
// Return the first element in this RDD
patients.first()
//define the schema using a case class
case class Patient(patientid: Integer, name: String, dateOfBirth:String , lastVisitDate:
String)
// create an RDD of Product objects
val patRDD = patients.map(_.split(M,M)).map(p => Patient(p(0).tolnt,p(1),p(2),p(3)))
patRDD.first()
patRDD.count(}
// change RDD of Product objects to a DataFrame val patDF = patRDD.toDF()
// register the DataFrame as a temp table patDF.registerTempTable("patients"}
// Select data from table
val results = sqlContext.sql(......SELECT* FROM patients '.....)
// display dataframe in a tabular format
results.show()
//Find all the patients whose lastVisitDate between current time and '2012-09-15'
val results = sqlContext.sql(......SELECT * FROM patients WHERE
TO_DATE(CAST(UNIX_TIMESTAMP(lastVisitDate, 'yyyy-MM-dd') AS TIMESTAMP))
BETWEEN '2012-09-15' AND current_timestamp() ORDER BY lastVisitDate......)
results.showQ
/.Find all the patients who born in 2011
val results = sqlContext.sql(......SELECT * FROM patients WHERE
YEAR(TO_DATE(CAST(UNIXJTlMESTAMP(dateOfBirth, 'yyyy-MM-dd') AS
TIMESTAMP))) = 2011 ......)
results. show()
//Find all the patients age
val results = sqlContext.sql(......SELECT name, dateOfBirth, datediff(current_date(),
TO_DATE(CAST(UNIX_TIMESTAMP(dateOfBirth, 'yyyy-MM-dd') AS TlMESTAMP}}}/365
AS age
FROM patients
Mini >
results.show()
//List patients whose last visited more than 60 days ago
- List patients whose last visited more than 60 days ago
val results = sqlContext.sql(......SELECT name, lastVisitDate FROM patients WHERE
datediff(current_date(), TO_DATE(CAST(UNIX_TIMESTAMP[lastVisitDate, 'yyyy-MM-dd')
AS T1MESTAMP))) > 60......);
results. showQ;
- Select patients 18 years old or younger
SELECT' FROM patients WHERE TO_DATE(CAST(UNIXJTlMESTAMP(dateOfBirth,
'yyyy-MM-dd') AS TIMESTAMP}) > DATE_SUB(current_date(),INTERVAL 18 YEAR);
val results = sqlContext.sql(......SELECT' FROM patients WHERE
TO_DATE(CAST(UNIX_TIMESTAMP(dateOfBirth, 'yyyy-MM-dd') AS TIMESTAMP)) >
DATE_SUB(current_date(), T8*365)......);
results. showQ;
val results = sqlContext.sql(......SELECT DATE_SUB(current_date(), 18*365) FROM
patients......);
results.show();
Question # 2
Problem Scenario 11 : 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 called departments.
2. Once import is done, please insert following 5 records in departments mysql table.
Insert into departments(10, physics);
Insert into departments(11, Chemistry);
Insert into departments(12, Maths);
Insert into departments(13, Science);
Insert into departments(14, Engineering);
3. Now import only new inserted records and append to existring directory . which has been
created in first step.
|
Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Clean already imported data. (In real exam, please make sure you dont delete
data generated from previous exercise).
hadoop fs -rm -R departments
Step 2 : Import data in departments directory.
sqoop import \
-connect jdbc:mysql://quickstart:3306/retail_db \
-username=retail_dba \
-password=cloudera \
-table departments \
"target-dir/user/cloudera/departments
Step 3 : Insert the five records in departments table.
mysql -user=retail_dba -password=cloudera retail_db
Insert into departments values(10, "physics"); Insert into departments values(11,
"Chemistry"); Insert into departments values(12, "Maths"); Insert into departments
values(13, "Science"); Insert into departments values(14, "Engineering"); commit;
select' from departments;
Step 4 : Get the maximum value of departments from last import, hdfs dfs -cat
/user/cloudera/departments/part* that should be 7
Step 5 : Do the incremental import based on last import and append the results.
sqoop import \
-connect "jdbc:mysql://quickstart.cloudera:330G/retail_db" \
~username=retail_dba \
-password=cloudera \
-table departments \
-target-dir /user/cloudera/departments \
-append \
-check-column "department_id" \
-incremental append \
-last-value 7
Step 6 : Now check the result.
hdfs dfs -cat /user/cloudera/departments/part"
Question # 3
Problem Scenario 1:
You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.categories
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following activities.
1. Connect MySQL DB and check the content of the tables.
2. Copy "retaildb.categories" table to hdfs, without specifying directory name.
3. Copy "retaildb.categories" table to hdfs, in a directory name "categories_target".
4. Copy "retaildb.categories" table to hdfs, in a warehouse directory name
"categories_warehouse".
|
Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Connecting to existing MySQL Database mysql -user=retail_dba -
password=cloudera retail_db
Step 2 : Show all the available tables show tables;
Step 3 : View/Count data from a table in MySQL select count(1} from categories;
Step 4 : Check the currently available data in HDFS directory hdfs dfs -Is
Step 5 : Import Single table (Without specifying directory).
sqoop import -connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -
password=cloudera -table=categories
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 6 : Read the data from one of the partition, created using above command, hdfs dfs -
catxategories/part-m-00000
Step 7 : Specifying target directory in import command (We are using number of mappers
=1, you can change accordingly) sqoop import -connect
jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera
~table=categories -target-dir=categortes_target -m 1
Step 8 : Check the content in one of the partition file.
hdfs dfs -cat categories_target/part-m-00000
Step 9 : Specifying parent directory so that you can copy more than one table in a specified
target directory. Command to specify warehouse directory.
sqoop import -.-connect jdbc:mysql://quickstart:3306/retail_db -username=retail dba -
password=cloudera -table=categories -warehouse-dir=categories_warehouse -m 1
Question # 4
Problem Scenario 62 : You have been given below code snippet.val a = sc.parallelize(List("dogM, "tiger", "lion", "cat", "panther", "eagle"), 2)
val b = a.map(x => (x.length, x))
operation1
Write a correct code snippet for operationl which will produce desired output, shown below.
Array[(lnt, String)] = Array((3,xdogx), (5,xtigerx), (4,xlionx), (3,xcatx), (7,xpantherx),
(5,xeaglex))
|
Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
b.mapValuesf'x" + _ + "x").collect
mapValues [Pair] : Takes the values of a RDD that consists of two-component tuples, and
applies the provided function to transform each value. Tlien,.it.forms newtwo-componend
tuples using the key and the transformed value and stores them in a new RDD.
Question # 5
Problem Scenario 39 : You have been given two files
spark16/file1.txt
1,9,5
2,7,4
3,8,3
spark16/file2.txt
1,g,h
2,i,j
3,k,l
Load these two tiles as Spark RDD and join them to produce the below results
(l,((9,5),(g,h)))
(2, ((7,4), (i,j))) (3, ((8,3), (k,l)))
And write code snippet which will sum the second columns of above joined results (5+4+3). |
Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create tiles in hdfs using Hue.
Step 2 : Create pairRDD for both the files.
val one = sc.textFile("spark16/file1.txt").map{
_.split(",",-1) match {
case Array(a, b, c) => (a, ( b, c))
} }
val two = sc.textFHe(Mspark16/file2.txt").map{
_.split('7\-1) match {
case Array(a, b, c) => (a, (b, c))
} }
Step 3 : Join both the RDD. val joined = one.join(two)
Step 4 : Sum second column values.
val sum = joined.map {
case (_, ((_, num2), (_, _))) => num2.tolnt
}.reduce(_ + _)
Question # 6
Problem Scenario 16 : 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 below assignment.
1. Create a table in hive as below.
create table departments_hive(department_id int, department_name string);
2. Now import data from mysql table departments to this hive table. Please make sure that
data should be visible using below hive command, select" from departments_hive
|
Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create hive table as said.
hive
show tables;
create table departments_hive(department_id int, department_name string);
Step 2 : The important here is, when we create a table without delimiter fields. Then default
delimiter for hive is ^A (\001). Hence, while importing data we have to provide proper
delimiter.
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 departments_hive \
-fields-terminated-by '\001'
Step 3 : Check-the data in directory.
hdfs dfs -Is /user/hive/warehouse/departments_hive
hdfs dfs -cat/user/hive/warehouse/departmentshive/part'
Check data in hive table.
Select * from departments_hive;
Question # 7
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"
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