CCA175 Exam Dumps : CCA Spark and Hadoop Developer Exam

Solution :

Step 1 : Login to musql db

mysql --user=retail_dba -password=cloudera

show databases;

use retail db; show tables;

Step 2 : Create a table as given in problem statement.

CREATE table departments_new (department_id int(11), department_name varchar(45), createddate T1MESTAMP DEFAULT NOW());

show tables;

Step 3 : isert records from departments table to departments_new insert into departments_new select a.", null from departments a;

Step 4 : Import data from departments new table to hdfs.

sqoop import \

-connect jdbc:mysql://quickstart:330G/retail_db \

~username=retail_dba \

-password=cloudera \

-table departments_new\

--target-dir /user/cloudera/departments_new \

--split-by departments

Stpe 5 : Check the imported data.

hdfs dfs -cat /user/cloudera/departmentsnew/part"

Step 6 : Insert following 5 records in departmentsnew table.

Insert into departments_new values(110, "Civil" , null);

Insert into departments_new values(111, "Mechanical" , null);

Insert into departments_new values(112, "Automobile" , null);

Insert into departments_new values(113, "Pharma" , null);

Insert into departments_new values(114, "Social Engineering" , null);

commit;

Stpe 7 : Import incremetal data based on created_date column.

sqoop import \

-connect jdbc:mysql://quickstart:330G/retaiI_db \

-username=retail_dba \

-password=cloudera \

--table departments_new\

-target-dir /user/cloudera/departments_new \

-append \

-check-column created_date \

-incremental lastmodified \

-split-by departments \

-last-value "2016-01-30 12:07:37.0"

Step 8 : Check the imported value.

hdfs dfs -cat /user/cloudera/departmentsnew/part"

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 :

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}]