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 : 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;

Solution : pairRDD1.fullOuterJoin(pairRDD2).collect

fullOuterJoin [Pair]

Performs the full outer join between two paired RDDs.

Listing Variants

def fullOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Option[V], OptionfW]))]

def fullOuterJoin[W](other: RDD[(K, W}]}: RDD[(K, (Option[V], OptionfW]))]

def fullOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (Option[V], Option[W]))]