Cloudera CCA175 Dumps

Solution :

b.groupByKey.collect

groupByKey [Pair]

Very similar to groupBy, but instead of supplying a function, the key-component of each pair will automatically be presented to the partitioner.

Listing Variants

def groupByKeyQ: RDD[(K, lterable[V]}]

def groupByKey(numPartittons: Int): RDD[(K, lterable[V] )]

def groupByKey(partitioner: Partitioner): RDD[(K, lterable[V])]

Solution :

Step 1 : Create this tile in HDFS under following directory (Without header} /user/cloudera/he/exam/task1/productcsv

Step 2 : Now using Spark-shell read the file as RDD

// load the data into a new RDD

val products = sc.textFile("/user/cloudera/he/exam/task1/product.csv")

// Return the first element in this RDD

prod u cts.fi rst()

Step 3 : Now define the schema using a case class

case class Product(productid: Integer, code: String, name: String, quantity:lnteger, price: Float)

Step 4 : create an RDD of Product objects

val prdRDD = products.map(_.split(",")).map(p => Product(p(0).tolnt,p(1),p(2),p(3}.tolnt,p(4}.toFloat))

prdRDD.first()

prdRDD.count()

Step 5 : Now create data frame val prdDF = prdRDD.toDF()

Step 6 : Now store data in hive warehouse directory. (However, table will not be created } import org.apache.spark.sql.SaveMode prdDF.write.mode(SaveMode.Overwrite).format("orc").saveAsTable("product_orc_table")

step 7: Now create table using data stored in warehouse directory. With the help of hive.

hive

show tables

CREATE EXTERNAL TABLE products (productid int,code string,name string .quantity int, price float}

STORED AS ore

LOCATION 7user/hive/warehouse/product_orc_table';

Step 8 : Now create a parquet table

import org.apache.spark.sql.SaveMode

prdDF.write.mode(SaveMode.Overwrite).format("parquet").saveAsTable("product_parquet_table")

Step 9 : Now create table using this

CREATE EXTERNAL TABLE products_parquet (productid int,code string,name string .quantity int, price float}

STORED AS parquet

LOCATION 7user/hive/warehouse/product_parquet_table';

Step 10 : Check data has been loaded or not.

Select * from products;

Select * from products_parquet;

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"