CCA175 Exam Dumps : CCA Spark and Hadoop Developer Exam

Solution :

Step 1 : Import Single table .

sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera -table=products --target-dir=p93

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 2 : Step 2 : Read the data from one of the partition, created using above command, hadoop fs -cat p93_products/part-m-00000

Step 3 : Load this directory as RDD using Spark and Python (Open pyspark terminal and do following}. productsRDD = sc.textFile(Mp93_products")

Step 4 : Filter empty prices, if exists

#filter out empty prices lines

Nonempty_lines = productsRDD.filter(lambda x: len(x.split(",")[4]) > 0)

Step 5 : Create data set like (categroyld, (id,name,price)

mappedRDD = nonempty_lines.map(lambda line: (line.split(",")[1], (line.split(",")[0], line.split(",")[2], float(line.split(",")[4]))))

tor line in mappedRDD.collect(): print(line)

Step 6 : Now groupBy the all records based on categoryld, which a key on mappedRDD it will produce output like (categoryld, iterable of all lines for a key/categoryld)

groupByCategroyld = mappedRDD.groupByKey() for line in groupByCategroyld.collect(): print(line)

step 7 : Now sort the data in each category based on price in ascending order.

# sorted is a function to sort an iterable, we can also specify, what would be the Key on which we want to sort in this case we have price on which it needs to be sorted.

groupByCategroyld.map(lambda tuple: sorted(tuple[1], key=lambda tupleValue: tupleValue[2])).take(5)

Step 8 : Now sort the data in each category based on price in descending order.

# sorted is a function to sort an iterable, we can also specify, what would be the Key on which we want to sort in this case we have price which it needs to be sorted.

on groupByCategroyld.map(lambda tuple: sorted(tuple[1], key=lambda tupleValue: tupleValue[2] , reverse=True)).take(5)

Solution :

Step 1 : Select Maximum, minimum, average , Standard Deviation, and total quantity.

val results = sqlContext.sql('.....SELECT MAX(price) AS MAX , MIN(price) AS MIN , AVG(price) AS Average, STD(price) AS STD, SUM(quantity) AS total_products FROM products......)

results. showQ

Step 2 : Select minimum and maximum price for each product code.

val results = sqlContext.sql(......SELECT code, MAX(price) AS Highest Price', MIN(price) AS Lowest Price'

FROM products GROUP BY code......)

results. showQ

Step 3 : Select Maximum, minimum, average , Standard Deviation, and total quantity for each product code, hwoever make sure Average and Standard deviation will have maximum two decimal values.

val results = sqlContext.sql(......SELECT code, MAX(price), MIN(price),

CAST(AVG(price} AS DECIMAL(7,2)) AS Average', CAST(STD(price) AS DECIMAL(7,2)) AS 'Std Dev\ SUM(quantity) FROM products

GROUP BY code......)

results. showQ

Step 4 : Select all the product code and average price only where product count is more than or equal to 3.

val results = sqlContext.sql(......SELECT code AS Product Code',

COUNTf) AS Count',

CAST(AVG(price) AS DECIMAL(7,2)) AS Average' FROM products GROUP BY code

HAVING Count >=3"M") results. showQ

Step 5 : Select maximum, minimum , average and total of all the products for each code. Also produce the same across all the products.

val results = sqlContext.sql( """SELECT

code,

MAX(price),

MIN(pnce),

CAST(AVG(price) AS DECIMAL(7,2)) AS Average',

SUM(quantity)-

FROM products

GROUP BY code

WITH ROLLUP""" )

results. show()

Solution :

val z = x.intersection(y)

intersection : Returns the elements in the two RDDs which are the same.