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CCA Spark and Hadoop Developer Exam Questions and Answers
Problem Scenario 50 : You have been given below code snippet (calculating an average score}, with intermediate output.
type ScoreCollector = (Int, Double)
type PersonScores = (String, (Int, Double))
val initialScores = Array(( " Fred " , 88.0), ( " Fred " , 95.0), ( " Fred " , 91.0), ( " Wilma " , 93.0), ( " Wilma " , 95.0), ( " Wilma " , 98.0))
val wilmaAndFredScores = sc.parallelize(initialScores).cache()
val scores = wilmaAndFredScores.combineByKey(createScoreCombiner, scoreCombiner, scoreMerger)
val averagingFunction = (personScore: PersonScores) = > { val (name, (numberScores, totalScore)) = personScore (name, totalScore / numberScores)
}
val averageScores = scores.collectAsMap(}.map(averagingFunction)
Expected output: averageScores: scala.collection.Map[String,Double] = Map(Fred - > 91.33333333333333, Wilma - > 95.33333333333333)
Define all three required function , which are input for combineByKey method, e.g. (createScoreCombiner, scoreCombiner, scoreMerger). And help us producing required results.
Problem Scenario 75 : You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.orders
table=retail_db.order_items
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following activities.
1. Copy " retail_db.order_items " table to hdfs in respective directory p90_order_items .
2. Do the summation of entire revenue in this table using pyspark.
3. Find the maximum and minimum revenue as well.
4. Calculate average revenue
Columns of ordeMtems table : (order_item_id , order_item_order_id , order_item_product_id, order_item_quantity,order_item_subtotal,order _ item_subtotal,order_item_product_price)
Problem Scenario 49 : You have been given below code snippet (do a sum of values by key}, with intermediate output.
val keysWithValuesList = Array( " foo=A " , " foo=A " , " foo=A " , " foo=A " , " foo=B " , " bar=C " , " bar=D " , " bar=D " )
val data = sc.parallelize(keysWithValuesl_ist}
//Create key value pairs
val kv = data.map(_.split( " = " )).map(v = > (v(0), v(l))).cache()
val initialCount = 0;
val countByKey = kv.aggregateByKey(initialCount)(addToCounts, sumPartitionCounts)
Now define two functions (addToCounts, sumPartitionCounts) such, which will produce following results.
Output 1
countByKey.collect
res3: Array[(String, Int)] = Array((foo,5), (bar,3))
import scala.collection._
val initialSet = scala.collection.mutable.HashSet.empty[String]
val uniqueByKey = kv.aggregateByKey(initialSet)(addToSet, mergePartitionSets)
Now define two functions (addToSet, mergePartitionSets) such, which will produce following results.
Output 2:
uniqueByKey.collect
res4: Array[(String, scala.collection.mutable.HashSet[String])] = Array((foo,Set(B, A}}, (bar,Set(C, D}}}
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