From fc9a90a29d7f71228b6ef8b4470932c98847b77f Mon Sep 17 00:00:00 2001
From: Olivier Blanvillain <olivier.blanvillain@epfl.ch>
Date: Mon, 17 May 2021 16:00:02 +0200
Subject: [PATCH] Add exercises/solutions-6.md
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+# Exercise 1 : Spark Fundamentals
+
+## Question 1
+
+```scala
+rdd.map(text => text.split(" ").length).reduce(_ + _)
+```
+
+## Question 2
+
+```scala
+val errorLogs = rawLogs.map(toLog(_)).filter(isError(_)).cache()
+val numberErrors = errorLogs.count()
+val messages = errorLogs.filter(isRecent).map(message(_)).collect()
+```
+
+## Question 3
+
+In the first case, all the strings are displayed on the master. In the second case, records are displayed on the standard output of the worker nodes.
+
+## Question 4
+
+In the case of 2., the map operations are pipelined and only applied to 10 elements.
+
+# Exercise 2 : Demographics
+
+
+## Question 1
+
+```scala
+val adultAges = people.map(_.age).filter(_ >= 18).cache()
+groups.map {
+ case (lower, upper) => adultAges.filter { (age: Int) =>
+ lower <= age && age <= upper
+ }.count()
+}
+```
+
+## Question 2
+
+```scala
+val groups: Array[(Int, Int)] =
+ people.map(_.age)
+ .filter(_ >= 18)
+ .map((age: Int) => (groupOf(age), 1))
+ .reduceByKey(_ + _)
+ .collect()
+
+// Now, we have a small collection
+// and can work on a single machine.
+groups.sortBy(_._1)
+ .map(_._2)
+ .toList()
+```
--
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