Improper cache strategy in examples

    val edges = tc.map(x => (x._2, x._1)) // Edges should be cached
    // This join is iterated until a fixed point is reached.
    var oldCount = 0L
    var nextCount = tc.count()
    do { 
      oldCount = nextCount
      // Perform the join, obtaining an RDD of (y, (z, x)) pairs,
      // then project the result to obtain the new (x, z) paths.
      tc = tc.union(tc.join(edges).map(x => (x._2._2, x._2._1))).distinct().cache()
      nextCount = tc.count()
    } while (nextCount != oldCount)
    println(s"TC has ${tc.count()} edges.")

    // Set the model threshold to maximize F-Measure
    val fMeasure = trainingSummary.fMeasureByThreshold // fMeasures should be cached
    val maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0)
    val bestThreshold = fMeasure.where($"F-Measure" === maxFMeasure)
      .select("threshold").head().getDouble(0)
    lrModel.setThreshold(bestThreshold)

    val peopleDF = spark.sparkContext
      .textFile("examples/src/main/resources/people.txt")
      .map(_.split(","))
      .map(attributes => Person(attributes(0), attributes(1).trim.toInt)) // This RDD should be cahced
      .toDF()
    // Register the DataFrame as a temporary view
    peopleDF.createOrReplaceTempView("people")
    val teenagersDF = spark.sql("SELECT name, age FROM people WHERE age BETWEEN 13 AND 19")
    teenagersDF.map(teenager => "Name: " + teenager(0)).show()
    teenagersDF.map(teenager => "Name: " + teenager.getAs[String]("name")).show()
    implicit val mapEncoder = org.apache.spark.sql.Encoders.kryo[Map[String, Any]]
    teenagersDF.map(teenager => teenager.getValuesMap[Any](List("name", "age"))).collect()