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SPARK-6878 [CORE] Fix for sum on empty RDD fails with exception #5489

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SPARK-6878 [CORE] Fix for sum on empty RDD fails with exception #5489

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f1708c9
Fix for sum on empty RDD fails with exception (SPARK-6878)
Apr 13, 2015
1c91954
Rewrote double range matcher to an exact equality assert (SPARK-6878)
Apr 13, 2015
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2 changes: 1 addition & 1 deletion core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala
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Expand Up @@ -31,7 +31,7 @@ import org.apache.spark.util.StatCounter
class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
/** Add up the elements in this RDD. */
def sum(): Double = {
self.reduce(_ + _)
self.fold(0.0)(_ + _)
}

/**
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7 changes: 7 additions & 0 deletions core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala
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Expand Up @@ -18,10 +18,17 @@
package org.apache.spark.rdd

import org.scalatest.FunSuite
import org.scalatest.Matchers._

import org.apache.spark._

class DoubleRDDSuite extends FunSuite with SharedSparkContext {
test("sum") {
sc.parallelize(Seq.empty[Double]).sum() should be(0.0 +- 0.0001)

@srowen srowen Apr 13, 2015

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I would just use assert here as int he rest of the file. Also I think you can assert exact equality in these cases. It really should be 3.0 in the last instance, precisely.

@erikvanoosten erikvanoosten Apr 13, 2015

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Experience thought me that exact equality with doubles is unreliable. Now I err on the safe side and always use inexact matchers in unit tests.

@srowen srowen Apr 13, 2015

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Doubles aren't unstable; 0.0 == 0.0 always. Yes i know what you mean but in these cases we can expect the result to exact to machine precision, always.

@erikvanoosten erikvanoosten Apr 13, 2015

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I didn't say unstable :)
Anyway, changed as requested.

sc.parallelize(Seq(1.0)).sum() should be(1.0 +- 0.0001)
sc.parallelize(Seq(1.0, 2.0)).sum() should be(3.0 +- 0.0001)
}

// Verify tests on the histogram functionality. We test with both evenly
// and non-evenly spaced buckets as the bucket lookup function changes.
test("WorksOnEmpty") {
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