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SPARK-1456 Remove view bounds on Ordered in favor of a context bound on Ordering. #410

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SPARK-1456 Remove view bounds on Ordered in favor of a context bound on Ordering. #410

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35798a8
Remove view bounds on Ordered in favor of a context bound on Ordering.
marmbrus Apr 15, 2014
1b9b85c
Update scala doc.
marmbrus Apr 16, 2014
812008e
Update Java API.
marmbrus Apr 18, 2014
c574221
fix example.
marmbrus Apr 18, 2014
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8 changes: 5 additions & 3 deletions core/src/main/scala/org/apache/spark/Partitioner.scala
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Expand Up @@ -89,12 +89,14 @@ class HashPartitioner(partitions: Int) extends Partitioner {
* A [[org.apache.spark.Partitioner]] that partitions sortable records by range into roughly
* equal ranges. The ranges are determined by sampling the content of the RDD passed in.
*/
class RangePartitioner[K <% Ordered[K]: ClassTag, V](
class RangePartitioner[K : Ordering : ClassTag, V](
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@rxin rxin Apr 15, 2014

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Can the user still pass in an Ordering if they want a specific Ordering?

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@markhamstra markhamstra Apr 15, 2014

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Yes, when de-sugared there is an implicit Ordering -- that's why Michael can recover and bind it explicitly at line 98. And yes, it can be overridden in the normal way.

partitions: Int,
@transient rdd: RDD[_ <: Product2[K,V]],
private val ascending: Boolean = true)
extends Partitioner {

private val ordering = implicitly[Ordering[K]]

// An array of upper bounds for the first (partitions - 1) partitions
private val rangeBounds: Array[K] = {
if (partitions == 1) {
Expand All @@ -103,7 +105,7 @@ class RangePartitioner[K <% Ordered[K]: ClassTag, V](
val rddSize = rdd.count()
val maxSampleSize = partitions * 20.0
val frac = math.min(maxSampleSize / math.max(rddSize, 1), 1.0)
val rddSample = rdd.sample(false, frac, 1).map(_._1).collect().sortWith(_ < _)
val rddSample = rdd.sample(false, frac, 1).map(_._1).collect().sorted
if (rddSample.length == 0) {
Array()
} else {
Expand All @@ -126,7 +128,7 @@ class RangePartitioner[K <% Ordered[K]: ClassTag, V](
var partition = 0
if (rangeBounds.length < 1000) {
// If we have less than 100 partitions naive search
while (partition < rangeBounds.length && k > rangeBounds(partition)) {
while (partition < rangeBounds.length && ordering.gt(k, rangeBounds(partition))) {
partition += 1
}
} else {
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2 changes: 1 addition & 1 deletion core/src/main/scala/org/apache/spark/SparkContext.scala
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Expand Up @@ -1267,7 +1267,7 @@ object SparkContext extends Logging {
rdd: RDD[(K, V)]) =
new SequenceFileRDDFunctions(rdd)

implicit def rddToOrderedRDDFunctions[K <% Ordered[K]: ClassTag, V: ClassTag](
implicit def rddToOrderedRDDFunctions[K : Ordering : ClassTag, V: ClassTag](
rdd: RDD[(K, V)]) =
new OrderedRDDFunctions[K, V, (K, V)](rdd)

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8 changes: 5 additions & 3 deletions core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
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Expand Up @@ -27,12 +27,14 @@ import org.apache.spark.{Logging, RangePartitioner}
* use these functions. They will work with any key type that has a `scala.math.Ordered`
* implementation.
*/
class OrderedRDDFunctions[K <% Ordered[K]: ClassTag,
class OrderedRDDFunctions[K : Ordering : ClassTag,
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@pwendell pwendell Apr 16, 2014

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@marmbrus Do you mind updating the scaladoc above since now users can pass their own orderings in addition to an Ordered type? It might be nice to just give a one-line example of how they can define a custom ordering as well.

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@marmbrus marmbrus Apr 16, 2014

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done.

V: ClassTag,
P <: Product2[K, V] : ClassTag](
self: RDD[P])
extends Logging with Serializable {

private val ordering = implicitly[Ordering[K]]

/**
* Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
* `collect` or `save` on the resulting RDD will return or output an ordered list of records
Expand All @@ -45,9 +47,9 @@ class OrderedRDDFunctions[K <% Ordered[K]: ClassTag,
shuffled.mapPartitions(iter => {
val buf = iter.toArray
if (ascending) {
buf.sortWith((x, y) => x._1 < y._1).iterator
buf.sortWith((x, y) => ordering.lt(x._1, y._1)).iterator
} else {
buf.sortWith((x, y) => x._1 > y._1).iterator
buf.sortWith((x, y) => ordering.gt(x._1, y._1)).iterator
}
}, preservesPartitioning = true)
}
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2 changes: 1 addition & 1 deletion core/src/main/scala/org/apache/spark/util/CollectionsUtil.scala
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Expand Up @@ -23,7 +23,7 @@ import scala.Array
import scala.reflect._

private[spark] object CollectionsUtils {
def makeBinarySearch[K <% Ordered[K] : ClassTag] : (Array[K], K) => Int = {
def makeBinarySearch[K : Ordering : ClassTag] : (Array[K], K) => Int = {
classTag[K] match {
case ClassTag.Float =>
(l, x) => util.Arrays.binarySearch(l.asInstanceOf[Array[Float]], x.asInstanceOf[Float])
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