diff options
3 files changed, 53 insertions, 2 deletions
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala index c17ca12379..e4ccd9f11b 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala @@ -445,6 +445,16 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { /** * Approximate version of count() that returns a potentially incomplete result * within a timeout, even if not all tasks have finished. + * + * The confidence is the probability that the error bounds of the result will + * contain the true value. That is, if countApprox were called repeatedly + * with confidence 0.9, we would expect 90% of the results to contain the + * true count. The confidence must be in the range [0,1] or an exception will + * be thrown. + * + * @param timeout maximum time to wait for the job, in milliseconds + * @param confidence the desired statistical confidence in the result + * @return a potentially incomplete result, with error bounds */ def countApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble] = rdd.countApprox(timeout, confidence) @@ -452,6 +462,8 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { /** * Approximate version of count() that returns a potentially incomplete result * within a timeout, even if not all tasks have finished. + * + * @param timeout maximum time to wait for the job, in milliseconds */ def countApprox(timeout: Long): PartialResult[BoundedDouble] = rdd.countApprox(timeout) @@ -464,7 +476,17 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { mapAsSerializableJavaMap(rdd.countByValue()).asInstanceOf[JMap[T, jl.Long]] /** - * (Experimental) Approximate version of countByValue(). + * Approximate version of countByValue(). + * + * The confidence is the probability that the error bounds of the result will + * contain the true value. That is, if countApprox were called repeatedly + * with confidence 0.9, we would expect 90% of the results to contain the + * true count. The confidence must be in the range [0,1] or an exception will + * be thrown. + * + * @param timeout maximum time to wait for the job, in milliseconds + * @param confidence the desired statistical confidence in the result + * @return a potentially incomplete result, with error bounds */ def countByValueApprox( timeout: Long, @@ -473,7 +495,10 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { rdd.countByValueApprox(timeout, confidence).map(mapAsSerializableJavaMap) /** - * (Experimental) Approximate version of countByValue(). + * Approximate version of countByValue(). + * + * @param timeout maximum time to wait for the job, in milliseconds + * @return a potentially incomplete result, with error bounds */ def countByValueApprox(timeout: Long): PartialResult[JMap[T, BoundedDouble]] = rdd.countByValueApprox(timeout).map(mapAsSerializableJavaMap) diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index 7936d8e1d4..3b12448d63 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -375,6 +375,16 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) /** * Approximate version of countByKey that can return a partial result if it does * not finish within a timeout. + * + * The confidence is the probability that the error bounds of the result will + * contain the true value. That is, if countApprox were called repeatedly + * with confidence 0.9, we would expect 90% of the results to contain the + * true count. The confidence must be in the range [0,1] or an exception will + * be thrown. + * + * @param timeout maximum time to wait for the job, in milliseconds + * @param confidence the desired statistical confidence in the result + * @return a potentially incomplete result, with error bounds */ def countByKeyApprox(timeout: Long, confidence: Double = 0.95) : PartialResult[Map[K, BoundedDouble]] = self.withScope { diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index d85d0fff46..e6db9b3eec 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -1107,10 +1107,21 @@ abstract class RDD[T: ClassTag]( /** * Approximate version of count() that returns a potentially incomplete result * within a timeout, even if not all tasks have finished. + * + * The confidence is the probability that the error bounds of the result will + * contain the true value. That is, if countApprox were called repeatedly + * with confidence 0.9, we would expect 90% of the results to contain the + * true count. The confidence must be in the range [0,1] or an exception will + * be thrown. + * + * @param timeout maximum time to wait for the job, in milliseconds + * @param confidence the desired statistical confidence in the result + * @return a potentially incomplete result, with error bounds */ def countApprox( timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = withScope { + require(0.0 <= confidence && confidence <= 1.0, s"confidence ($confidence) must be in [0,1]") val countElements: (TaskContext, Iterator[T]) => Long = { (ctx, iter) => var result = 0L while (iter.hasNext) { @@ -1137,10 +1148,15 @@ abstract class RDD[T: ClassTag]( /** * Approximate version of countByValue(). + * + * @param timeout maximum time to wait for the job, in milliseconds + * @param confidence the desired statistical confidence in the result + * @return a potentially incomplete result, with error bounds */ def countByValueApprox(timeout: Long, confidence: Double = 0.95) (implicit ord: Ordering[T] = null) : PartialResult[Map[T, BoundedDouble]] = withScope { + require(0.0 <= confidence && confidence <= 1.0, s"confidence ($confidence) must be in [0,1]") if (elementClassTag.runtimeClass.isArray) { throw new SparkException("countByValueApprox() does not support arrays") } |