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SPARK-1941: Update streamlib to 2.7.0 and use HyperLogLogPlus instead of HyperLogLog. #897

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rxin wants to merge 15 commits into from
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SPARK-1941: Update streamlib to 2.7.0 and use HyperLogLogPlus instead of HyperLogLog. #897

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SPARK-1941: Update streamlib to 2.7.0 and use HyperLogLogPlus instead…
rxin May 28, 2014
e7786cb
Merge branch 'master' into hll
rxin May 31, 2014
1294be6
Updated HLL+.
rxin May 31, 2014
88cfe77
Updated documentation and restored the old incorrect API to maintain …
rxin Jun 2, 2014
9221b27
Merge branch 'master' into hll
rxin Jun 2, 2014
1db1522
Excluded util.SerializableHyperLogLog from MIMA check.
rxin Jun 2, 2014
6555bfe
Added a default method and re-arranged MimaExcludes.
rxin Jun 2, 2014
acaa524
Added the right exclude rules in MimaExcludes.
rxin Jun 2, 2014
354deb8
Added comment on the Mima exclude rules.
rxin Jun 2, 2014
e110d70
Merge branch 'master' into hll
rxin Jun 3, 2014
9e320c8
Incorporate code review feedback.
rxin Jun 3, 2014
41e649a
Update final mima list.
rxin Jun 3, 2014
e367527
One more round of code review.
rxin Jun 4, 2014
f154ea0
Added a comment on the value bound for testing.
rxin Jun 4, 2014
4d83f41
New error bound and non-randomness.
rxin Jun 4, 2014
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28 changes: 21 additions & 7 deletions core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
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Expand Up @@ -28,7 +28,7 @@ import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer
import scala.reflect.ClassTag

import com.clearspring.analytics.stream.cardinality.HyperLogLog
import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus
import org.apache.hadoop.conf.{Configurable, Configuration}
import org.apache.hadoop.fs.FileSystem
import org.apache.hadoop.io.SequenceFile.CompressionType
Expand All @@ -46,7 +46,6 @@ import org.apache.spark.Partitioner.defaultPartitioner
import org.apache.spark.SparkContext._
import org.apache.spark.partial.{BoundedDouble, PartialResult}
import org.apache.spark.serializer.Serializer
import org.apache.spark.util.SerializableHyperLogLog

/**
* Extra functions available on RDDs of (key, value) pairs through an implicit conversion.
Expand Down Expand Up @@ -218,14 +217,29 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
* The accuracy of approximation can be controlled through the relative standard deviation
* (relativeSD) parameter, which also controls the amount of memory used. Lower values result in
* more accurate counts but increase the memory footprint and vice versa. Uses the provided
* Partitioner to partition the output RDD.
* [[Partitioner]] to partition the output RDD.
*
* The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
* Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available at
* [[http://research.google.com/pubs/pub40671.html]].
*/
def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): RDD[(K, Long)] = {
val createHLL = (v: V) => new SerializableHyperLogLog(new HyperLogLog(relativeSD)).add(v)
val mergeValueHLL = (hll: SerializableHyperLogLog, v: V) => hll.add(v)
val mergeHLL = (h1: SerializableHyperLogLog, h2: SerializableHyperLogLog) => h1.merge(h2)
val precision = (math.log((1.106 / relativeSD) * (1.106 / relativeSD)) / math.log(2)).toInt
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@ash211 ash211 May 28, 2014

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Where does this magic value of 1.106 come from?

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@rxin rxin May 28, 2014

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I'm not even sure if the math is correct yet. Will update the PR once I confirm it.

val createHLL = (v: V) => {
val hll = new HyperLogLogPlus(precision)
hll.offer(v)
hll
}
val mergeValueHLL = (hll: HyperLogLogPlus, v: V) => {
hll.offer(v)
hll
}
val mergeHLL = (h1: HyperLogLogPlus, h2: HyperLogLogPlus) => {
h1.addAll(h2)
h1
}

combineByKey(createHLL, mergeValueHLL, mergeHLL, partitioner).mapValues(_.value.cardinality())
combineByKey(createHLL, mergeValueHLL, mergeHLL, partitioner).mapValues(_.cardinality())
}

/**
Expand Down
21 changes: 17 additions & 4 deletions core/src/main/scala/org/apache/spark/rdd/RDD.scala
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Expand Up @@ -24,7 +24,7 @@ import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer
import scala.reflect.{classTag, ClassTag}

import com.clearspring.analytics.stream.cardinality.HyperLogLog
import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus
import org.apache.hadoop.io.BytesWritable
import org.apache.hadoop.io.compress.CompressionCodec
import org.apache.hadoop.io.NullWritable
Expand All @@ -41,7 +41,7 @@ import org.apache.spark.partial.CountEvaluator
import org.apache.spark.partial.GroupedCountEvaluator
import org.apache.spark.partial.PartialResult
import org.apache.spark.storage.StorageLevel
import org.apache.spark.util.{BoundedPriorityQueue, SerializableHyperLogLog, Utils}
import org.apache.spark.util.{BoundedPriorityQueue, Utils}
import org.apache.spark.util.collection.OpenHashMap
import org.apache.spark.util.random.{BernoulliSampler, PoissonSampler}

Expand Down Expand Up @@ -925,11 +925,24 @@ abstract class RDD[T: ClassTag](
* (relativeSD) parameter, which also controls the amount of memory used. Lower values result in
* more accurate counts but increase the memory footprint and vise versa. The default value of
* relativeSD is 0.05.
*
* The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
* Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available at
* [[http://research.google.com/pubs/pub40671.html]].
*/
@Experimental
def countApproxDistinct(relativeSD: Double = 0.05): Long = {
val zeroCounter = new SerializableHyperLogLog(new HyperLogLog(relativeSD))
aggregate(zeroCounter)(_.add(_), _.merge(_)).value.cardinality()
val precision = (math.log((1.106 / relativeSD) * (1.106 / relativeSD)) / math.log(2)).toInt
val zeroCounter = new HyperLogLogPlus(precision)
aggregate(zeroCounter)(
(hll: HyperLogLogPlus, v: T) => {
hll.offer(v)
hll
},
(h1: HyperLogLogPlus, h2: HyperLogLogPlus) => {
h1.addAll(h2)
h2
}).cardinality()
}

/**
Expand Down
52 changes: 0 additions & 52 deletions core/src/main/scala/org/apache/spark/util/SerializableHyperLogLog.scala
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8 changes: 2 additions & 6 deletions core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala
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Expand Up @@ -126,9 +126,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext {
val stacked = (1 to 100).flatMap(i => (1 to i).map(j => (i, j)))
val rdd1 = sc.parallelize(stacked)
val counted1 = rdd1.countApproxDistinctByKey(relativeSD).collect()
counted1.foreach{
case(k, count) => assert(error(count, k) < relativeSD)
}
counted1.foreach { case (k, count) => assert(error(count, k) < relativeSD) }

val rnd = new Random()

Expand All @@ -139,9 +137,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext {
}
val rdd2 = sc.parallelize(randStacked)
val counted2 = rdd2.countApproxDistinctByKey(relativeSD, 4).collect()
counted2.foreach{
case(k, count) => assert(error(count, k) < relativeSD)
}
counted2.foreach { case(k, count) => assert(error(count, k) < relativeSD) }
}

test("join") {
Expand Down
2 changes: 1 addition & 1 deletion pom.xml
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Expand Up @@ -284,7 +284,7 @@
<dependency>
<groupId>com.clearspring.analytics</groupId>
<artifactId>stream</artifactId>
<version>2.5.1</version>
<version>2.7.0</version>
<exclusions>
<!-- Only HyperLogLog is used, which doesn't depend on fastutil -->
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@mengxr mengxr Jun 3, 2014

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Please also update the comment.

<exclusion>
Expand Down
2 changes: 1 addition & 1 deletion project/SparkBuild.scala
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Expand Up @@ -358,7 +358,7 @@ object SparkBuild extends Build {
"com.twitter" %% "chill" % chillVersion excludeAll(excludeAsm),
"com.twitter" % "chill-java" % chillVersion excludeAll(excludeAsm),
"org.tachyonproject" % "tachyon" % "0.4.1-thrift" excludeAll(excludeHadoop, excludeCurator, excludeEclipseJetty, excludePowermock),
"com.clearspring.analytics" % "stream" % "2.5.1" excludeAll(excludeFastutil),
"com.clearspring.analytics" % "stream" % "2.7.0" excludeAll(excludeFastutil),
"org.spark-project" % "pyrolite" % "2.0.1",
"net.sf.py4j" % "py4j" % "0.8.1"
),
Expand Down