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author | Adam Pingel <adam@axle-lang.org> | 2014-11-17 10:47:29 -0800 |
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committer | Patrick Wendell <pwendell@gmail.com> | 2014-11-17 10:47:29 -0800 |
commit | e7690ed20a2734b7ca88e78a60a8e75ba19e9d8b (patch) | |
tree | d3daefdd3d6d99a4d9ef852c13c789b4f3a11ba7 /examples/scala-2.10/src | |
parent | 5c92d47ad2e3414f2ae089cb47f3c6daccba8d90 (diff) | |
download | spark-e7690ed20a2734b7ca88e78a60a8e75ba19e9d8b.tar.gz spark-e7690ed20a2734b7ca88e78a60a8e75ba19e9d8b.tar.bz2 spark-e7690ed20a2734b7ca88e78a60a8e75ba19e9d8b.zip |
SPARK-2811 upgrade algebird to 0.8.1
Author: Adam Pingel <adam@axle-lang.org>
Closes #3282 from adampingel/master and squashes the following commits:
70c8d3c [Adam Pingel] relocate the algebird example back to example/src
7a9d8be [Adam Pingel] SPARK-2811 upgrade algebird to 0.8.1
Diffstat (limited to 'examples/scala-2.10/src')
2 files changed, 0 insertions, 206 deletions
diff --git a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala b/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala deleted file mode 100644 index 683752ac96..0000000000 --- a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala +++ /dev/null @@ -1,114 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.streaming - -import com.twitter.algebird._ - -import org.apache.spark.SparkConf -import org.apache.spark.SparkContext._ -import org.apache.spark.storage.StorageLevel -import org.apache.spark.streaming.{Seconds, StreamingContext} -import org.apache.spark.streaming.StreamingContext._ -import org.apache.spark.streaming.twitter._ - -// scalastyle:off -/** - * Illustrates the use of the Count-Min Sketch, from Twitter's Algebird library, to compute - * windowed and global Top-K estimates of user IDs occurring in a Twitter stream. - * <br> - * <strong>Note</strong> that since Algebird's implementation currently only supports Long inputs, - * the example operates on Long IDs. Once the implementation supports other inputs (such as String), - * the same approach could be used for computing popular topics for example. - * <p> - * <p> - * <a href= - * "http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/"> - * This blog post</a> has a good overview of the Count-Min Sketch (CMS). The CMS is a data - * structure for approximate frequency estimation in data streams (e.g. Top-K elements, frequency - * of any given element, etc), that uses space sub-linear in the number of elements in the - * stream. Once elements are added to the CMS, the estimated count of an element can be computed, - * as well as "heavy-hitters" that occur more than a threshold percentage of the overall total - * count. - * <p><p> - * Algebird's implementation is a monoid, so we can succinctly merge two CMS instances in the - * reduce operation. - */ -// scalastyle:on -object TwitterAlgebirdCMS { - def main(args: Array[String]) { - StreamingExamples.setStreamingLogLevels() - - // CMS parameters - val DELTA = 1E-3 - val EPS = 0.01 - val SEED = 1 - val PERC = 0.001 - // K highest frequency elements to take - val TOPK = 10 - - val filters = args - val sparkConf = new SparkConf().setAppName("TwitterAlgebirdCMS") - val ssc = new StreamingContext(sparkConf, Seconds(10)) - val stream = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_ONLY_SER_2) - - val users = stream.map(status => status.getUser.getId) - - val cms = new CountMinSketchMonoid(EPS, DELTA, SEED, PERC) - var globalCMS = cms.zero - val mm = new MapMonoid[Long, Int]() - var globalExact = Map[Long, Int]() - - val approxTopUsers = users.mapPartitions(ids => { - ids.map(id => cms.create(id)) - }).reduce(_ ++ _) - - val exactTopUsers = users.map(id => (id, 1)) - .reduceByKey((a, b) => a + b) - - approxTopUsers.foreachRDD(rdd => { - if (rdd.count() != 0) { - val partial = rdd.first() - val partialTopK = partial.heavyHitters.map(id => - (id, partial.frequency(id).estimate)).toSeq.sortBy(_._2).reverse.slice(0, TOPK) - globalCMS ++= partial - val globalTopK = globalCMS.heavyHitters.map(id => - (id, globalCMS.frequency(id).estimate)).toSeq.sortBy(_._2).reverse.slice(0, TOPK) - println("Approx heavy hitters at %2.2f%% threshold this batch: %s".format(PERC, - partialTopK.mkString("[", ",", "]"))) - println("Approx heavy hitters at %2.2f%% threshold overall: %s".format(PERC, - globalTopK.mkString("[", ",", "]"))) - } - }) - - exactTopUsers.foreachRDD(rdd => { - if (rdd.count() != 0) { - val partialMap = rdd.collect().toMap - val partialTopK = rdd.map( - {case (id, count) => (count, id)}) - .sortByKey(ascending = false).take(TOPK) - globalExact = mm.plus(globalExact.toMap, partialMap) - val globalTopK = globalExact.toSeq.sortBy(_._2).reverse.slice(0, TOPK) - println("Exact heavy hitters this batch: %s".format(partialTopK.mkString("[", ",", "]"))) - println("Exact heavy hitters overall: %s".format(globalTopK.mkString("[", ",", "]"))) - } - }) - - ssc.start() - ssc.awaitTermination() - } -} diff --git a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala b/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala deleted file mode 100644 index 62db5e663b..0000000000 --- a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala +++ /dev/null @@ -1,92 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.streaming - -import com.twitter.algebird.HyperLogLogMonoid -import com.twitter.algebird.HyperLogLog._ - -import org.apache.spark.storage.StorageLevel -import org.apache.spark.streaming.{Seconds, StreamingContext} -import org.apache.spark.streaming.twitter._ -import org.apache.spark.SparkConf - -// scalastyle:off -/** - * Illustrates the use of the HyperLogLog algorithm, from Twitter's Algebird library, to compute - * a windowed and global estimate of the unique user IDs occurring in a Twitter stream. - * <p> - * <p> - * This <a href="http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/"> - * blog post</a> and this - * <a href= "http://highscalability.com/blog/2012/4/5/big-data-counting-how-to-count-a-billion-distinct-objects-us.html"> - * blog post</a> - * have good overviews of HyperLogLog (HLL). HLL is a memory-efficient datastructure for - * estimating the cardinality of a data stream, i.e. the number of unique elements. - * <p><p> - * Algebird's implementation is a monoid, so we can succinctly merge two HLL instances in the - * reduce operation. - */ -// scalastyle:on -object TwitterAlgebirdHLL { - def main(args: Array[String]) { - - StreamingExamples.setStreamingLogLevels() - - /** Bit size parameter for HyperLogLog, trades off accuracy vs size */ - val BIT_SIZE = 12 - val filters = args - val sparkConf = new SparkConf().setAppName("TwitterAlgebirdHLL") - val ssc = new StreamingContext(sparkConf, Seconds(5)) - val stream = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_ONLY_SER) - - val users = stream.map(status => status.getUser.getId) - - val hll = new HyperLogLogMonoid(BIT_SIZE) - var globalHll = hll.zero - var userSet: Set[Long] = Set() - - val approxUsers = users.mapPartitions(ids => { - ids.map(id => hll(id)) - }).reduce(_ + _) - - val exactUsers = users.map(id => Set(id)).reduce(_ ++ _) - - approxUsers.foreachRDD(rdd => { - if (rdd.count() != 0) { - val partial = rdd.first() - globalHll += partial - println("Approx distinct users this batch: %d".format(partial.estimatedSize.toInt)) - println("Approx distinct users overall: %d".format(globalHll.estimatedSize.toInt)) - } - }) - - exactUsers.foreachRDD(rdd => { - if (rdd.count() != 0) { - val partial = rdd.first() - userSet ++= partial - println("Exact distinct users this batch: %d".format(partial.size)) - println("Exact distinct users overall: %d".format(userSet.size)) - println("Error rate: %2.5f%%".format(((globalHll.estimatedSize / userSet.size.toDouble) - 1 - ) * 100)) - } - }) - - ssc.start() - ssc.awaitTermination() - } -} |