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Diffstat (limited to 'examples/src')
-rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala | 114 | ||||
-rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala | 92 |
2 files changed, 206 insertions, 0 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala new file mode 100644 index 0000000000..683752ac96 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala @@ -0,0 +1,114 @@ +/* + * 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/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala new file mode 100644 index 0000000000..62db5e663b --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala @@ -0,0 +1,92 @@ +/* + * 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() + } +} |