diff --git a/examples/pom.xml b/examples/pom.xml
index f43af670c613fb6fa3dcaa09717b3e16e83af6da..f6125444e2a46e326482a6e91e860f37b37f2a8a 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -20,11 +20,10 @@
<artifactId>jetty-server</artifactId>
</dependency>
<dependency>
- <groupId>org.twitter4j</groupId>
- <artifactId>twitter4j-stream</artifactId>
- <version>3.0.3</version>
+ <groupId>com.twitter</groupId>
+ <artifactId>algebird-core_2.9.2</artifactId>
+ <version>0.1.9</version>
</dependency>
-
<dependency>
<groupId>org.scalatest</groupId>
<artifactId>scalatest_${scala.version}</artifactId>
diff --git a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala
new file mode 100644
index 0000000000000000000000000000000000000000..39a1a702eeae925f278c0b692e2c929bf5e7b74e
--- /dev/null
+++ b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala
@@ -0,0 +1,93 @@
+package spark.streaming.examples
+
+import spark.streaming.{Seconds, StreamingContext}
+import spark.storage.StorageLevel
+import com.twitter.algebird._
+import spark.streaming.StreamingContext._
+import spark.SparkContext._
+
+/**
+ * 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 datastructure
+ * 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.
+ */
+object TwitterAlgebirdCMS {
+ def main(args: Array[String]) {
+ if (args.length < 3) {
+ System.err.println("Usage: TwitterAlgebirdCMS <master> <twitter_username> <twitter_password>" +
+ " [filter1] [filter2] ... [filter n]")
+ System.exit(1)
+ }
+
+ // 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 Array(master, username, password) = args.slice(0, 3)
+ val filters = args.slice(3, args.length)
+
+ val ssc = new StreamingContext(master, "TwitterAlgebirdCMS", Seconds(10))
+ val stream = ssc.twitterStream(username, password, filters, StorageLevel.MEMORY_ONLY_SER)
+
+ val users = stream.map(status => status.getUser.getId)
+
+ val cms = new CountMinSketchMonoid(DELTA, EPS, 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.foreach(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.foreach(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()
+ }
+}
diff --git a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala
new file mode 100644
index 0000000000000000000000000000000000000000..914fba4ca22c54a2678ce2c14b2db1d1eb976265
--- /dev/null
+++ b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala
@@ -0,0 +1,71 @@
+package spark.streaming.examples
+
+import spark.streaming.{Seconds, StreamingContext}
+import spark.storage.StorageLevel
+import com.twitter.algebird.HyperLogLog._
+import com.twitter.algebird.HyperLogLogMonoid
+import spark.streaming.dstream.TwitterInputDStream
+
+/**
+ * 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.
+ */
+object TwitterAlgebirdHLL {
+ def main(args: Array[String]) {
+ if (args.length < 3) {
+ System.err.println("Usage: TwitterAlgebirdHLL <master> <twitter_username> <twitter_password>" +
+ " [filter1] [filter2] ... [filter n]")
+ System.exit(1)
+ }
+
+ /** Bit size parameter for HyperLogLog, trades off accuracy vs size */
+ val BIT_SIZE = 12
+ val Array(master, username, password) = args.slice(0, 3)
+ val filters = args.slice(3, args.length)
+
+ val ssc = new StreamingContext(master, "TwitterAlgebirdHLL", Seconds(5))
+ val stream = ssc.twitterStream(username, password, 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.foreach(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.foreach(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()
+ }
+}
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 7f432b60dbc1aa359e22879f39a4fc67d76c7e4e..fcaa7d3cee9bde587dffb7b7c7c90058d238201a 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -154,7 +154,8 @@ object SparkBuild extends Build {
)
def examplesSettings = sharedSettings ++ Seq(
- name := "spark-examples"
+ name := "spark-examples",
+ libraryDependencies ++= Seq("com.twitter" % "algebird-core_2.9.2" % "0.1.9")
)
def bagelSettings = sharedSettings ++ Seq(name := "spark-bagel")
diff --git a/streaming/pom.xml b/streaming/pom.xml
index 6ee7e59df39d16b1bf47ad23604bfe7f80f40f53..d78c39da0d1a60a1bdbf928c3981392f1bbc11c1 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -47,6 +47,16 @@
<artifactId>zkclient</artifactId>
<version>0.1</version>
</dependency>
+ <dependency>
+ <groupId>org.twitter4j</groupId>
+ <artifactId>twitter4j-stream</artifactId>
+ <version>3.0.3</version>
+ </dependency>
+ <dependency>
+ <groupId>org.twitter4j</groupId>
+ <artifactId>twitter4j-core</artifactId>
+ <version>3.0.3</version>
+ </dependency>
<dependency>
<groupId>org.scalatest</groupId>