/*
Copyright Google Inc. 2018
Licensed 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 demo
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.dstream.DStream
object HashTagsStreaming {
case class Popularity(tag: String, amount: Int)
// [START extract]
private[demo] def extractTrendingTags(input: RDD[String]): RDD[Popularity] =
input.flatMap(_.split("\\s+")) // Split on any white character
.filter(_.startsWith("#")) // Keep only the hashtags
// Remove punctuation, force to lowercase
.map(_.replaceAll("[,.!?:;]", "").toLowerCase)
// Remove the first #
.map(_.replaceFirst("^#", ""))
.filter(!_.isEmpty) // Remove any non-words
.map((_, 1)) // Create word count pairs
.reduceByKey(_ + _) // Count occurrences
.map(r => Popularity(r._1, r._2))
// Sort hashtags by descending number of occurrences
.sortBy(r => (-r.amount, r.tag), ascending = true)
// [END extract]
def processTrendingHashTags(input: DStream[String],
windowLength: Int,
slidingInterval: Int,
n: Int,
handler: Array[Popularity] => Unit): Unit = {
val sortedHashtags: DStream[Popularity] = input
.window(Seconds(windowLength), Seconds(slidingInterval)) //create a window
.transform(extractTrendingTags(_)) //apply transformation
sortedHashtags.foreachRDD(rdd => {
handler(rdd.take(n)) //take top N hashtags and save to external source
})
}
}
