spark SparkPageRank 源码
spark SparkPageRank 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala
/*
* 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.
*/
// scalastyle:off println
package org.apache.spark.examples
import org.apache.spark.sql.SparkSession
/**
* Computes the PageRank of URLs from an input file. Input file should
* be in format of:
* URL neighbor URL
* URL neighbor URL
* URL neighbor URL
* ...
* where URL and their neighbors are separated by space(s).
*
* This is an example implementation for learning how to use Spark. For more conventional use,
* please refer to org.apache.spark.graphx.lib.PageRank
*
* Example Usage:
* {{{
* bin/run-example SparkPageRank data/mllib/pagerank_data.txt 10
* }}}
*/
object SparkPageRank {
def showWarning(): Unit = {
System.err.println(
"""WARN: This is a naive implementation of PageRank and is given as an example!
|Please use the PageRank implementation found in org.apache.spark.graphx.lib.PageRank
|for more conventional use.
""".stripMargin)
}
def main(args: Array[String]): Unit = {
if (args.length < 1) {
System.err.println("Usage: SparkPageRank <file> <iter>")
System.exit(1)
}
showWarning()
val spark = SparkSession
.builder
.appName("SparkPageRank")
.getOrCreate()
val iters = if (args.length > 1) args(1).toInt else 10
val lines = spark.read.textFile(args(0)).rdd
val links = lines.map{ s =>
val parts = s.split("\\s+")
(parts(0), parts(1))
}.distinct().groupByKey().cache()
var ranks = links.mapValues(v => 1.0)
for (i <- 1 to iters) {
val contribs = links.join(ranks).values.flatMap{ case (urls, rank) =>
val size = urls.size
urls.map(url => (url, rank / size))
}
ranks = contribs.reduceByKey(_ + _).mapValues(0.15 + 0.85 * _)
}
val output = ranks.collect()
output.foreach(tup => println(s"${tup._1} has rank: ${tup._2} ."))
spark.stop()
}
}
// scalastyle:on println
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