PageRank估计是很多面试场合上镜率比较高的吧，面试Spark的时候会被问到，最近flink热，估计也会被问到吧，浪尖就在这里帮大家解决这个疑难杂症。
算法常见的原题是：
pagerank的算法会维护两个数据集：一个由（pageID，linkList）的元素组成，包含每个页面的相邻页面的列表；另一个由（pageID，rank）元素组成，包含每个页面的当前排序值。它按如下步骤进行计算。
将每个页面的排序值初始化为1.0。
在每次迭代中，对页面p，向其每个相邻页面（有直接链接的页面）发送一个值为rank(p)/numNeighbors(p)的贡献值。
将每个页面的排序值设为0.15 + 0.85 * contributionsReceived。
最后两个步骤会重复几个循环，在此过程中，算法会逐渐收敛于每个页面的实际PageRank值。在实际操作中，收敛通常需要大约10轮迭代。
下面废话少说直接上案例吧。
import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.GroupReduceFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.functions.FunctionAnnotation.ForwardedFields;
import org.apache.flink.api.java.operators.IterativeDataSet;
import org.apache.flink.api.java.tuple.Tuple1;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.util.Collector;
import java.util.ArrayList;
import static org.apache.flink.api.java.aggregation.Aggregations.SUM;
/**
算法会维护两个数据集：一个由（pageID，linkList）的元素组成，包含每个页面的相邻页面的列表；
另一个由（pageID，rank）元素组成，包含每个页面的当前排序值。它按如下步骤进行计算：
将每个页面的排序值初始化为1.0。
在每次迭代中，对页面p，向其每个相邻页面（有直接链接的页面）发送一个值为rank(p)/numNeighbors(p)的贡献值。
将每个页面的排序值设为0.15 + 0.85 * contributionsReceived。
最后两个步骤会重复几个循环，在此过程中，算法会逐渐收敛于每个游戏拍卖平台的实际PageRank值。在实际操作中，收敛通常需要大约10轮迭代。
*/
@SuppressWarnings("serial")
public class PageRank {
private static final double DAMPENING_FACTOR = 0.85;
private static final double EPSILON = 0.0001;
// *
// PROGRAM
// *
public static void main(String[] args) throws Exception {
ParameterTool params = ParameterTool.fromArgs(args);
final int numPages = params.getInt("numPages", PageRankData.getNumberOfPages());
final int maxIterations = params.getInt("iterations", 10);
// set up execution environment
final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
// make the parameters available to the web ui
env.getConfig().setGlobalJobParameters(params);
// get input data
DataSet<Long> pagesInput = getPagesDataSet(env, params);
DataSet<Tuple2<Long, Long>> linksInput = getLinksDataSet(env, params);
// 初始化rank <pageID,rank>
DataSet<Tuple2<Long, Double>> pagesWithRanks = pagesInput.
map(new RankAssigner((1.0d / numPages)));
// 获取<pageID,NeighborsLinkList>
DataSet<Tuple2<Long, Long[]>> adjacencyListInput =
linksInput.groupBy(0).reduceGroup(new BuildOutgoingEdgeList());
// set iterative data set
IterativeDataSet<Tuple2<Long, Double>> iteration = pagesWithRanks.iterate(maxIterations);
DataSet<Tuple2<Long, Double>> newRanks = iteration
// join pages with outgoing edges and distribute rank
.join(adjacencyListInput).where(0).equalTo(0).flatMap(new JoinVertexWithEdgesMatch())
// collect and sum ranks
.groupBy(0).aggregate(SUM, 1)
// apply dampening factor
.map(new Dampener(DAMPENING_FACTOR, numPages));
DataSet<Tuple2<Long, Double>> finalPageRanks = iteration.closeWith(
newRanks,
newRanks.join(iteration).where(0).equalTo(0)
// termination condition
.filter(new EpsilonFilter())); //自定义了一个终止条件
// emit result
if (params.has("output")) {
finalPageRanks.writeAsCsv(params.get("output"), "\n", " ");
// execute program
env.execute("Basic Page Rank Example");
} else {
System.out.println("Printing result to stdout. Use --output to specify output path.");
finalPageRanks.print();
}}
// *
// USER FUNCTIONS
// *
/**
- A map function that assigns an initial rank to all pages.
*/
public static final class RankAssigner implements MapFunction<Long, Tuple2<Long, Double>> {
Tuple2<Long, Double> outPageWithRank;
public RankAssigner(double rank) {
this.outPageWithRank = new Tuple2<Long, Double>(-1L, rank);
}
@Override
public Tuple2<Long, Double> map(Long page) {
outPageWithRank.f0 = page;
return outPageWithRank;
}}
/**
- A reduce function that takes a sequence of edges and builds the adjacency list for the vertex where the edges
- originate. Run as a pre-processing step.
*/
@ForwardedFields("0")
public static final class BuildOutgoingEdgeList implements GroupReduceFunction<Tuple2<Long, Long>, Tuple2<Long, Long[]>> {
private final ArrayList<Long> neighbors = new ArrayList<Long>();
@Override
public void reduce(Iterable<Tuple2<Long, Long>> values, Collector<Tuple2<Long, Long[]>> out) {
neighbors.clear();
Long id = 0L;
for (Tuple2<Long, Long> n : values) {
id = n.f0;
neighbors.add(n.f1);
}
out.collect(new Tuple2<Long, Long[]>(id, neighbors.toArray(new Long[neighbors.size()])));
}}
/**
- Join function that distributes a fraction of a vertex's rank to all neighbors.
*/
public static final class JoinVertexWithEdgesMatch implements FlatMapFunction<Tuple2<Tuple2<Long, Double>, Tuple2<Long, Long[]>>, Tuple2<Long, Double>> {
@Override
public void flatMap(Tuple2<Tuple2<Long, Double>, Tuple2<Long, Long[]>> value, Collector<Tuple2<Long, Double>> out){
Long[] neighbors = value.f1.f1;
double rank = value.f0.f1;
double rankToDistribute = rank / ((double) neighbors.length);
for (Long neighbor: neighbors) {
out.collect(new Tuple2<Long, Double>(neighbor, rankToDistribute));
}
}}
/**
- The function that applies the page rank dampening formula.
*/
@ForwardedFields("0")
public static final class Dampener implements MapFunction<Tuple2<Long, Double>, Tuple2<Long, Double>> {
private final double dampening;
private final double randomJump;
public Dampener(double dampening, double numVertices) {
this.dampening = dampening;
this.randomJump = (1 - dampening) / numVertices;
}
@Override
public Tuple2<Long, Double> map(Tuple2<Long, Double> value) {
value.f1 = (value.f1 * dampening) + randomJump;
return value;
}}
/**
- Filter that filters vertices where the rank difference is below a threshold.
*/
public static final class EpsilonFilter implements FilterFunction<Tuple2<Tuple2<Long, Double>, Tuple2<Long, Double>>> {
@Override
public boolean filter(Tuple2<Tuple2<Long, Double>, Tuple2<Long, Double>> value) {
return Math.abs(value.f0.f1 - value.f1.f1) > EPSILON;
}}
// *
// UTIL METHODS
// *
private static DataSet getPagesDataSet(ExecutionEnvironment env, ParameterTool params) {
if (params.has("pages")) {
return env.readCsvFile(params.get("pages"))
.fieldDelimiter(" ")
.lineDelimiter("\n")
.types(Long.class)
.map(new MapFunction<Tuple1<Long>, Long>() {
@Override
public Long map(Tuple1<Long> v) {
return v.f0;
}
});
} else {
System.out.println("Executing PageRank example with default pages data set.");
System.out.println("Use --pages to specify file input.");
return PageRankData.getDefaultPagesDataSet(env);
}}
private static DataSet<Tuple2<Long, Long>> getLinksDataSet(ExecutionEnvironment env, ParameterTool params) {
if (params.has("links")) {
return env.readCsvFile(params.get("links"))
.fieldDelimiter(" ")
.lineDelimiter("\n")
.types(Long.class, Long.class);
} else {
System.out.println("Executing PageRank example with default links data set.");
System.out.println("Use --links to specify file input.");
return PageRankData.getDefaultEdgeDataSet(env);
}}
}
public class PageRankData {
public static final Object[][] EDGES = {
{1L, 2L},
{1L, 15L},
{2L, 3L},
{2L, 4L},
{2L, 5L},
{2L, 6L},
{2L, 7L},
{3L, 13L},
{4L, 2L},
{5L, 11L},
{5L, 12L},
{6L, 1L},
{6L, 7L},
{6L, 8L},
{7L, 1L},
{7L, 8L},
{8L, 1L},
{8L, 9L},
{8L, 10L},
{9L, 14L},
{9L, 1L},
{10L, 1L},
{10L, 13L},
{11L, 12L},
{11L, 1L},
{12L, 1L},
{13L, 14L},
{14L, 12L},
{15L, 1L},};
private static int numPages = 15;
public static DataSet<Tuple2<Long, Long>> getDefaultEdgeDataSet(ExecutionEnvironment env) {
List<Tuple2<Long, Long>> edges = new ArrayList<Tuple2<Long, Long>>();
for (Object[] e : EDGES) {
edges.add(new Tuple2<Long, Long>((Long) e[0], (Long) e[1]));
}
return env.fromCollection(edges);}
public static DataSet getDefaultPagesDataSet(ExecutionEnvironment env) {
return env.generateSequence(1, 15);}
public static int getNumberOfPages() {
return numPages;}
}
是不是很简单，大家可以自己搞个案例测试一下哦～
