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高校廉洁文化建设网站,河南郑州最新事件,学校网站建设的验收单,帮人做网站如何收费目录 1. 高级WordCount1.1 IntWritable降序排列1.2 输入输出格式1.3 处理流程 2. 代码和结果2.1 pom.xml中依赖配置2.2 工具类util2.3 高级WordCount2.4 结果 参考 本文引用的Apache Hadoop源代码基于Apache许可证 2.0，详情请参阅 Apache许可证2.0。 1. 高级WordCo…

目录

  • 1. 高级WordCount
    • 1.1 IntWritable降序排列
    • 1.2 输入输出格式
    • 1.3 处理流程
  • 2. 代码和结果
    • 2.1 pom.xml中依赖配置
    • 2.2 工具类util
    • 2.3 高级WordCount
    • 2.4 结果
  • 参考

  本文引用的Apache Hadoop源代码基于Apache许可证 2.0，详情请参阅 Apache许可证2.0。

1. 高级WordCount

  文本内容就是下文2.3中的代码，目标是要实现文本计数，并且数量在前，文本在后，同时数量要升序排列。

1.1 IntWritable降序排列

  IntWritable类型中实现一个升序排列的比较器，代码如下。而实现IntWritable降序排序只需要定义一个新类，继承IntWritable.Comparator，并且重载public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2)，使其返回值为父类该方法返回值的相反数。此外，如果你想要让作为键的IntWritable类型进行降序排列，还需要在MapReduce任务调度代码中设置Job.setSortComparatorClass(比较器.class)。

/*** 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.*/public static class Comparator extends WritableComparator {public Comparator() {super(IntWritable.class);}@Overridepublic int compare(byte[] b1, int s1, int l1,byte[] b2, int s2, int l2) {int thisValue = readInt(b1, s1);int thatValue = readInt(b2, s2);return (thisValue<thatValue ? -1 : (thisValue==thatValue ? 0 : 1));}}

1.2 输入输出格式

java类名输入/输出功能
org.apache.hadoop.mapreduce.lib.input.TextInputFormatMapReduce默认的输入格式将输入文件按行分割，每一行作为<key, value>对，其中key是行的偏移量(从0开始)，value 是行的内容
org.apache.hadoop.mapreduce.lib.output.TextOutputFormatMapReduce默认的输出格式将输出写成文本文件，每个<key, value>对占一行，key和value之间用制表符(\t)分隔
org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormatSequenceFile的输入格式读取Hadoop的二进制文件格式SequenceFile
org.apache.hadoop.mapreduce.lib.input.SequenceFileOutputFormatSequenceFile的输出格式将输出写成Hadoop的二进制文件格式SequenceFile

  (Hadoop定义的SequenceFile是一种高效、可分割的二进制文件格式，支持压缩)
  (Hadoop定义了好多输入输出格式，由于我没有详细使用，这里就不介绍了)
  如果要进行多次MapReduce作业，中间结果可以以SequenceFile的形式存储，加速作业的运行。

1.3 处理流程

  首先高级WordCount也要像普通WordCount一样对文本进行计数，因此Reduce函数输入的键值对为<Text，IntWritable>。而最终要求的结果键值对为<IntWritable, Text>，如果把Reduce函数的输出键值对直接变为<IntWritable, Text>并且在该任务中只使用一个作业的话，你会发现无法完成IntWritable降序排列(尽管你可以已经设置SortComparatorClass)，那是因为Shuffle过程的排序只会发生在Map结束后Reduce发生前，这时键的类型是Text而非IntWritable。
  为了解决这个任务，需要进行两次作业，第一次作业负责计数，并以SequenceFile的格式输出，Map的输出、Reduce的输入和输出均为<Text, IntWritable>，最终文件输出格式选择SequenceFileOutputFormat；第二次作业负责交换键值对，并以SequenceFile的个数读入，然后再对键进行降序排列，这就需要使用Hadoop自带的org.apache.hadoop.mapreduce.lib.map.InverseMapper，它能交换键值对。这次作业的输入格式选择SequenceFileInputFormat，Map输入和Map输出分别是<Text, IntWritable>、<IntWritable, Text>，这时设置SortComparatorClass就可以实现IntWritable降序排列。

2. 代码和结果

2.1 pom.xml中依赖配置

  <dependencies><dependency><groupId>junit</groupId><artifactId>junit</artifactId><version>4.11</version><scope>test</scope></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-common</artifactId><version>3.3.6</version><exclusions><exclusion><groupId>org.slf4j</groupId><artifactId>slf4j-log4j12</artifactId></exclusion></exclusions></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-mapreduce-client-core</artifactId><version>3.3.6</version><type>pom</type></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-mapreduce-client-jobclient</artifactId><version>3.3.6</version></dependency></dependencies>

2.2 工具类util

import java.net.URI;
import java.util.regex.Matcher;
import java.util.regex.Pattern;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;public class util {public static FileSystem getFileSystem(String uri, Configuration conf) throws Exception {URI add = new URI(uri);return FileSystem.get(add, conf);}public static void removeALL(String uri, Configuration conf, String path) throws Exception {FileSystem fs = getFileSystem(uri, conf);if (fs.exists(new Path(path))) {boolean isDeleted = fs.delete(new Path(path), true);System.out.println("Delete Output Folder? " + isDeleted);}}public static void removeALL(String uri, Configuration conf, String[] pathList) throws Exception {FileSystem fs = getFileSystem(uri, conf);for (String path : pathList) {if (fs.exists(new Path(path))) {boolean isDeleted = fs.delete(new Path(path), true);System.out.println(String.format("Delete %s? %s", path, isDeleted));}}}public static void showResult(String uri, Configuration conf, String path) throws Exception {FileSystem fs = getFileSystem(uri, conf);String regex = "part-r-";Pattern pattern = Pattern.compile(regex);if (fs.exists(new Path(path))) {FileStatus[] files = fs.listStatus(new Path(path));for (FileStatus file : files) {Matcher matcher = pattern.matcher(file.getPath().toString());if (matcher.find()) {System.out.println(file.getPath() + ":");FSDataInputStream openStream = fs.open(file.getPath());IOUtils.copyBytes(openStream, System.out, 1024);openStream.close();}}}}
}

2.3 高级WordCount

import java.io.IOException;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.map.InverseMapper;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;public class App {public static class IntWritableDecreaseingComparator extends IntWritable.Comparator {@Overridepublic int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {return -super.compare(b1, s1, l1, b2, s2, l2);}}public static class MyMapper extends Mapper<LongWritable, Text, Text, IntWritable> {@Overrideprotected void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException {String[] splitStr = value.toString().split("\\s+");for (String str : splitStr) {context.write(new Text(str), new IntWritable(1));}}}public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {@Overrideprotected void reduce(Text key, Iterable<IntWritable> values, Context context)throws IOException, InterruptedException {int sum = 0;for (IntWritable val : values) {sum += val.get();}context.write(key, new IntWritable(sum));}}public static void main(String[] args) throws Exception {Configuration conf = new Configuration();String tempPath = "hdfs://localhost:9000/user/developer/Temp";String[] myArgs = {"file:///home/developer/CodeArtsProjects/advanced-word-count/AdvancedWordCount.txt","hdfs://localhost:9000/user/developer/AdvancedWordCount/output"};util.removeALL("hdfs://localhost:9000", conf, new String[] { tempPath, myArgs[myArgs.length - 1] });Job job = Job.getInstance(conf, "AdvancedWordCount");job.setJarByClass(App.class);job.setMapperClass(MyMapper.class);job.setReducerClass(MyReducer.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(IntWritable.class);job.setOutputFormatClass(SequenceFileOutputFormat.class);job.setNumReduceTasks(2);for (int i = 0; i < myArgs.length - 1; i++) {FileInputFormat.addInputPath(job, new Path(myArgs[i]));}FileOutputFormat.setOutputPath(job, new Path(tempPath));int res1 = job.waitForCompletion(true) ? 0 : 1;if (res1 == 0) {Job sortJob = Job.getInstance(conf, "Sort");sortJob.setJarByClass(App.class);sortJob.setMapperClass(InverseMapper.class);sortJob.setInputFormatClass(SequenceFileInputFormat.class);sortJob.setOutputKeyClass(IntWritable.class);sortJob.setOutputValueClass(Text.class);sortJob.setSortComparatorClass(IntWritableDecreaseingComparator.class);FileInputFormat.addInputPath(sortJob, new Path(tempPath));FileOutputFormat.setOutputPath(sortJob, new Path(myArgs[myArgs.length - 1]));int res2 = sortJob.waitForCompletion(true) ? 0 : 1;if (res2 == 0) {System.out.println("高级WordCount结果为:");util.showResult("hdfs://localhost:9000", conf, myArgs[myArgs.length - 1]);}System.exit(res2);}System.exit(res1);}
}

2.4 结果

在这里插入图片描述
  结果文件内容如下：

64
14      {
13      }
12      import
8       int
7       public
7       =
4       static
4       class
4       -
4       new
4       @Override
3       for
3       :
3       void
3       throws
3       extends
2       l1,
2       1;
2       0;
2       String[]
2       s2,
2       s1,
2       i
2       context.write(new
2       context)
2       conf,
2       InterruptedException
2       key,
2       IntWritable,
2       return
2       IOException,
2       b2,
2       sum
2       Context
2       protected
2       myArgs[myArgs.length
2       Text,
2       1]);
1       };
1       values,
1       values)
1       value.toString().split("\\s+");
1       value,
1       val.get();
1       val
1       util.showResult("hdfs://localhost:9000",
1       util.removeALL("hdfs://localhost:9000",
1       str
1       splitStr)
1       splitStr
1       res
1       reduce(Text
1       org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
1       org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
1       org.apache.hadoop.mapreduce.Reducer;
1       org.apache.hadoop.mapreduce.Mapper;
1       org.apache.hadoop.mapreduce.Job;
1       org.apache.hadoop.io.WritableComparable;
1       org.apache.hadoop.io.Text;
1       org.apache.hadoop.io.LongWritable;
1       org.apache.hadoop.io.IntWritable;
1       org.apache.hadoop.fs.Path;
1       org.apache.hadoop.conf.Configuration;
1       myArgs.length
1       myArgs
1       map(LongWritable
1       main(String[]
1       l2);
1       l2)
1       key);
1       job.waitForCompletion(true)
1       job.setSortComparatorClass(IntWritableDecreaseingComparator.class);
1       job.setReducerClass(MyReducer.class);
1       job.setOutputValueClass(Text.class);
1       job.setOutputKeyClass(IntWritable.class);
1       job.setMapperClass(MyMapper.class);
1       job.setJarByClass(App.class);
1       job.setCombinerClass(MyReducer.class);
1       job
1       java.io.IOException;
1       if
1       i++)
1       compare(byte[]
1       compare(WritableComparable
1       byte[]
1       b1,
1       b);
1       b)
1       args)
1       a,
1       WritableComparable
1       Text>
1       Text(str),
1       Text
1       System.out.println("高级WordCount结果为:");
1       System.exit(res);
1       Reducer<Text,
1       Path(myArgs[myArgs.length
1       Path(myArgs[i]));
1       MyReducer
1       MyMapper
1       Mapper<LongWritable,
1       Job.getInstance(conf,
1       Job
1       Iterable<IntWritable>
1       IntWritableDecreaseingComparator
1       IntWritable>
1       IntWritable.Comparator
1       IntWritable(sum),
1       IntWritable(1));
1       FileOutputFormat.setOutputPath(job,
1       FileInputFormat.addInputPath(job,
1       Exception
1       Configuration();
1       Configuration
1       App
1       ?
1       ==
1       <
1       1]));
1       0)
1       0
1       -super.compare(b1,
1       -super.compare(a,
1       +=
1       (res
1       (int
1       (String
1       (IntWritable
1       "hdfs://localhost:9000/user/developer/AdvancedWordCount/output"
1       "file:///home/developer/CodeArtsProjects/AdvancedWordCount.txt",
1       "AdvancedWordCount");
1       conf

参考

http://www.dtcms.com/wzjs/355042.html

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