Hadoop MapReduce IDEA上应用开发配置

2019-10-23 11:00:42 | 编辑

开发Hadoop MapReduce应用,首先使用idea新建一个maven项目,以便编译MapReduce程序并通过命令行或在自己的IDE中以本地(独立,standalone)模式运行它们。

1.Maven POM
编译和测试Map-Reduce 程序时需要的依赖项

<project>
    <modelVersion>4.0.0</modelVersion>
    <groupId>com.hadoopbook</groupId>
    <artifactId>hadoop-book-mr-dev</artifactId>
    <version>4.0</version>
    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <hadoop.version>2.5.1</hadoop.version>
    </properties>
    <dependencies>
        <!--Hadoopmainclientartifact-->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <!--Unittestartifacts-->
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.11</version>
            <scope>test</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.mrunit</groupId>
            <artifactId>mrunit</artifactId>
            <version>1.1.0</version>
            <classifier>hadoop2</classifier>
            <scope>test</scope>
        </dependency>
        <!--Hadooptestartifactforrunningminiclusters-->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-minicluster</artifactId>
            <version>${hadoop.version}</version>
            <scope>test</scope>
        </dependency
    </dependencies>
    <build>
        <finalName>hadoop-examples</finalName>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.1</version>
                <configuration>
                    <source>1.6</source>
                    <target>1.6</target>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-jar-plugin</artifactId>
                <version>2.5</version>
                <configuration>
                    <outputDirectory>${basedir}</outputDirectory>
                </configuration>
            </p1ugin>
        </plugins>
    </build>
</project>

要想构建MapReduce项目,你只需要有hadoop-client依赖关系,它包含了和HDFS及MapReduce 交互所需要的所有Hadoop client-side 类。当运行单元测试时,我们要使用junit类;当写MapReduce测试用例时,我们使用mrunit类。hadoop-minicluster 库中包含了“mini-”集群,这有助于在一个单JVM中运行Hadoop集群进行测试。
 

2.计数器案例代码

public class WordCount2 {

    public static class TokenizerMapper
            extends Mapper<Object, Text, Text, IntWritable>{

        static enum CountersEnum { INPUT_WORDS }

        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        private boolean caseSensitive;
        private Set<String> patternsToSkip = new HashSet<String>();

        private Configuration conf;
        private BufferedReader fis;

        @Override
        public void setup(Context context) throws IOException,
                InterruptedException {
            conf = context.getConfiguration();
            caseSensitive = conf.getBoolean("wordcount.case.sensitive", true);
            if (conf.getBoolean("wordcount.skip.patterns", false)) {
                URI[] patternsURIs = Job.getInstance(conf).getCacheFiles();
                for (URI patternsURI : patternsURIs) {
                    Path patternsPath = new Path(patternsURI.getPath());
                    String patternsFileName = patternsPath.getName().toString();
                    parseSkipFile(patternsFileName);
                }
            }
        }

        private void parseSkipFile(String fileName) {
            try {
                fis = new BufferedReader(new FileReader(fileName));
                String pattern = null;
                while ((pattern = fis.readLine()) != null) {
                    patternsToSkip.add(pattern);
                }
            } catch (IOException ioe) {
                System.err.println("Caught exception while parsing the cached file '"
                        + StringUtils.stringifyException(ioe));
            }
        }

        @Override
        public void map(Object key, Text value, Context context
        ) throws IOException, InterruptedException {
            String line = (caseSensitive) ?
                    value.toString() : value.toString().toLowerCase();
            for (String pattern : patternsToSkip) {
                line = line.replaceAll(pattern, "");
            }
            StringTokenizer itr = new StringTokenizer(line);
            while (itr.hasMoreTokens()) {
                word.set(itr.nextToken());
                context.write(word, one);
                Counter counter = context.getCounter(CountersEnum.class.getName(),
                        CountersEnum.INPUT_WORDS.toString());
                counter.increment(1);
            }
        }
    }

    public static class IntSumReducer
            extends Reducer<Text,IntWritable,Text,IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values,
                           Context context
        ) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        GenericOptionsParser optionParser = new GenericOptionsParser(conf, args);
        String[] remainingArgs = optionParser.getRemainingArgs();
        if ((remainingArgs.length != 2) && (remainingArgs.length != 4)) {
            System.err.println("Usage: wordcount <in> <out> [-skip skipPatternFile]");
            System.exit(2);
        }
        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(WordCount2.class);
        job.setMapperClass(TokenizerMapper.class);
        job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        List<String> otherArgs = new ArrayList<String>();
        for (int i=0; i < remainingArgs.length; ++i) {
            if ("-skip".equals(remainingArgs[i])) {
                job.addCacheFile(new Path(remainingArgs[++i]).toUri());
                job.getConfiguration().setBoolean("wordcount.skip.patterns", true);
            } else {
                otherArgs.add(remainingArgs[i]);
            }
        }
        FileInputFormat.addInputPath(job, new Path(otherArgs.get(0)));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs.get(1)));

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

GenericOptionsParser是Hadoop 自带了的些辅助类。用来解释常用的Hadoop命令行选项,并根据需要为Configuration对象设置相应的取值。通常不直接使用GenericOptionsParser. 更方便的方式是实现Tool接口,通过ToolRunner来运行应用程序。
 

3.本地测试运行mapreduce

更改运行参数设置,添加输入、输出参数,对应GenericOptionsParser 获取的两个参数

点击 Edit Configurations

然后直接点击运行

 

然后就可以去输出文件查看内容

 

4.提交到集群运行mapreduce

打包好jar报后,上传到集群,然后运行下面命令就可以再集群运行

hadoop jar WordCount-1.0-SNAPSHOT.jar cn/busuanzi/bigdata/wordcount/WordCount2 /cn/busuanzi/big-data/wordcount/input/ /cn/busuanzi/big-data/wordcount/output/2