你知道哪些在Windows平台下运行的Java分布式框架？

在windows平台下，Java分布式框架是非常常见的。这些框架可以帮助开发人员将应用程序部署到多个服务器上，从而提高应用程序的可伸缩性和可靠性。在这篇文章中，我们将介绍一些在Windows平台下运行的Java分布式框架。

1. Apache hadoop

Apache Hadoop是一个开源的分布式计算框架，可以在Windows平台上运行。它可以处理大数据集并将它们分割成小块进行处理。Hadoop主要由两个组件组成：Hadoop Distributed File System（hdfs）和mapReduce。

下面是一个使用Hadoop进行WordCount的演示代码：

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.output.FileOutputFormat;

public class WordCount {

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

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

        public void map(Object key, Text value, Context context
        ) throws IOException, InterruptedException {
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                word.set(itr.nextToken());
                context.write(word, one);
            }
        }
    }

    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();
        Job job = Job.getInstance(conf, "word count");
        job.setjarByClass(WordCount.class);
        job.setMapperClass(TokenizerMapper.class);
        job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(IntSumReducer.class);
        job.setOutpuTKEyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

2. Apache spark

Apache Spark是一个快速的、用于大规模数据处理的通用计算引擎。它可以在Windows平台上运行，并且支持多种编程语言，包括Java、Scala和python等。Spark主要由三个组件组成：Spark Core、Spark sql和Spark Streaming。

下面是一个使用Spark进行WordCount的演示代码：

import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import scala.Tuple2;

import java.util.Arrays;
import java.util.Iterator;
import java.util.List;

public class WordCount {

    public static void main(String[] args) {
        // 创建SparkContext
        JavaSparkContext sc = new JavaSparkContext("local", "WordCount");

        // 读取文本文件
        JavaRDD<String> lines = sc.textFile(args[0]);

        // 将每一行拆分成单词
        JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
            public Iterator<String> call(String s) {
                return Arrays.asList(s.split(" ")).iterator();
            }
        });

        // 将单词和数量组成键值对
        JavaPairRDD<String, Integer> pairs = words.mapToPair(new PairFunction<String, String, Integer>() {
            public Tuple2<String, Integer> call(String s) {
                return new Tuple2<String, Integer>(s, 1);
            }
        });

        // 计算每个单词出现的次数
        JavaPairRDD<String, Integer> counts = pairs.reduceByKey(new Function2<Integer, Integer, Integer>() {
            public Integer call(Integer a, Integer b) {
                return a + b;
            }
        });

        // 输出结果
        List<Tuple2<String, Integer>> output = counts.collect();
        for (Tuple2<?,?> tuple : output) {
            System.out.println(tuple._1() + ": " + tuple._2());
        }

        // 关闭SparkContext
        sc.stop();
    }
}

3. Apache ZooKeeper

Apache ZooKeeper是一个开源的分布式协调服务，可以在Windows平台上运行。它可以帮助开发人员管理分布式应用程序中的配置信息、命名服务、分布式锁等。

下面是一个使用ZooKeeper进行分布式锁的演示代码：

import org.apache.zookeeper.*;
import org.apache.zookeeper.data.Stat;

import java.io.IOException;
import java.util.Collections;
import java.util.List;
import java.util.concurrent.CountDownLatch;

public class DistributedLock {

    private static final String LOCK_ROOT_PATH = "/locks";
    private static final String LOCK_node_NAME = "lock_";
    private static final int SESSION_TIMEOUT = 3000;

    private ZooKeeper zk;
    private String lockPath;
    private String lockName;
    private CountDownLatch latch;

    public DistributedLock(String connectionString, String lockName) {
        this.lockName = lockName;
        this.latch = new CountDownLatch(1);
        try {
            this.zk = new ZooKeeper(connectionString, SESSION_TIMEOUT, new Watcher() {
                public void process(WatchedEvent event) {
                    if (event.getState() == Event.KeeperState.SyncConnected) {
                        latch.countDown();
                    }
                }
            });
            latch.await();
        } catch (IOException e) {
            e.printStackTrace();
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    }

    public boolean lock() {
        try {
            // 创建锁的根节点
            if (zk.exists(LOCK_ROOT_PATH, false) == null) {
                zk.create(LOCK_ROOT_PATH, null, ZooDefs.Ids.OPEN_ACL_UNSAFE, CreateMode.PERSISTENT);
            }

            // 创建锁的子节点
            lockPath = zk.create(LOCK_ROOT_PATH + "/" + LOCK_NODE_NAME, null, ZooDefs.Ids.OPEN_ACL_UNSAFE, CreateMode.EPHEMERAL_SEQUENTIAL);

            // 获取所有锁的子节点
            List<String> children = zk.getChildren(LOCK_ROOT_PATH, false);

            // 排序所有锁的子节点，获取当前锁的序号
            Collections.sort(children);
            int index = children.indexOf(lockPath.substring(LOCK_ROOT_PATH.length() + 1));

            // 如果当前锁是第一个锁，则获取锁成功
            if (index == 0) {
                return true;
            } else {
                // 否则，获取前一个锁的序号，并对它进行监听
                String prevLockPath = LOCK_ROOT_PATH + "/" + children.get(index - 1);
                Stat stat = zk.exists(prevLockPath, new Watcher() {
                    public void process(WatchedEvent event) {
                        synchronized (this) {
                            notifyAll();
                        }
                    }
                });

                // 等待前一个锁的释放
                if (stat != null) {
                    synchronized (this) {
                        wait();
                    }
                }

                return true;
            }
        } catch (Exception e) {
            e.printStackTrace();
        }

        return false;
    }

    public void unlock() {
        try {
            zk.delete(lockPath, -1);
            zk.close();
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

以上是在Windows平台下运行的一些Java分布式框架，它们都可以帮助开发人员更好地管理分布式应用程序，并提高应用程序的性能和可靠性。