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。
- 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);
}
}- 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();
}
}- 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。
