开源的分布式框架有哪些

**开源的分布式框架有哪些**

作为一名经验丰富的开发者，我将为您介绍一些开源的分布式框架以及如何使用它们。在本文中，我将重点介绍Apache Hadoop和Apache Spark这两个流行的分布式计算框架。

### 步骤及代码示例：

| 步骤 | 描述 | 代码示例 |
| ------ | ------- | -------- |
| 步骤一 | 下载并安装Apache Hadoop |
| 步骤二 | 配置Hadoop集群 |
| 步骤三 | 编写一个简单的MapReduce程序 | ```java
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{

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 {

private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable 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);
}
}
``` |
| 步骤四 | 运行MapReduce程序 | ```bash
hadoop jar WordCount.jar WordCount input output
``` |
| 步骤五 | 下载并安装Apache Spark |
| 步骤六 | 编写一个简单的Spark应用程序 | ```scala
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext

object WordCount {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("WordCount")
val sc = new SparkContext(conf)

val textFile = sc.textFile("hdfs://input/")
val counts = textFile.flatMap(line => line.split(" "))
.map(word => (word, 1))
.reduceByKey(_ + _)

counts.saveAsTextFile("hdfs://output/")
}
}
``` |
| 步骤七 | 运行Spark程序 | ```bash
spark-submit --class WordCount WordCount.jar
``` |

以上是简单介绍了一下使用Apache Hadoop和Apache Spark这两个开源的分布式计算框架的流程和示例代码。希望您能通过这些示例更好地了解分布式计算框架的使用。如有任何疑问，欢迎随时向我提问！