基于hadoop源码开发环境搭建
在开发hadoop的MR,以及研究hadoop源码,都需要将hadoop源码java部署到开发工具中,
例如常用的eclipse,具体做法如下:
第一步:在Eclipse新建一个Java项目
第二步:将Hadoop程序src下core, hdfs, mapred, tools几个目录copy到上述新建项目的src目录
第三步:修改将Java Build Path,删除src,添加src/core, src/hdfs....几个源码目录
第四步:为Java Build Path添加项目依赖jar,可以导入Hadoop程序的lib下所有jar包(别漏掉其子目录jar包),导入ant程序lib下所有jar包。
第五步:理论上第四步就OK了,但是可能会报大量如下错误:
Access restriction: The method arrayBaseOffset(Class) from the type Unsafe is not accessible due to restriction on required library C:\Program Files\JDK\jre\lib\rt.jar xxx.java xxxx line 141 Java Problem
解决办法是:右键项目“propertiyes” > "Java Build Path" > "Libraries",展开"JRE System Library",双击"Access rules",点击"Add"按钮,在"Resolution"下拉框选择"Accessible","Rule Pattern"填写"**/*",保存后就OK了。
测试wordcount实例:
reduce:
package test; import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class MyReduce extends Reducer<Text, IntWritable, Text, IntWritable> { public 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)); } }
map:
package test; import java.io.IOException; import java.util.StringTokenizer; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; public class MyMap extends Mapper<Object, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); private Text word; public void map(Object key, Text value, Context context)throws IOException, InterruptedException { String line = value.toString(); StringTokenizer tokenizer = new StringTokenizer(line); while (tokenizer.hasMoreTokens()) { word = new Text(); word.set(tokenizer.nextToken()); context.write(word, one); } } }
driver:
package test; 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.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; public class MyDriver { public static void main(String[] args) throws Exception, InterruptedException { Configuration conf = new Configuration(); conf.set("dfs.permissions", "flase"); Job job = new Job(conf, "Hello Hadoop"); job.setJarByClass(MyDriver.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); job.setMapperClass(MyMap.class); job.setCombinerClass(MyReduce.class); job.setReducerClass(MyReduce.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); // JobClient.runJob(conf); job.waitForCompletion(true); } }
配置响应的运行参数,运行即可。