这是我的地图
public static class MapClass extends Mapper<LongWritable, Text, Text, Text> {
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException{
String[] fields = value.toString().split(",", -20);
String country = fields[4];
String numClaims = fields[8];
if (numClaims.length() > 0 && !numClaims.startsWith("\"")) {
context.write(new Text(country), new Text(numClaims + ",1"));
}
}
}
这是我的减少
public void reduce(Text key, Iterator<Text> values, Context context) throws IOException, InterruptedException {
double sum = 0.0;
int count = 0;
while (values.hasNext()) {
String[] fields = values.next().toString().split(",");
sum += Double.parseDouble(fields[0]);
count += Integer.parseInt(fields[1]);
}
context.write(new Text(key), new DoubleWritable(sum/count));
}
以下是它的配置方式
Job job = new Job(getConf());
job.setJarByClass(AverageByAttributeUsingCombiner.class);
job.setJobName("AverageByAttributeUsingCombiner");
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setMapperClass(MapClass.class);
// job.setCombinerClass(Combinber.class);
job.setReducerClass(Reduce.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// job.setNumReduceTasks(0); // to not run the reducer
boolean success = job.waitForCompletion(true);
return success ? 0 : 1;
输入是形式的
"PATENT","GYEAR","GDATE","APPYEAR","COUNTRY","POSTATE","ASSIGNEE","ASSCODE","CLAIMS","NCLASS","CAT","SUBCAT","CMADE","CRECEIVE","RATIOCIT","GENERAL","ORIGINAL","FWDAPLAG","BCKGTLAG","SELFCTUB","SELFCTLB","SECDUPBD│
","SECDLWBD" │
3070801,1963,1096,,"BE","",,1,,269,6,69,,1,,0,,,,,,, │
3070802,1963,1096,,"US","TX",,1,,2,6,63,,0,,,,,,,,, │
3070803,1963,1096,,"US","IL",,1,,2,6,63,,9,,0.3704,,,,,,, │
3070804,1963,1096,,"US","OH",,1,,2,6,63,,3,,0.6667,,,,,,,
整个地图缩小的输出看起来像
“AR”5,1│
“AR”9,1│
“AR”2,1│
“AR”15,1│
“AR”13,1│
“AR”1,1│
“AR”34,1│
“AR”12,1│
“AR”8,1│
“AR”7,1│
“AR”23,1│
“AR”3,1│
“AR”4,1│
“AR”4,1
如何调试和修复此问题？我正在学习hadoop
解决方法:
如前所述,问题在于您没有覆盖默认抽象Reducer类的默认reduce方法.
更具体地说,到目前为止(one / the)问题是你的reduce方法签名是：
public void reduce(Text key, **Iterator**<Text> values, Context context)
throws IOException, InterruptedException
相反,它应该是：
public void reduce(Text key, **Iterable**<Text> values, Context context)
throws IOException, InterruptedException
旧的API版本是正确的,您实现Reducer接口reduce()方法,它的工作原理.
对这种情况的一个很好的验证是使用@Override,因为它强制编译签名不匹配的时间检查.
原文地址：https://codeday.me/bug/20190826/1727600.html
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