统计HDFS中文件记录数的代码可以使用Hadoop的MapReduce框架来实现。下面是一个示例代码:
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
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;
import java.io.IOException;
public class HDFSRecordCount {
public static class RecordCountMapper extends Mapper<LongWritable, Text, NullWritable, NullWritable> {
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
// 每读取一行记录,输出一个键值对
context.write(NullWritable.get(), NullWritable.get());
}
}
public static class RecordCountReducer extends Reducer<NullWritable, NullWritable, NullWritable, LongWritable> {
@Override
protected void reduce(NullWritable key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
long count = 0;
// 统计记录数
for (NullWritable value : values) {
count++;
}
// 输出记录数
context.write(NullWritable.get(), new LongWritable(count));
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "HDFS Record Count");
job.setJarByClass(HDFSRecordCount.class);
job.setMapperClass(RecordCountMapper.class);
job.setReducerClass(RecordCountReducer.class);
job.setOutputKeyClass(NullWritable.class);
job.setOutputValueClass(NullWritable.class);
FileInputFormat.addInputPath(job, new Path("hdfs://your-hdfs-path"));
FileOutputFormat.setOutputPath(job, new Path("hdfs://your-output-path"));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
这段代码使用了Hadoop的MapReduce框架来实现统计HDFS中文件的记录数。其中,Mapper类将每一行记录映射为一个键值对,Reducer类对所有键值对进行汇总并输出记录数。你需要将"hdfs://your-hdfs-path"替换为实际的HDFS文件路径,将"hdfs://your-output-path"替换为输出结果的HDFS路径。
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