MapreduceRCFile如何写入和读取API
这篇文章主要介绍Mapreduce RCFile如何写入和读取API，文中介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们一定要看完！
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RCFile是FaceBook开发的高压缩比、高效读的行列存储结构。通常在Hive中可以直接对一张Text表使用insert-select转换，但有时希望使用Mapreduce进行RCFile的读写。
读取文本文件，使用mapreduce生成RCFile格式文件
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hive.serde2.columnar.BytesRefArrayWritable;
import org.apache.hadoop.hive.serde2.columnar.BytesRefWritable;
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.output.FileOutputFormat;
import org.apache.hive.hcatalog.rcfile.RCFileMapReduceInputFormat;
import java.io.IOException;
public class RcFileReaderJob {
static class RcFileMapper extends Mapper
@Override
protected void map(Object key, BytesRefArrayWritable value,
Context context)
throws IOException, InterruptedException {
Text txt = new Text();
StringBuffer sb = new StringBuffer();
for (int i = 0; i < value.size(); i++) {
BytesRefWritable v = value.get(i);
txt.set(v.getData(), v.getStart(), v.getLength());
if (i == value.size() - 1) {
sb.append(txt.toString());
} else {
sb.append(txt.toString() + "\t");
}
}
context.write(new Text(sb.toString()), NullWritable.get());
}
@Override
protected void cleanup(Context context) throws IOException,
InterruptedException {
super.cleanup(context);
}
@Override
protected void setup(Context context) throws IOException,
InterruptedException {
super.setup(context);
}
}
static class RcFileReduce extends Reducer
@Override
protected void reduce(Text key, Iterable
Context context) throws IOException, InterruptedException {
context.write(key, NullWritable.get());
}
}
public static boolean runLoadMapReducue(Configuration conf, Path input, Path output) throws IOException,
ClassNotFoundException, InterruptedException {
Job job = Job.getInstance(conf);
job.setJarByClass(RcFileReaderJob.class);
job.setJobName("RcFileReaderJob");
job.setNumReduceTasks(1);
job.setMapperClass(RcFileMapper.class);
job.setReducerClass(RcFileReduce.class);
job.setInputFormatClass(RCFileMapReduceInputFormat.class);
// MultipleInputs.addInputPath(job, input, RCFileInputFormat.class);
RCFileMapReduceInputFormat.addInputPath(job, input);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(NullWritable.class);
FileOutputFormat.setOutputPath(job, output);
return job.waitForCompletion(true);
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
if (args.length != 2) {
System.err.println("Usage: rcfile
System.exit(2);
}
RcFileReaderJob.runLoadMapReducue(conf, new Path(args[0]), new Path(args[1]));
}
}
读取RCFile格式文件，使用mapreduce生成Text格式文件
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hive.serde2.columnar.BytesRefArrayWritable;
import org.apache.hadoop.hive.serde2.columnar.BytesRefWritable;
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.lib.input.FileInputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.hive.hcatalog.rcfile.RCFileMapReduceOutputFormat;
import java.io.IOException;
public class RcFileWriterJob extends Configured implements Tool{
public static class Map extends Mapper
private byte[] fieldData;
private int numCols;
private BytesRefArrayWritable bytes;
@Override
protected void setup(Context context) throws IOException, InterruptedException {
numCols = context.getConfiguration().getInt("hive.io.rcfile.column.number.conf", 0);
bytes = new BytesRefArrayWritable(numCols);
}
public void map(Object key, Text line, Context context
) throws IOException, InterruptedException {
bytes.clear();
String[] cols = line.toString().split("\t", -1);
System.out.println("SIZE : "+cols.length);
for (int i=0; i
BytesRefWritable cu = new BytesRefWritable(fieldData, 0, fieldData.length);
bytes.set(i, cu);
}
context.write(NullWritable.get(), bytes);
}
}
public int run(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if(otherArgs.length < 2){
System.out.println("Usage: " +
"hadoop jar RCFileLoader.jar
"-tableName
"-output
