Hive ORC数据格式的MapReduce读写

1，mr代码如下

package com.test.hadoop;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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 org.apache.orc.TypeDescription;
import org.apache.orc.mapred.OrcStruct;
import org.apache.orc.mapreduce.OrcInputFormat;
import org.apache.orc.mapreduce.OrcOutputFormat;

public class ORCSample {

public static class ORCMapper extends
Mapper<NullWritable, OrcStruct, Text, Text> {
public void map(NullWritable key, OrcStruct value, Context output)
throws IOException, InterruptedException {
output.write((Text) value.getFieldValue(1),
(Text) value.getFieldValue(2));
}
}

public static class ORCReducer extends
Reducer<Text, Text, NullWritable, OrcStruct> {
private TypeDescription schema = TypeDescription
.fromString("struct<name:string,mobile:string>");
private OrcStruct pair = (OrcStruct) OrcStruct.createValue(schema);

private final NullWritable nw = NullWritable.get();

public void reduce(Text key, Iterable<Text> values, Context output)
throws IOException, InterruptedException {
for (Text val : values) {
pair.setFieldValue(0, key);
pair.setFieldValue(1, val);
output.write(nw, pair);
}
}
}

public static void main(String args[]) throws Exception {

Configuration conf = new Configuration();
conf.set("orc.mapred.output.schema","struct<name:string,mobile:string>");
Job job = Job.getInstance(conf, "ORC Test");
job.setJarByClass(ORCSample.class);
job.setMapperClass(ORCMapper.class);
job.setReducerClass(ORCReducer.class);
job.setInputFormatClass(OrcInputFormat.class);
job.setOutputFormatClass(OrcOutputFormat.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
job.setOutputKeyClass(NullWritable.class);
job.setOutputValueClass(OrcStruct.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

2，pom.xml中添加依赖（基于hadoop2.7.1）

<dependencies>
<dependency>
<groupId>org.apache.orc</groupId>
<artifactId>orc-mapreduce</artifactId>
<version>1.1.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.7.1</version>
</dependency>
</dependencies>

3，创建表，在 t_test_orc中添加3行数据。

CREATE  TABLE `t_test_orc`(
`siteid` string,
`name` string,
`mobile` string)
stored as orc

CREATE TABLE `t_test_orc_new`(
`name` string,
`mobile` string)
ROW FORMAT SERDE
'org.apache.hadoop.hive.ql.io.orc.OrcSerde'
STORED AS INPUTFORMAT
'org.apache.hadoop.hive.ql.io.orc.OrcInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat'
LOCATION
'hdfs://namenode:9000/user/testorc3'

4，打包运行

hadoop jar MRTest-1.0-jar-with-dependencies.jar com.test.hadoop.ORCSample /hive/warehouse/mytest.db/t_test_orc /user/testorc3

5，完成后可以用hive --orcfiledump -d 查看执行结果



并且进入hive 查询orc格式的 t_test_orc表也可以看到数据
更多信息可以参考https://orc.apache.org/