MapReduce基础开发之十读写ORC File

1、ORC File

Orc是Hive特有的一种列式存储的文件格式，它有着非常高的压缩比和读取效率，因此很快取代了之前的RCFile，成为Hive中非常常用的一种文件格式。

2、编译ORC Jar包

    http://orc.apache.org/ 下载源代码orc-1.2.1.tar.gz编译jar包
   用ubuntu14编译，安装jdk1.8、cmake3.2.2、Maven3.0.5。
  解压orc-1.2.1.tar.gz，进入目录orc-1.2.1/java下，执行mvn package编译生成jar文件。
  编译完成后进入
orc-1.2.1/java/mapreduce/target获取orc-mapreduce-1.2.1.jar
orc-1.2.1/java/core/target获取orc-core-1.2.1.jar
orc-1.2.1/java/tools/target获取orc-tools-1.2.1.jar
orc-1.2.1/java/tools/target获取orc-tools-1.2.1-uber.jar
orc-1.2.1/java/ storage-api /target获取hive-storage-api-2.1.1-pre-orc.jar
四个jar包导入到MapReduce工程lib目录下。
注意提交到hadoop集群时，第三方独立jar要一并打包。

 

3、MR读写ORC File基础代码

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.mapred.OrcStruct; import org.apache.orc.mapreduce.OrcInputFormat; import org.apache.orc.mapreduce.OrcOutputFormat; import org.apache.orc.TypeDescription;public class ORCSample {public static class ORCMapper extends Mapper<NullWritable, OrcStruct, Text, Text> {private Text oKey=new Text();private Text oValue=new Text();public void map(NullWritable key, OrcStruct value, Context context) throws IOException, InterruptedException {//要知道OrcStruct存储的结构StringBuffer bf = new StringBuffer(); if(value.getNumFields()==3){Text valAcount=(Text)value.getFieldValue(0);Text valDomain=(Text)value.getFieldValue(1);Text valPost=(Text)value.getFieldValue(2);bf.append(valAcount.toString()).append("|").append(valDomain.toString()).append("|").append(valPost.toString());}if (bf.length() > 0) oValue.set(bf.toString());else oValue.set("");oKey.set("");context.write(oKey,oValue);}}/*public static class ORCReducer extends Reducer<Text, Text, Text, Text> {public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {for(Text value:values){String strVal=value.toString();Text okey=new Text();okey.set(strVal);context.write(okey,null);}}}*/public static class ORCReducer extends Reducer<Text, Text, NullWritable, OrcStruct> {//具体OrcStruct字段对应hadoop的定义参考https://orc.apache.org/docs/mapreduce.htmlprivate TypeDescription schema = TypeDescription.fromString("struct<account:string,domain:string,post:string>");private OrcStruct orcs = (OrcStruct) OrcStruct.createValue(schema);private final NullWritable nw = NullWritable.get();public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {for (Text val : values) {if(val.toString() ==null) continue;String[] strVals=val.toString().split("\\|");if(strVals.length==3){Text txtAccount=new Text();txtAccount.set(strVals[0]);orcs.setFieldValue(0, txtAccount);Text txtDomain=new Text();txtAccount.set(strVals[1]);orcs.setFieldValue(1, txtDomain);Text txtPost=new Text();txtAccount.set(strVals[2]);orcs.setFieldValue(2, txtPost);context.write(nw, orcs);} }}}public static void main(String args[]) throws Exception {Configuration conf = new Configuration();//要设置结构，否则reduce会提示输入空值conf.set("orc.mapred.output.schema","struct<account:string,domain:string,post:string>");Job job = new Job(conf, "ORCSample"); job.setJarByClass(ORCSample.class);job.setMapperClass(ORCMapper.class);job.setReducerClass(ORCReducer.class);//map类型设置job.setInputFormatClass(OrcInputFormat.class);job.setMapOutputKeyClass(Text.class);//优先于setOutputKeyClass生效于mapjob.setMapOutputValueClass(Text.class);//reduce类型设置 job.setNumReduceTasks(1);job.setOutputFormatClass(OrcOutputFormat.class);job.setOutputKeyClass(NullWritable.class);job.setOutputValueClass(OrcStruct.class);//job.setOutputKeyClass(Text.class);//对map和reduce输出都生效//job.setOutputValueClass(Text.class);//输入输出路径FileInputFormat.addInputPath(job, new Path(args[0]));FileOutputFormat.setOutputPath(job, new Path(args[1]));System.exit(job.waitForCompletion(true) ? 0 : 1);} }

    

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