java 读写Parquet格式的数据 Parquet example

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.IOException;
import java.util.Random;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.log4j.Logger;
import org.apache.parquet.example.data.Group;
import org.apache.parquet.example.data.GroupFactory;
import org.apache.parquet.example.data.simple.SimpleGroupFactory;
import org.apache.parquet.hadoop.ParquetReader;
import org.apache.parquet.hadoop.ParquetReader.Builder;
import org.apache.parquet.hadoop.ParquetWriter;
import org.apache.parquet.hadoop.example.GroupReadSupport;
import org.apache.parquet.hadoop.example.GroupWriteSupport;
import org.apache.parquet.schema.MessageType;
import org.apache.parquet.schema.MessageTypeParser;

public class ReadParquet {
    static Logger logger=Logger.getLogger(ReadParquet.class);
    public static void main(String[] args) throws Exception {
        
//        parquetWriter("test\parquet-out2","input.txt");
        parquetReaderV2("test\parquet-out2");
    }
    
    
    static void parquetReaderV2(String inPath) throws Exception{
        GroupReadSupport readSupport = new GroupReadSupport();
        Builder<Group> reader= ParquetReader.builder(readSupport, new Path(inPath));
        ParquetReader<Group> build=reader.build();
        Group line=null;
        while((line=build.read())!=null){
　　　　　　Group time= line.getGroup("time", 0);
　　　　　　　　//通过下标和字段名称都可以获取

　　　　　　　　/*System.out.println(line.getString(0, 0)+" "+
　　　　　　　　line.getString(1, 0)+" "+
　　　　　　　　time.getInteger(0, 0)+" "+
　　　　　　　　time.getString(1, 0)+" ");*/

　　　　　　　　System.out.println(line.getString("city", 0)+" "+
　　　　　　　　line.getString("ip", 0)+" "+
　　　　　　　　time.getInteger("ttl", 0)+" "+
　　　　　　　　time.getString("ttl2", 0)+" ");

　　　　　　　　//System.out.println(line.toString());

        }
        System.out.println("读取结束");
    } 
    //新版本中new ParquetReader()所有构造方法好像都弃用了,用上面的builder去构造对象
    static void parquetReader(String inPath) throws Exception{
        GroupReadSupport readSupport = new GroupReadSupport();
        ParquetReader<Group> reader = new ParquetReader<Group>(new Path(inPath),readSupport);
        Group line=null;
        while((line=reader.read())!=null){
          System.out.println(line.toString());
        }

        System.out.println("读取结束");
        
    }
    /**
     * 
     * @param outPath　　输出Parquet格式
     * @param inPath  输入普通文本文件
     * @throws IOException
     */
    static void parquetWriter(String outPath,String inPath) throws IOException{
        MessageType schema = MessageTypeParser.parseMessageType("message Pair {
" +
                " required binary city (UTF8);
" +
                " required binary ip (UTF8);
" +
                " repeated group time {
"+
                　　" required int32 ttl;
"+
               　　 " required binary ttl2;
"+
                "}
"+
              "}");
        GroupFactory factory = new SimpleGroupFactory(schema);
        Path path = new Path(outPath);
       Configuration configuration = new Configuration();
       GroupWriteSupport writeSupport = new GroupWriteSupport();
       writeSupport.setSchema(schema,configuration);
       ParquetWriter<Group> writer = new ParquetWriter<Group>(path,configuration,writeSupport);
　　　　//把本地文件读取进去，用来生成parquet格式文件
       BufferedReader br =new BufferedReader(new FileReader(new File(inPath)));
       String line="";
       Random r=new Random();
       while((line=br.readLine())!=null){
           String[] strs=line.split("\s+");
           if(strs.length==2) {
               Group group = factory.newGroup()
                       .append("city",strs[0])
                       .append("ip",strs[1]);
               Group tmpG =group.addGroup("time");
               tmpG.append("ttl", r.nextInt(9)+1);
               tmpG.append("ttl2", r.nextInt(9)+"_a");
               writer.write(group);
           }
       }
       System.out.println("write end");
       writer.close();
    }
}

说下schema（写Parquet格式数据需要schema，读取的话"自动识别"了schema）

/*
 * 每一个字段有三个属性：重复数、数据类型和字段名，重复数可以是以下三种：
 *         required(出现1次)
 *         repeated(出现0次或多次) 
 *         optional(出现0次或1次)
 * 每一个字段的数据类型可以分成两种：
 *         group(复杂类型)
 *         primitive(基本类型)
 * 数据类型有
 * INT64, INT32, BOOLEAN, BINARY, FLOAT, DOUBLE, INT96, FIXED_LEN_BYTE_ARRAY
 */
这个repeated和required 不光是次数上的区别，序列化后生成的数据类型也不同，
比如repeqted修饰 ttl2 打印出来为 WrappedArray([7,7_a]) 
而 required修饰 ttl2 打印出来为 [7,7_a]　　

除了用MessageTypeParser.parseMessageType类生成MessageType 还可以用下面方法
（注意这里有个坑--spark里会有这个问题--ttl2这里 as(OriginalType.UTF8) 和 required binary city (UTF8)作用一样，加上UTF8，在读取的时候可以转为StringType，不加的话会报错 [B cannot be cast to java.lang.String  ）

/*MessageType schema = MessageTypeParser.parseMessageType("message Pair {
" +
                " required binary city (UTF8);
" +
                " required binary ip (UTF8);
" +
                "repeated group time {
"+
                "required int32 ttl;
"+
                "required binary ttl2;
"+
                "}
"+
                "}");*/
        
//import org.apache.parquet.schema.Types;
MessageType schema = Types.buildMessage() 
           .required(PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named("city") 
           .required(PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named("ip") 
           .repeatedGroup().required(PrimitiveTypeName.INT32).named("ttl")
                            .required(PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named("ttl2")
                            .named("time")
          .named("Pair"); 

解决 [B cannot be cast to java.lang.String 异常：
1.要么生成parquet文件的时候加个UTF8
2.要么读取的时候再提供一个同样的schema类指定该字段类型，比如下面:

hadoop Mapreducer读写 Parquetexample
http://www.cnblogs.com/yanghaolie/p/7389543.html

maven依赖（我用的1.7）

<dependency>
    <groupId>org.apache.parquet</groupId>
    <artifactId>parquet-hadoop</artifactId>
    <version>1.7.0</version>
</dependency>