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java读写parquet文件 java 读写Parquet格式的数据的代码实例

Nucky_yang 人气:0
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本文介绍了java 读写Parquet格式的数据,分享给大家,具体如下:

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)+"\t"+
        line.getString(1, 0)+"\t"+
        time.getInteger(0, 0)+"\t"+
        time.getString(1, 0)+"\t");*/
        System.out.println(line.getString("city", 0)+"\t"+
        line.getString("ip", 0)+"\t"+
        time.getInteger("ttl", 0)+"\t"+
        time.getString("ttl2", 0)+"\t");
        //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 {\n" +
        " required binary city (UTF8);\n" +
        " required binary ip (UTF8);\n" +
        " repeated group time {\n"+
          " required int32 ttl;\n"+
           " required binary ttl2;\n"+
        "}\n"+
       "}");
    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 {\n" +
        " required binary city (UTF8);\n" +
        " required binary ip (UTF8);\n" +
        "repeated group time {\n"+
        "required int32 ttl;\n"+
        "required binary ttl2;\n"+
        "}\n"+
        "}");*/
    
//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:

maven依赖(我用的1.7)

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

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