avro使用详解

一、avro的介绍
1、概括
avro是一个数据序列化系统，它提供

丰富的数据结构
快速可压缩的二进制数据形式
存储持久数据的文件容器
远程过程调用RPC
简单的动态语言结合功能
2、类型
 

二、avro在hadoop的使用
1、模式确定
例如：{"namespace": "example.avro",
        "type": "record",
        "name": "User",
        "fields": [
        {"name": "name", "type": "string"},
        {"name": "favorite_number",  "type": ["int", "null"]},
        {"name": "favorite_color", "type": ["string", "null"]}
        ]
    }

其中namespace是包名，name是类名

2、text数据作为输入
2.1 无插件的序列化
//创建数据记录
Schema schema = new Schema.Parser().parse(new File("user.avsc"));
GenericRecord user1 = new GenericData.Record(schema);
user1.put("name", "Alyssa");
user1.put("favorite_number", 256);
// Leave favorite color null

GenericRecord user2 = new GenericData.Record(schema);
user2.put("name", "Ben");
user2.put("favorite_number", 7);
user2.put("favorite_color", "red");

//序列化
// Serialize user1, user2 and user3 to disk
DatumWriter userDatumWriter = new SpecificDatumWriter(User.class);
DataFileWriter dataFileWriter = new DataFileWriter(userDatumWriter);
dataFileWriter.create(user1.getSchema(), new File("users.avro"));
dataFileWriter.append(user1);
dataFileWriter.append(user2);
dataFileWriter.append(user3);
dataFileWriter.close();

//反序列化
// Deserialize Users from disk
DatumReader userDatumReader = new SpecificDatumReader(User.class);
DataFileReader dataFileReader = new DataFileReader(file, userDatumReader);
User user = null;
while (dataFileReader.hasNext()) {
    // Reuse user object by passing it to next(). This  saves us from
    // allocating and garbage collecting many objects for   files with
    // many items.
    user = dataFileReader.next(user);
    System.out.println(user);
}

2.2有插件的序列化
2.2.1 插件导入

  org.apache.avro
  avro-maven-plugin
  1.8.2
 
   
      generate-sources
     
        schema
     
     
        ${project.basedir}/../
        ${project.basedir}/target/generated-sources/
     
   
 


2.2.2 编译schema文件
注意schema文件放在指定的文件中 
 
在idea中编译此文件，使之在目录中生成class文件

2.2.3 常规使用
DatumWriter userDatumWriter = new SpecificDatumWriter(User.class);
DataFileWriter dataFileWriter = new DataFileWriter(userDatumWriter);
dataFileWriter.create(user1.getSchema(), new File("users.avro"));
dataFileWriter.append(user1);
dataFileWriter.append(user2);
dataFileWriter.append(user3);
dataFileWriter.close();

//序列化
// Deserialize Users from disk
DatumReader userDatumReader = new SpecificDatumReader(User.class);
DataFileReader dataFileReader = new DataFileReader(file, userDatumReader);
User user = null;
while (dataFileReader.hasNext()) {
    // Reuse user object by passing it to next(). This saves us from
    // allocating and garbage collecting many objects for files with
    // many items.
    user = dataFileReader.next(user);
    System.out.println(user);
}

3、例子（使用的是有插件的方式）
MapReduceColorCount:

package example;

import java.io.IOException;

import org.apache.avro.Schema;
import org.apache.avro.mapred.AvroKey;
import org.apache.avro.mapred.AvroValue;
import org.apache.avro.mapreduce.AvroJob;
import org.apache.avro.mapreduce.AvroKeyInputFormat;
import org.apache.avro.mapreduce.AvroKeyValueOutputFormat;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

import example.avro.User;

public class MapReduceColorCount extends Configured implements Tool {

  public static class ColorCountMapper extends
  Mapper, NullWritable, Text, IntWritable> {

    @Override
    public void map(AvroKey key, NullWritable value, Context context)
        throws IOException, InterruptedException {

      CharSequence color = key.datum().getFavoriteColor();
      if (color == null) {
        color = "none";
      }
      context.write(new Text(color.toString()), new IntWritable(1));
      }
    }

    public static class ColorCountReducer extends
  Reducer, AvroValue> {

    @Override
    public void reduce(Text key, Iterable values,
        Context context) throws IOException, InterruptedException {

      int sum = 0;
      for (IntWritable value : values) {
        sum += value.get();
      }
      context.write(new AvroKey(key.toString()), new AvroValue(sum));
    }
    }

  public int run(String[] args) throws Exception {
    if (args.length != 2) {
      System.err.println("Usage: MapReduceColorCount

");
      return -1;
    }

    Job job = new Job(getConf());
    job.setJarByClass(MapReduceColorCount.class);
    job.setJobName("Color Count");

    FileInputFormat.setInputPaths(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));

    job.setInputFormatClass(AvroKeyInputFormat.class);
    job.setMapperClass(ColorCountMapper.class);
    AvroJob.setInputKeySchema(job, User.getClassSchema());
    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(IntWritable.class);

    job.setOutputFormatClass(AvroKeyValueOutputFormat.class);
    job.setReducerClass(ColorCountReducer.class);
    AvroJob.setOutputKeySchema(job, Schema.create(Schema.Type.STRING));
    AvroJob.setOutputValueSchema(job, Schema.create(Schema.Type.INT));

    return (job.waitForCompletion(true) ? 0 : 1);
  }

  public static void main(String[] args) throws Exception {
    int res = ToolRunner.run(new MapReduceColorCount(), args);
    System.exit(res);
  }
}

注意：当采用不用插件的方式时，map的代码如下 
@Override 
public void map(AvroKey key, NullWritable value, Context context)throws IOException,InterruptedException {} 
由于代码并不知道AvroKey的schema，所以要在main中使用AvroJob.setDataModelClass(job,GenericData.class);指定数据的schema。