一、avro的介绍

1、概括

avro是一个数据序列化系统，它提供

1. 丰富的数据结构
2. 快速可压缩的二进制数据形式
3. 存储持久数据的文件容器
4. 远程过程调用RPC
5. 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<User> userDatumWriter = new SpecificDatumWriter<User>(User.class);
DataFileWriter<User> dataFileWriter = new DataFileWriter<User>(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<User> userDatumReader = new SpecificDatumReader<User>(User.class);
DataFileReader<User> dataFileReader = new DataFileReader<User>(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

<plugin>
  <groupId>org.apache.avro</groupId>
  <artifactId>avro-maven-plugin</artifactId>
  <version>1.8.2</version>
  <executions>
    <execution>
      <phase>generate-sources</phase>
      <goals>
        <goal>schema</goal>
      </goals>
      <configuration>
        <sourceDirectory>${project.basedir}/../</sourceDirectory>
        <outputDirectory>${project.basedir}/target/generated-sources/</outputDirectory>
      </configuration>
    </execution>
  </executions>
</plugin>

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<User> userDatumWriter = new SpecificDatumWriter<User>(User.class);
DataFileWriter<User> dataFileWriter = new DataFileWriter<User>(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<User> userDatumReader = new SpecificDatumReader<User>(User.class);
DataFileReader<User> dataFileReader = new DataFileReader<User>(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<AvroKey<User>, NullWritable, Text, IntWritable> {

    @Override
    public void map(AvroKey<User> 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<Text, IntWritable, AvroKey<CharSequence>, AvroValue<Integer>> {

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

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

  public int run(String[] args) throws Exception {
    if (args.length != 2) {
      System.err.println("Usage: MapReduceColorCount <input path> <output path>");
      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。