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
avro使用详解-编程知识网

avro使用详解-编程知识网

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。