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<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。
