个人认为在MapReduce中使用Avro可以提升数据的处理性能，主要是以下几点：

向Job提供数据文件时可以使用Avro序列化过的二进制数据文件

在数据解析方面速度比较快

排序功能

Avro官网也提供了一个ColorCount这样的一个案例，演示使用Avro序列化过的二进制数据文件作为MapReduce的Job的输入数据，并且完成计算之后，输出结果也是Avro序列化后的数据文件，下面是这个案例源码及相关步骤：

1. 项目的pom文件：

org.apache.avro

avro

1.7.7

org.apache.avro

avro-mapred

1.7.7

hadoop2 //这是maven的分类器，用来进一步来确定jar包的类别的

org.apache.hadoop

hadoop-client

2.9.2

junit

junit

4.12

test

org.apache.maven.plugins

maven-jar-plugin

com.zpark.demo.avro.mapreduce.MapReduceColorCount

org.apache.maven.plugins

maven-compiler-plugin

1.8

1.8

注意事项：

在引入依赖 avro-mapred 时一定要设置分类器属性

hadoop2，并且在使用不熟悉的依赖时，一定看下它的pom文件内容，看里面是不是定义了分类器。

2. Mapper和Reducer代码

public class MapReduceColorCount extends Configured implementsTool {public static class ColorCountMapper extendsMapper, NullWritable, Text, IntWritable>{

@Overridepublic void map(AvroKeykey, NullWritable value, Context context)throwsIOException, InterruptedException {

String color= (String)key.datum().get("favorite_color");if (color == null) {

color= "none";

}

context.write(new Text(color), new IntWritable(1));

}

}public static class ColorCountReducer extendsReducer, AvroValue>{

@Overridepublic void reduce(Text key, Iterablevalues,

Context context)throwsIOException, 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) throwsException {if (args.length != 2) {

System.err.println("Usage: MapReduceColorCount ");return -1;

}//Job job = new Job(getConf());

Job job= Job.getInstance(getConf(), "word count");

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, ColorCountSchema.schema);

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) throwsException {int res = ToolRunner.run(newMapReduceColorCount(), args);

System.exit(res);

}

}

3. Avro Schema

通过下面这个类来产成Avro序列时的Schema对象

public classColorCountSchema {public static Schema schema = newSchema.Parser().parse(newStringBuilder()

.append("{\"namespace\": \"com.zpark.demo.avro.mapreduce\",")

.append("\"type\": \"record\",")

.append("\"name\": \"User\",")

.append("\"fields\": [")

.append("{\"name\": \"name\", \"type\": \"string\"},")

.append("{\"name\": \"favorite_number\", \"type\": [\"int\", \"null\"]},")

.append("{\"name\": \"favorite_color\", \"type\": [\"string\", \"null\"]}")

.append("]")

.append("}").toString()

);

}

下面是对应的user.avsc文件的内容

{"namespace": "com.zpark.demo.avro.mapreduce","type": "record","name": "User","fields": [

{"name": "name", "type": "string"},

{"name": "favorite_number", "type": ["int", "null"]},

{"name": "favorite_color", "type": ["string", "null"]}

]

}

4. 打包上传Jar包到Hadoop环境下运行

一定要注意，同时需要把依赖jar包avro-mapred-1.7.7-hadoop2.jar上传到 $HADOOP_HOME/share/hadoop/mapreduce目录下，并且一定上传分类器版本对应haddop2的jar，否则会报下面的错：

5.查看计算结果执行后会输出part-r-00000.avro这样的计算结果文件，可以通过 java -jar avro-tools-1.9.1.jar tojson part-r-00000.avro来查看，此外通过java -jar avro-tools-1.9.1.jar help可以查看avro-tools的详细命令列表

Version 1.9.1of Apache Avro

Copyright2010-2015The Apache Software Foundation

This product includes software developed at

The Apache Software Foundation (https://www.apache.org/).

----------------Available tools:

canonical Converts an Avro Schema to its canonical form

cat Extracts samplesfromfiles

compile Generates Java codeforthe given schema.

concat Concatenates avro files without re-compressing.

fingerprint Returns the fingerprintforthe schemas.

fragtojson Renders a binary-encoded Avro datum asJSON.

fromjson Reads JSON records and writes an Avro data file.

fromtext Imports a text file into an avro data file.

getmeta Printsoutthe metadata of an Avro data file.

getschema Printsoutschema of an Avro data file.

idl Generates a JSON schemafroman Avro IDL file

idl2schemata Extract JSON schemata of the typesfroman Avro IDL file

induce Induce schema/protocol from Java class/interfacevia reflection.

jsontofrag Renders a JSON-encoded Avro datum asbinary.

random Creates a file with randomly generated instances of a schema.

recodec Alters the codec of a data file.

repair Recovers datafroma corrupt Avro Data file

rpcprotocol Output the protocol of a RPC service

rpcreceive Opens an RPC Server and listensforone message.

rpcsend Sends a single RPC message.

tether Run a tethered mapreduce job.

tojson Dumps an Avro data fileasJSON, record per line or pretty.

totext Converts an Avro data file to a text file.

totrevni Converts an Avro data file to a Trevni file.

trevni_meta Dumps a Trevni file's metadata as JSON.

trevni_random Create a Trevni file filled with random instances of a schema.

trevni_tojson Dumps a Trevni fileas JSON.