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Hadoop兼容性测试版

译者：flink.sojb.cn

Flink与Apache Hadoop MapReduce接口兼容，因此允许重用为Hadoop MapReduce实现的代码。

您可以：

本文档展示了如何在Flink中使用现有的Hadoop MapReduce代码。有关从Hadoop支持的文件系统中读取的信息，请参阅“ 连接到其他系统”指南。

项目配置

Hadoop的输入/输出格式的支持是的一部分flink-java和 flink-scala写入Flink作业时总是需要的Maven模块。的代码位于org.apache.flink.api.java.hadoop和 org.apache.flink.api.scala.hadoop在附加的子包的 mapred和mapreduceAPI。

flink-hadoop-compatibility Maven模块中包含对Hadoop Mappers和Reducers的支持。此代码驻留在org.apache.flink.hadoopcompatibility 包中。

pom.xml如果要重用Mappers和Reducers，请将以下依赖项添加到您的。

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-hadoop-compatibility_2.11</artifactId>
    <version>1.7-SNAPSHOT</version>
</dependency>

使用Hadoop数据类型

Flink支持开箱即用的所有Hadoop Writable和WritableComparable数据类型。如果您只想使用Hadoop数据类型，则不需要包含Hadoop兼容性依赖项。有关详细信息，请参阅 编程指南。

使用Hadoop InputFormats

要使用的Hadoop InputFormats与Flink格式必须首先使用任一包裹readHadoopFile或createHadoopInput在的 HadoopInputsutilty类。前者用于从FileInputFormat后者输出的输入格式，而后者必须用于通用输入格式。结果InputFormat可用于通过使用创建数据源 ExecutionEnvironmen#createInput。

结果DataSet包含2元组，其中第一个字段是键，第二个字段是从Hadoop InputFormat检索的值。

以下示例显示了如何使用Hadoop TextInputFormat。

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

DataSet<Tuple2<LongWritable, Text>> input =
    env.createInput(HadoopInputs.readHadoopFile(new TextInputFormat(),
                        LongWritable.class, Text.class, textPath));

// Do something with the data.
[...]

val env = ExecutionEnvironment.getExecutionEnvironment

val input: DataSet[(LongWritable, Text)] =
  env.createInput(HadoopInputs.readHadoopFile(
                    new TextInputFormat, classOf[LongWritable], classOf[Text], textPath))

// Do something with the data. [...]

使用Hadoop OutputFormats

Flink为Hadoop提供了兼容性打包器OutputFormats。支持实现org.apache.hadoop.mapred.OutputFormat或扩展的 任何类org.apache.hadoop.mapreduce.OutputFormat。OutputFormat打包器期望其输入数据是包含2元组键和值的DataSet。这些将由Hadoop OutputFormat处理。

以下示例显示了如何使用Hadoop TextOutputFormat。

// Obtain the result we want to emit
DataSet<Tuple2<Text, IntWritable>> hadoopResult = [...]

// Set up the Hadoop TextOutputFormat.
HadoopOutputFormat<Text, IntWritable> hadoopOF =
  // create the Flink wrapper.
  new HadoopOutputFormat<Text, IntWritable>(
    // set the Hadoop OutputFormat and specify the job.
    new TextOutputFormat<Text, IntWritable>(), job
  );
hadoopOF.getConfiguration().set("mapreduce.output.textoutputformat.separator", " ");
TextOutputFormat.setOutputPath(job, new Path(outputPath));

// Emit data using the Hadoop TextOutputFormat.
hadoopResult.output(hadoopOF);

// Obtain your result to emit. val hadoopResult: DataSet[(Text, IntWritable)] = [...]

val hadoopOF = new HadoopOutputFormat[Text,IntWritable](
  new TextOutputFormat[Text, IntWritable],
  new JobConf)

hadoopOF.getJobConf.set("mapred.textoutputformat.separator", " ")
FileOutputFormat.setOutputPath(hadoopOF.getJobConf, new Path(resultPath))

hadoopResult.output(hadoopOF)

使用Hadoop Mappers和Reducers

Hadoop Mappers在语义上等同于Flink的FlatMapFunctions，而Hadoop Reducers等同于Flink的GroupReduceFunctions。Flink为Hadoop MapReduce Mapper和Reducer接口的实现提供打包器，即，您可以在常规Flink程序中重用您的Hadoop Mappers和Reducers。目前，只org.apache.hadoop.mapred支持Hadoop的mapred API（）的Mapper和Reduce接口。

打包器将DataSet&lt;Tuple2&lt;KEYIN,VALUEIN&gt;&gt;输入作为输入并生成DataSet&lt;Tuple2&lt;KEYOUT,VALUEOUT&gt;&gt;输出，其中KEYIN和KEYOUT是键，VALUEIN并且VALUEOUT是Hadoop函数处理的Hadoop键值对的值。对于Reducers，Flink为GroupReduceFunction提供了一个打包器（HadoopReduceCombineFunction），没有Combiner（HadoopReduceFunction）。打包器接受一个可选JobConf对象来配置Hadoop Mapper或Reducer。

Flink的函数打包器是

  • org.apache.flink.hadoopcompatibility.mapred.HadoopMapFunction，
  • org.apache.flink.hadoopcompatibility.mapred.HadoopReduceFunction，和
  • org.apache.flink.hadoopcompatibility.mapred.HadoopReduceCombineFunction。

并可用作常规Flink FlatMapFunctions或GroupReduceFunctions。

以下示例显示了如何使用Hadoop Mapper和Reducer函数。

// Obtain data to process somehow.
DataSet<Tuple2<Text, LongWritable>> text = [...]

DataSet<Tuple2<Text, LongWritable>> result = text
  // use Hadoop Mapper (Tokenizer) as MapFunction
  .flatMap(new HadoopMapFunction<LongWritable, Text, Text, LongWritable>(
    new Tokenizer()
  ))
  .groupBy(0)
  // use Hadoop Reducer (Counter) as Reduce- and CombineFunction
  .reduceGroup(new HadoopReduceCombineFunction<Text, LongWritable, Text, LongWritable>(
    new Counter(), new Counter()
  ));

请注意： Reducer打包器适用于Flink的groupBy（） 算子操作定义的组。它不考虑您可能在中设置的任何自定义分区器，排序或分组比较器JobConf。

完成Hadoop WordCount示例

以下示例显示了使用Hadoop数据类型，Input-和OutputFormats以及Mapper和Reducer实现的完整WordCount实现。

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

// Set up the Hadoop TextInputFormat.
Job job = Job.getInstance();
HadoopInputFormat<LongWritable, Text> hadoopIF =
  new HadoopInputFormat<LongWritable, Text>(
    new TextInputFormat(), LongWritable.class, Text.class, job
  );
TextInputFormat.addInputPath(job, new Path(inputPath));

// Read data using the Hadoop TextInputFormat.
DataSet<Tuple2<LongWritable, Text>> text = env.createInput(hadoopIF);

DataSet<Tuple2<Text, LongWritable>> result = text
  // use Hadoop Mapper (Tokenizer) as MapFunction
  .flatMap(new HadoopMapFunction<LongWritable, Text, Text, LongWritable>(
    new Tokenizer()
  ))
  .groupBy(0)
  // use Hadoop Reducer (Counter) as Reduce- and CombineFunction
  .reduceGroup(new HadoopReduceCombineFunction<Text, LongWritable, Text, LongWritable>(
    new Counter(), new Counter()
  ));

// Set up the Hadoop TextOutputFormat.
HadoopOutputFormat<Text, IntWritable> hadoopOF =
  new HadoopOutputFormat<Text, IntWritable>(
    new TextOutputFormat<Text, IntWritable>(), job
  );
hadoopOF.getConfiguration().set("mapreduce.output.textoutputformat.separator", " ");
TextOutputFormat.setOutputPath(job, new Path(outputPath));

// Emit data using the Hadoop TextOutputFormat.
result.output(hadoopOF);

// Execute Program
env.execute("Hadoop WordCount");

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