一、概述

本文将通过源码出发讲述spark如何调用hadoop几种OutputFormat,从而实现的文件输出，这里将讲述几种工作中常使用的算子，例如：

saveAsTextFile

(

path

) 、

saveAsHadoopFile

(

path

)

二、spark源码分析

saveAsTextFile

(

path

)底层调用也是

saveAsHadoopFile

(

path

)，所以这里主要是讲述后者的源码；这一步也将带你认识到可以自定义的内容；

1.main

def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("")
    val sc = new SparkContext(conf)
    //禁用success文件
    sc.hadoopConfiguration.set("mapreduce.fileoutputcommitter.marksuccessfuljobs", "false")
    val value: RDD[(String,Int)] = sc.parallelize(List(
      ("1",1), ("1",1), ("2",1), ("2",1),("2",1),
    ))
    value1
      .saveAsHadoopFile("C:\\Users\\Desktop\\learn\\spark_program_test\\definedFileName"
                        ,classOf[String]
                        ,classOf[String]
                        ,classOf[TextOutputFormat[String,String]])
    sc.stop()
  }

2.PairRDDFunctions

def saveAsHadoopFile[F <: OutputFormat[K, V]](
      path: String)(implicit fm: ClassTag[F]): Unit = self.withScope {
    saveAsHadoopFile(path, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]])
  }

def saveAsHadoopFile(
      path: String,
      keyClass: Class[_],
      valueClass: Class[_],
      outputFormatClass: Class[_ <: OutputFormat[_, _]],
      conf: JobConf = new JobConf(self.context.hadoopConfiguration),
      codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope {
    // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
    val hadoopConf = conf
    hadoopConf.setOutputKeyClass(keyClass)
    hadoopConf.setOutputValueClass(valueClass)
    conf.setOutputFormat(outputFormatClass)
    for (c <- codec) {
      hadoopConf.setCompressMapOutput(true)
      hadoopConf.set("mapreduce.output.fileoutputformat.compress", "true")
      hadoopConf.setMapOutputCompressorClass(c)
      hadoopConf.set("mapreduce.output.fileoutputformat.compress.codec", c.getCanonicalName)
      hadoopConf.set("mapreduce.output.fileoutputformat.compress.type",
        CompressionType.BLOCK.toString)
    }

    // Use configured output committer if already set
    if (conf.getOutputCommitter == null) {
      hadoopConf.setOutputCommitter(classOf[FileOutputCommitter])
    }

    // When speculation is on and output committer class name contains "Direct", we should warn
    // users that they may loss data if they are using a direct output committer.
    val speculationEnabled = self.conf.getBoolean("spark.speculation", false)
    val outputCommitterClass = hadoopConf.get("mapred.output.committer.class", "")
    if (speculationEnabled && outputCommitterClass.contains("Direct")) {
      val warningMessage =
        s"$outputCommitterClass may be an output committer that writes data directly to " +
          "the final location. Because speculation is enabled, this output committer may " +
          "cause data loss (see the case in SPARK-10063). If possible, please use an output " +
          "committer that does not have this behavior (e.g. FileOutputCommitter)."
      logWarning(warningMessage)
    }

    FileOutputFormat.setOutputPath(hadoopConf,
      SparkHadoopWriterUtils.createPathFromString(path, hadoopConf))
    saveAsHadoopDataset(hadoopConf)
  }

这里指定了OutputFormat为TextOutputFormat,如果不指定也是默认TextOutputFormat；进入到PairRDDFunctions第二个方法，其中saveAsHadoopDataset(hadoopConf)再进去；

def saveAsHadoopDataset(conf: JobConf): Unit = self.withScope {
    // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
    val hadoopConf = conf
    val outputFormatInstance = hadoopConf.getOutputFormat
    val keyClass = hadoopConf.getOutputKeyClass
    val valueClass = hadoopConf.getOutputValueClass
    if (outputFormatInstance == null) {
      throw new SparkException("Output format class not set")
    }
    if (keyClass == null) {
      throw new SparkException("Output key class not set")
    }
    if (valueClass == null) {
      throw new SparkException("Output value class not set")
    }
    SparkHadoopUtil.get.addCredentials(hadoopConf)

    logDebug("Saving as hadoop file of type (" + keyClass.getSimpleName + ", " +
      valueClass.getSimpleName + ")")

    if (SparkHadoopWriterUtils.isOutputSpecValidationEnabled(self.conf)) {
      // FileOutputFormat ignores the filesystem parameter
      val ignoredFs = FileSystem.get(hadoopConf)
      hadoopConf.getOutputFormat.checkOutputSpecs(ignoredFs, hadoopConf)
    }

    val writer = new SparkHadoopWriter(hadoopConf)
    writer.preSetup()

    val writeToFile = (context: TaskContext, iter: Iterator[(K, V)]) => {
      // Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it
      // around by taking a mod. We expect that no task will be attempted 2 billion times.
      val taskAttemptId = (context.taskAttemptId % Int.MaxValue).toInt

      val (outputMetrics, callback) = SparkHadoopWriterUtils.initHadoopOutputMetrics(context)

      writer.setup(context.stageId, context.partitionId, taskAttemptId)
      writer.open()
      var recordsWritten = 0L

      Utils.tryWithSafeFinallyAndFailureCallbacks {
        while (iter.hasNext) {
          val record = iter.next()
          writer.write(record._1.asInstanceOf[AnyRef], record._2.asInstanceOf[AnyRef])

          // Update bytes written metric every few records
          SparkHadoopWriterUtils.maybeUpdateOutputMetrics(outputMetrics, callback, recordsWritten)
          recordsWritten += 1
        }
      }(finallyBlock = writer.close())
      writer.commit()
      outputMetrics.setBytesWritten(callback())
      outputMetrics.setRecordsWritten(recordsWritten)
    }

    self.context.runJob(self, writeToFile)
    writer.commitJob()
  }

到达这里就是写入文件的主要逻辑了：

①writer.open()：他是SparkHadoopWriter的方法；首先这里会初始化文件名（诸如part-0000），然后传入你所设置的OutputFormat类的getRecordWriter返回的RecordWriter中，所以如果想要自定义文件名，从这里看来可以重写getRecordWriter方法，后面会讲解TextOutputFormat和MultipleTextOutputFormat如何重写的getRecordWriter；

def open() {
    val numfmt = NumberFormat.getInstance(Locale.US)
    numfmt.setMinimumIntegerDigits(5)
    numfmt.setGroupingUsed(false)

    val outputName = "part-"  + numfmt.format(splitID)
    val path = FileOutputFormat.getOutputPath(conf.value)
    val fs: FileSystem = {
      if (path != null) {
        path.getFileSystem(conf.value)
      } else {
        FileSystem.get(conf.value)
      }
    }
    getOutputCommitter().setupTask(getTaskContext())
    writer = getOutputFormat().getRecordWriter(fs, conf.value, outputName, Reporter.NULL)
  }

②writeToFile函数：这是一块是具体如何写入文件的，首先可以看出这里每个分区只会生成一个文件，然后是调用你所设置的OutputFormat所使用的RecordWriter的write方法写入文件；如果要自定义写入内容，也就要自定义RecordWriter类；

三、TextOutputFormat & MultipleTextOutputFormat

1.TextoutputFormat

这个类可以直接复制，然后根据自己的需求稍微改点代码就行了，接下来我就从需求出来来看看怎么重写这个类；

①文件编码格式为UTF-8以外的编码格式，或者换行符不为 ‘\n’：因为在TextoutputFormat的LineRecoderWriter中这两个是写死的，所以需要重写TextoutputFormat类，只需要复制整个类代码就行，然后修改你需要改的地方，例如：

public class MyOutput<K,V> extends FileOutputFormat<K, V> {
    protected static class LineRecordWriter<K, V>
            implements RecordWriter<K, V> {
        private static final String utf8 = "GBK";
        private static final byte[] newline;
        static {
            try {
                newline = "\r\n".getBytes(utf8);
            } catch (UnsupportedEncodingException uee) {
                throw new IllegalArgumentException("can't find " + utf8 + " encoding");
            }
        }
。。。

这里就修改了编码格式和换行符

②key/value的分隔符，这个可以写死（重写的时候），也可以在main中改hadoopconf配置

def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("")
    val sc = new SparkContext(conf)
    //修改输出文件的key/value分隔符
    sc.hadoopConfiguration.set("mapreduce.output.textoutputformat.separator",",")

③修改文件名

@Override
    public RecordWriter<K, V> getRecordWriter(FileSystem ignored, JobConf job, String name, Progressable progress) throws IOException {
        //重写的类加上的，这个可以自定义
        name = Integer.parseInt(name.split("-")[1])+"";

。。。
}

④修改key/value写入逻辑：这里我没有做修改，可以根据业务逻辑修改；

  public synchronized void write(K key, V value)
                throws IOException {

            boolean nullKey = key == null || key instanceof NullWritable;
            boolean nullValue = value == null || value instanceof NullWritable;
            if (nullKey && nullValue) {
                return;
            }
            if (!nullKey) {
                writeObject(key);
            }
            if (!(nullKey || nullValue)) {
                out.write(keyValueSeparator);
            }
            if (!nullValue) {
                writeObject(value);
            }
            out.write(newline);
        }
//PairRddFunction中传过来的时候，key/value都转换为了Anyval，所以这里会走else
private void writeObject(Object o) throws IOException {
            if (o instanceof Text) {
                Text to = (Text) o;
                out.write(to.getBytes(), 0, to.getLength());
            } else {
                out.write(o.toString().getBytes(utf8));
            }
        }

四、MultipleTextOutputFormat

这是hadoop提供的简易的自定义文件名，自定义输出key/value数据，但是最终写入文件还是TextOutputFormat的LineRcorderWriter，也就是说文件编码格式、换行符无法自定义。

  //修改生成的分区文件名，每个分区传入的name不同诸如：Part-0001，优先级低于generateFileNameForKeyValue
  protected String generateLeafFileName(String name) {
    return name;
  }

  //key，value不用解释，这里的name是generateLeafFileName返回的name，如果没有generateLeafFileName则是Part—0001，需要注意的是，由于是多分区写文件，如果不同分区生成文件名同样的文件，将会被覆盖，如果仅用key，必须保证相同key在同一分区，key+name则可以保证不会被覆盖，但是可能文件生成太多
  protected String generateFileNameForKeyValue(K key, V value, String name) {
    return name;
  }

//实际写入key
  protected K generateActualKey(K key, V value) {
    return key;
  }
  
  //实际写入的value
  protected V generateActualValue(K key, V value) {
    return value;
  }
  
  //这个方法决定了最终写入文件的RecorderWriter，是getRecordWriter方法调用的，实际上mutipleOutputFormat，重写的RcordWriter（内部类的形式），只是使得name，key，value可以自定义
  abstract protected RecordWriter<K, V> getBaseRecordWriter(FileSystem fs,
      JobConf job, String name, Progressable arg3) throws IOException;

备注：1.如果业务逻辑保函需要按照规定的文件大小或者条数切分，除了用foreachPartition算子，剩下的就必须修改PairRddFunction类的saveAsHadoopDataset方法的源码了；

2.spark写本地文件会生成.crc文件，写入hdfs不会生成；

原文链接：https://blog.csdn.net/m0_64640191/article/details/129726358