1

我正在使用 Spark(v1.6.1) 阅读 Hadoop 序列文件。缓存RDD后，RDD中的内容就失效了（最后一个条目重复了n几次）。

这是我的代码片段：

import org.apache.hadoop.io.Text
import org.apache.hadoop.mapred.SequenceFileOutputFormat
import org.apache.spark.{SparkConf, SparkContext}

object Main {
  def main(args: Array[String]) {
    val seqfile = "data-1.seq"
    val conf: SparkConf = new SparkConf()
      .setAppName("..Buffer..")
      .setMaster("local")
      .registerKryoClasses(Array(classOf[Text]))
    val sc = new SparkContext(conf)

    sc.parallelize((0 to 1000).toSeq) //creating a sample sequence file
      .map(i => (new Text(s"$i"), new Text(s"${i*i}")))
      .saveAsHadoopFile(seqfile, classOf[Text], classOf[Text],
        classOf[SequenceFileOutputFormat[Text, Text]])

    val c = sc.sequenceFile(seqfile, classOf[Text], classOf[Text])
      .cache()
      .map(t => {println(t); t})
      .collectAsMap()
    println(c)
    println(c.size)

    sc.stop()
  }
}

输出：

(1000,1000000)
(1000,1000000)
(1000,1000000)
(1000,1000000)
(1000,1000000)
...... //Total 1000 lines with same content as above ...
Map(1000 -> 1000000)
1

编辑：对于未来的访问者：如果您正在阅读我在上面的代码片段中所做的序列文件，请参阅接受的答案。一个简单的解决方法是制作 HadoopWritable实例的副本：

val c = sc.sequenceFile(seqfile, classOf[Text], classOf[Text])
  .map(t =>(new Text(t._1), new Text(t._2)))   //Make copy of writable instances
4

2 回答 2

3

请参考 sequenceFile 中的注释。

/** Get an RDD for a Hadoop SequenceFile with given key and value types.
 *
 * '''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each
 * record, directly caching the returned RDD or directly passing it to an aggregation or shuffle
 * operation will create many references to the same object.
 * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first
 * copy them using a `map` function.
 */
于 2016-03-23T03:56:44.563 回答
0

下面的代码对我有用....而不是 getbytes 我使用了 copybytes 

val response = sc.sequenceFile(inputPathConcat, classOf[Text], classOf[BytesWritable])
  .map(x => (org.apache.hadoop.io.Text.decode(x._2.copyBytes())))
于 2018-08-29T08:31:06.537 回答