Xtykutl

I have a single tar file mytar.tar that is 40 GB in size. Inside this tar file are 500 tar.gz files, and inside each one of these tar.gz files are a bunch of JSON files. I have written up the code to process this single tar file and attempt to get the list of JSON string contents. My code looks like the following.

val isRdd = sc.binaryFiles("/mnt/mytar.tar")

  .flatMap(t => { 

    val buf = scala.collection.mutable.ListBuffer.empty[TarArchiveInputStream]

    val stream = t._2

    val is = new TarArchiveInputStream(stream.open())

    var entry = is.getNextTarEntry()

    while (entry != null) {

      val name = entry.getName()

      val size = entry.getSize.toInt



      if (entry.isFile() && size > -1) {

        val content = new Array[Byte](size)

        is.read(content, 0, content.length)



        val tgIs = new TarArchiveInputStream(new GzipCompressorInputStream(new ByteArrayInputStream(content)))

        buf += tgIs

      }

      entry = is.getNextTarEntry()

    }

    buf.toList

  })

  .cache



val byteRdd = isRdd.flatMap(is => {

    val buf = scala.collection.mutable.ListBuffer.empty[Array[Byte]]

    var entry = is.getNextTarEntry()

    while (entry != null) {

      val name = entry.getName()

      val size = entry.getSize.toInt



      if (entry.isFile() && name.endsWith(".json") && size > -1) {

        val data = new Array[Byte](size)

        is.read(data, 0, data.length)

        buf += data

      }

      entry = is.getNextTarEntry()

    }

    buf.toList

  })

  .cache



val jsonRdd = byteRdd

  .map(arr => getJson(arr))

  .filter(_.length > 0)

  .cache



jsonRdd.count //action just to execute the code

When I execute this code, I get an OutOfMemoryError (OOME).

org.apache.spark.SparkException: Job aborted due to stage failure: 

Task 0 in stage 24.0 failed 4 times, most recent failure: 

Lost task 0.3 in stage 24.0 (TID 137, 10.162.224.171, executor 13): 

java.lang.OutOfMemoryError: Java heap space

My EC2 cluster has 1 driver and 2 worker nodes of type i3.xlarge (30.5 GB Memory, 4 Cores). From looking at the logs and just thinking about it, I believe the OOME happens during the creation of the isRDD (input stream RDD).

Is there anything else in the code or creation of my Spark cluster that I can do to mitigate this problem? Should I select an EC2 instance with more memory (e.g. a memory optimized instance like R5.2xlarge)? FWIW, I upgraded to an R5.2xlarge cluster setting and still saw the OOME.

One thing I have thought about doing was to untar mytar.tar and instead start with the .tar.gz files inside. I am thinking that each .tar.gz inside the tar file will have to be less than 30 GB to avoid the OOME (on the i3.xlarge).

Any tips or advice is appreciated.