How to read a zip containing multiple files in Apache Spark

Question

I am having a Zipped file containing multiple text files. I want to read each of the file and build a List of RDD containining the content of each files.

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Answer 1

Here's a working version of @Atais solution (which needs enhancement by closing the streams) :

implicit class ZipSparkContext(val sc: SparkContext) extends AnyVal {

def readFile(path: String,
             minPartitions: Int = sc.defaultMinPartitions): RDD[String] = {

  if (path.toLowerCase.contains("zip")) {

    sc.binaryFiles(path, minPartitions)
      .flatMap {
        case (zipFilePath, zipContent) ⇒
          val zipInputStream = new ZipInputStream(zipContent.open())
          Stream.continually(zipInputStream.getNextEntry)
            .takeWhile(_ != null)
            .map { _ ⇒
              scala.io.Source.fromInputStream(zipInputStream, "UTF-8").getLines.mkString("\n")
            } #::: { zipInputStream.close; Stream.empty[String] }
      }
  } else {
    sc.textFile(path, minPartitions)
  }
}
}

Then all you have to do is the following to read a zip file :

sc.readFile(path)

Answer 2

If you are reading binary files use sc.binaryFiles. This will return an RDD of tuples containing the file name and a PortableDataStream. You can feed the latter into a ZipInputStream.

Answer 3

Apache Spark default compression support

I have written all the necessary theory in other answer, that you might want to refer to: https://stackoverflow.com/a/45958182/1549135

Read zip containing multiple files

I have followed the advice given by @Herman and used ZipInputStream. This gave me this solution, which returns RDD[String] of the zip content.

import java.io.{BufferedReader, InputStreamReader}
import java.util.zip.ZipInputStream
import org.apache.spark.SparkContext
import org.apache.spark.input.PortableDataStream
import org.apache.spark.rdd.RDD

implicit class ZipSparkContext(val sc: SparkContext) extends AnyVal {

    def readFile(path: String,
                 minPartitions: Int = sc.defaultMinPartitions): RDD[String] = {

      if (path.endsWith(".zip")) {
        sc.binaryFiles(path, minPartitions)
          .flatMap { case (name: String, content: PortableDataStream) =>
            val zis = new ZipInputStream(content.open)
            Stream.continually(zis.getNextEntry)
                  .takeWhile {
                      case null => zis.close(); false
                      case _ => true
                  }
                  .flatMap { _ =>
                      val br = new BufferedReader(new InputStreamReader(zis))
                      Stream.continually(br.readLine()).takeWhile(_ != null)
                  }
        }
      } else {
        sc.textFile(path, minPartitions)
      }
    }
  }

simply use it by importing the implicit class and call the readFile method on SparkContext:

import com.github.atais.spark.Implicits.ZipSparkContext
sc.readFile(path)

Answer 4

This filters only the first line. can anyone share your insights. I am trying to read a CSV file which is zipped and create JavaRDD for further processing.

JavaPairRDD<String, PortableDataStream> zipData =
                sc.binaryFiles("hdfs://temp.zip");
        JavaRDD<Record> newRDDRecord = zipData.flatMap(
          new FlatMapFunction<Tuple2<String, PortableDataStream>, Record>(){
              public Iterator<Record> call(Tuple2<String,PortableDataStream> content) throws Exception {
                  List<Record> records = new ArrayList<Record>();
                      ZipInputStream zin = new ZipInputStream(content._2.open());
                      ZipEntry zipEntry;
                      while ((zipEntry = zin.getNextEntry()) != null) {
                          count++;
                          if (!zipEntry.isDirectory()) {
                              Record sd;
                              String line;
                              InputStreamReader streamReader = new InputStreamReader(zin);
                              BufferedReader bufferedReader = new BufferedReader(streamReader);
                              line = bufferedReader.readLine();
                              String[] records= new CSVParser().parseLineMulti(line);
                              sd = new Record(TimeBuilder.convertStringToTimestamp(records[0]),
                                        getDefaultValue(records[1]),
                                        getDefaultValue(records[22]));
                              records.add(sd);
                          }
                      }

                return records.iterator();
              }

        });

Answer 5

Here is another working solution which gives out file name which can be later split and used to create separate schemas from it.

implicit class ZipSparkContext(val sc: SparkContext) extends AnyVal {

    def readFile(path: String,
                 minPartitions: Int = sc.defaultMinPartitions): RDD[String] = {

      if (path.toLowerCase.contains("zip")) {

        sc.binaryFiles(path, minPartitions)
          .flatMap {
            case (zipFilePath, zipContent) ⇒
              val zipInputStream = new ZipInputStream(zipContent.open())
              Stream.continually(zipInputStream.getNextEntry)
                .takeWhile(_ != null)
                .map { x ⇒
                  val filename1 = x.getName
                  scala.io.Source.fromInputStream(zipInputStream, "UTF-8").getLines.mkString(s"~${filename1}\n")+s"~${filename1}"
                } #::: { zipInputStream.close; Stream.empty[String] }
          }
      } else {
        sc.textFile(path, minPartitions)
      }
    }
  }

full code is here

https://github.com/kali786516/Spark2StructuredStreaming/blob/master/src/main/scala/com/dataframe/extraDFExamples/SparkReadZipFiles.scala