Better way to convert a string field into timestamp in Spark

Question

I have a CSV in which a field is datetime in a specific format. I cannot import it directly in my Dataframe because it needs to be a timestamp. So I import it as string and

Answer 1

I would like to move the getTimeStamp method wrote by you into rdd's mapPartitions and reuse GenericMutableRow among rows in an iterator:

val strRdd = sc.textFile("hdfs://path/to/cvs-file")
val rowRdd: RDD[Row] = strRdd.map(_.split('\t')).mapPartitions { iter =>
  new Iterator[Row] {
    val row = new GenericMutableRow(4)
    var current: Array[String] = _

    def hasNext = iter.hasNext
    def next() = {
      current = iter.next()
      row(0) = current(0)
      row(1) = current(1)
      row(2) = current(2)

      val ts = getTimestamp(current(3))
      if(ts != null) {
        row.update(3, ts)
      } else {
        row.setNullAt(3)
      }
      row
    }
  }
}

And you should still use schema to generate a DataFrame

val df = sqlContext.createDataFrame(rowRdd, tableSchema)

The usage of GenericMutableRow inside an iterator implementation could be find in Aggregate Operator, InMemoryColumnarTableScan, ParquetTableOperations etc.