Spark: Reading files using different delimiter than new line

In Spark shell, I extracted data according to Setting textinputformat.record.delimiter in spark:

$ spark-shell
...
scala> import org.apache.hadoop.io.LongWritable
import org.apache.hadoop.io.LongWritable

scala> import org.apache.hadoop.io.Text
import org.apache.hadoop.io.Text

scala> import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.conf.Configuration

scala> import org.apache.hadoop.mapreduce.lib.input.TextInputFormat
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat

scala> val conf = new Configuration
conf: org.apache.hadoop.conf.Configuration = Configuration: core-default.xml, core-site.xml, mapred-default.xml, mapred-site.xml, yarn-default.xml, yarn-site.xml

scala> conf.set("textinputformat.record.delimiter", "\u0001")

scala> val data = sc.newAPIHadoopFile("mydata.txt", classOf[TextInputFormat], classOf[LongWritable], classOf[Text], conf).map(_._2.toString)
data: org.apache.spark.rdd.RDD[(org.apache.hadoop.io.LongWritable, org.apache.hadoop.io.Text)] = NewHadoopRDD[0] at newAPIHadoopFile at <console>:19

sc.newAPIHadoopFile("mydata.txt", ...) is a RDD[(LongWritable, Text)], where the first part of the elements is the starting character index, and the second part is the actual text delimited by "\u0001".

You can use textinputformat.record.delimiter to set the delimiter for TextInputFormat, E.g.,

import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.mapreduce.Job
import org.apache.hadoop.io.{LongWritable, Text}
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat

val conf = new Configuration(sc.hadoopConfiguration)
conf.set("textinputformat.record.delimiter", "X")
val input = sc.newAPIHadoopFile("file_path", classOf[TextInputFormat], classOf[LongWritable], classOf[Text], conf)
val lines = input.map { case (_, text) => text.toString}
println(lines.collect)

For example, my input is a file containing one line aXbXcXd. The above code will output

Array(a, b, c, d)