How to write to CSV in Spark

Extending Tathagata Das answer to Spark 2.x and Scala 2.11

Using Spark SQL we can do this in one liner

//implicits for magic functions like .toDf
import spark.implicits._

val df = Seq(
  ("first", 2.0),
  ("choose", 7.0),
  ("test", 1.5)
).toDF("name", "vals")

//write DataFrame/DataSet to external storage
df.write
  .format("csv")
  .save("csv/file/location")

Then you can go head and proceed with adoalonso's answer.

Since Spark uses Hadoop File System API to write data to files, this is sort of inevitable. If you do

rdd.saveAsTextFile("foo")

It will be saved as "foo/part-XXXXX" with one part-* file every partition in the RDD you are trying to save. The reason each partition in the RDD is written a separate file is for fault-tolerance. If the task writing 3rd partition (i.e. to part-00002) fails, Spark simply re-run the task and overwrite the partially written/corrupted part-00002, with no effect on other parts. If they all wrote to the same file, then it is much harder recover a single task for failures.

The part-XXXXX files are usually not a problem if you are going to consume it again in Spark / Hadoop-based frameworks because since they all use HDFS API, if you ask them to read "foo", they will all read all the part-XXXXX files inside foo as well.

I'll suggest to do it in this way (Java example):

theRddToPrint.coalesce(1, true).saveAsTextFile(textFileName);
FileSystem fs = anyUtilClass.getHadoopFileSystem(rootFolder);
FileUtil.copyMerge(
    fs, new Path(textFileName),
    fs, new Path(textFileNameDestiny),
    true, fs.getConf(), null);