Scala Spark loop goes through without any error, but does not produce an output - scala

Question

I have a file in HDFS containing paths of various other files. Here is the file called file1:
path/of/HDFS/fileA
path/of/HDFS/fileB
path/of/HDFS/fileC
.
.
.
I am using a for loop in Scala Spark as follows to read each line of the above file and process it in another function:
val lines=Source.fromFile("path/to/file1.txt").getLines.toList
for(i<-lines){
i.toString()
val firstLines=sc.hadoopFile(i,classOf[TextInputFormat],classOf[LongWritable],classOf[Text]).flatMap {
case (k, v) => if (k.get == 0) Seq(v.toString) else Seq.empty[String]
}
}
when I run the above loop, it runs through without returning any errors and I get the Scala prompt in a new line: scala>
However, when I try to see a few lines of output which should be stored in firstLines, it does not work:
scala> firstLines
<console>:38: error: not found: value firstLines
firstLine
^
What is the problem in the above loop that is not producing the output, however running through without any errors?
Additional info
The function hadoopFile accepts a String path name as its first parameter. That is why I am trying to pass each line of file1 (each line is a path name) as a String in the first parameter i. The flatMap functionality is taking the first line of the file that has been passed to hadoopFile and stores that alone and dumps all the other lines. So the desired output (firstLines) should be the first line of all the files that are being passed to hadoopFile through their path names (i).
I tried running the function for just a single file, without a looop, and that produces the output:
val firstLines=sc.hadoopFile("path/of/HDFS/fileA",classOf[TextInputFormat],classOf[LongWritable],classOf[Text]).flatMap {
case (k, v) => if (k.get == 0) Seq(v.toString) else Seq.empty[String]
}
scala> firstLines.take(3)
res27: Array[String] = Array(<?xml version="1.0" encoding="utf-8"?>)
fileA is an XML file, so you can see the resulting first line of that file. So I know the function works fine, it is just a problem with the loop that I am not able to figure out. Please help.

Answer 1

The variable firstLines is defined in the body of the for loop and its scope is therefore limited to this loop. This means you cannot access the variable outside of the loop, and this is why the Scala compiler tells you error: not found: value firstLines.
From your description, I understand you want to collect the first line of every file which are listed in lines.
The every here can translate into different construct in Scala. We can use something like the for loop you wrote or even better adopt a functional approach and use a map function applied on the list of files. In the code below I put inside the map the code you used in your description, which creates an HadoopRDD and applies flatMap with your function to retrieve the first line of a file.
We then obtain a list of RDD[String] of lines. At this stage, note that we have not started to do any actual work. To trigger the evaluation of the RDDs and collect the result, we need an addition call to the collect method for each of the RDD we have in our list.
// Renamed "lines" to "files" as it is more explicit.
val fileNames = Source.fromFile("path/to/file1.txt").getLines.toList
val firstLinesRDDs = fileNames.map(sc.hadoopFile(_,classOf[TextInputFormat],classOf[LongWritable],classOf[Text]).flatMap {
case (k, v) => if (k.get == 0) Seq(v.toString) else Seq.empty[String]
})
// firstLinesRDDs is a list of RDD[String]. Based on this code, each RDD
// should consist in a single String value. We collect them using RDD#collect:
val firstLines = firstLinesRDDs.map(_.collect)
However, this approach suffers from a flaw which prevent us to benefit from any advantage Spark can provide.
When we apply the operation in map to filenames, we are not working with an RDD, hence the file names are processed sequentially on the driver (the process which hosts your Spark session) and not part of a parallelizable Spark job. This is equivalent to doing what you wrote in your second block of code, one file name at a time.
To address the problem, what can we do? A good thing to keep in mind when working with Spark is to try to push the declaration of the RDDs as early as possible in our code. Why? Because this allows Spark to parallelize and optimize the work we want to do. Your example could be a textbook illustration of this concept, though an additional complexity here is added by the requirement to manipulate files.
In our present case, we can benefit from the fact that hadoopFile accepts comma-separated files in input. Therefore, instead of sequentially creating RDDs for every file, we create one RDD for all of them:
val firstLinesRDD = sc.hadoopFile(fileNames.mkString(","), classOf[TextInputFormat],classOf[LongWritable],classOf[Text]).flatMap {
case (k, v) => if (k.get == 0) Seq(v.toString) else Seq.empty[String]
}
And we retrieve our first lines with a single collect:
val firstLines = firstLinesRDD.collect