write an RDD into HDFS in a spark-streaming context

Question

I have a spark streaming environment with spark 1.2.0 where i retrieve data from a local folder and every time I find a new file added to the folder I perform some transformation.

val ssc = new StreamingContext(sc, Seconds(10))
val data = ssc.textFileStream(directory)

In order to perform my analysis on DStream data I have to transform it into an Array

var arr = new ArrayBuffer[String]();
   data.foreachRDD {
   arr ++= _.collect()
}

Then I use data obtained to extract the information I want and to save them on HDFS.

val myRDD  = sc.parallelize(arr)
myRDD.saveAsTextFile("hdfs directory....")

Since I really need to manipulate data with an Array it's impossible to save data on HDFS with DStream.saveAsTextFiles("...") (which would work fine) and I have to save the RDD but with this preocedure I finally have empty output files named part-00000 etc...

With an arr.foreach(println) I am able to see the correct results of the transofmations.

My suspect is that spark tries at every batch to write data in the same files, deleting what was previously written. I tried to save in a dynamic named folder like myRDD.saveAsTextFile("folder" + System.currentTimeMillis().toString()) but always only one foldes is created and the output files are still empty.

How can I write an RDD into HDFS in a spark-streaming context?

Answer 1

You are using Spark Streaming in a way it wasn't designed. I'd either recommend drop using Spark for your use case, or adapt your code so it works the Spark way. Collecting the array to the driver defeats the purpose of using a distributed engine and makes your app effectively single-machine (two machines will also cause more overhead than just processing the data on a single machine).

Everything you can do with an array, you can do with Spark. So just run your computations inside the stream, distributed on the workers, and write your output using DStream.saveAsTextFiles(). You can use foreachRDD + saveAsParquet(path, overwrite = true) to write to a single file.