how to make saveAsTextFile NOT split output into multiple file?
When using Scala in Spark, whenever I dump the results out using saveAsTextFile, it seems to split the output into multiple parts. I'm just passing a parameter(path) to it.
val year = sc.textFile("apat63_99.txt").map(_.split(",")(1)).flatMap(_.split(",")).map((_,1)).reduceByKey((_+_)).map(_.swap)
year.saveAsTextFile("year")
- Does the number of outputs correspond to the number of reducers it uses?
- Does this mean the output is compressed?
- I know I can combine the output together using bash, but is there an option to store the output in a single text file, without splitting?? I looked at the API docs, but it doesn't say much about this.
Answer
The reason it saves it as multiple files is because the computation is distributed. If the output is small enough such that you think you can fit it on one machine, then you can end your program with
val arr = year.collect()
And then save the resulting array as a file, Another way would be to use a custom partitioner, partitionBy, and make it so everything goes to one partition though that isn't advisable because you won't get any parallelization.
If you require the file to be saved with saveAsTextFile you can use coalesce(1,true).saveAsTextFile(). This basically means do the computation then coalesce to 1 partition. You can also use repartition(1) which is just a wrapper for coalesce with the shuffle argument set to true. Looking through the source of RDD.scala is how I figured most of this stuff out, you should take a look.