I have a Spark standalone cluster with 2 workers -
Master and one slave thread run on a single machine -- Machine 1
Another slave running on a separate machine -- Machine 2
I am running a spark shell in the 2nd machine that reads a file from hdfs
and does some calculations on them and stores the result in hdfs.
This is how I read the file in spark shell -
val file = sc.textFile("hdfs://localhost:9000/user/root/table.csv")
And this is how I write the result back to a file -
finalRDD.saveAsTextFile("hdfs://localhost:9000/user/root/output_file")
When I run the code, it runs in the cluster and the job succeeds with each
worker processing roughly half of the input file. I am also able to see the
records processed in the webUI.
But when I check HDFS in the 2nd machine, I find only one part of the output
file.
The other part is stored in the hdfs in the 1st machine. But even the part
is not actually present in the proper hdfs location and is instead stored in
a _temporary directory
In machine 2 -
root@worker:~# hadoop fs -ls ./output_file
Found 2 items
-rw-r--r-- 3 root supergroup 0 2015-07-06 16:12
output_file/_SUCCESS
-rw-r--r-- 3 root supergroup 984337 2015-07-06 16:12
output_file/part-00000
In machine 1 -
root@spark:~# hadoop fs -ls
./output_file/_temporary/0/task_201507061612_0003_m_000001
-rw-r--r-- 3 root supergroup 971824 2015-07-06 16:12
output_file/_temporary/0/
task_201507061612_0003_m_000001/part-00001
I have a couple of questions -
1. Shouldn't both parts be on the worker 2 ( since the hdfs referred to in
the saveAsTextFile is the local hdfs) ? OR will the output be always split
in the workers ?
2. Why is the output stored in the _temporary directory in machine 1 ?
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