Reputation: 327
I am trying to use spark_apply_bundle
to limit the number of packages/data
transferred to the worker nodes on a YARN
managed cluster. As mentioned in here I must pass the path of the tarball
to spark_apply
as the packages argument and I also must make it available via "sparklyr.shell.files"
in the spark config.
My questions are:
"sparklyr.shell.files"
? Is it a duplicate of the path passed to spark_apply
?Currently my unsuccessful script look something like this:
bundle <- paste(getwd(), list.files()[grep("\\.tar$",list.files())][1], sep = "/")
...
config$sparklyr.shell.files <- bundle
sc <- spark_connect(master = "yarn-client", config = config)
...
spark_apply(sdf, f, packages = bundle)
Upvotes: 3
Views: 468
Reputation: 327
The spark job succeeded by copying the tarball to hdfs. It seems as if it's plausible to use some other method (e.g. copying the file to each worker node) but this seems to be the easiest solution.
The updated script looks as follows:
bundle <- paste(getwd(), list.files()[grep("\\.tar$",list.files())][1], sep = "/")
...
hdfs_path <- "hdfs://nn.example.com/some/directory/"
hdfs_bundle <- paste0(hdfs_path, basename(bundle))
system(paste("hdfs dfs -put", bundle, hdfs_path))
config$sparklyr.shell.files <- hdfs_bundle
sc <- spark_connect(master = "yarn-client", config = config)
...
spark_apply(sdf, f, packages = bundle)
Upvotes: 2