Can sparklyr be used with spark deployed on yarn-managed hadoop cluster?

Question

Is the sparklyr R package able to connect to YARN-managed hadoop clusters? This doesn't seem to be documented in the cluster deployment documentation. Using the SparkR package that ships with Spark it is possible by doing:

# set R environment variables
Sys.setenv(YARN_CONF_DIR=...)
Sys.setenv(SPARK_CONF_DIR=...)
Sys.setenv(LD_LIBRARY_PATH=...)
Sys.setenv(SPARKR_SUBMIT_ARGS=...)

spark_lib_dir <- ... # install specific
library(SparkR, lib.loc = c(sparkr_lib_dir, .libPaths()))
sc <- sparkR.init(master = "yarn-client")

However when I swaped the last lines above with

library(sparklyr)
sc <- spark_connect(master = "yarn-client")

I get errors:

Error in start_shell(scon, list(), jars, packages) : 
  Failed to launch Spark shell. Ports file does not exist.
    Path: /usr/hdp/2.4.2.0-258/spark/bin/spark-submit
    Parameters: '--packages' 'com.databricks:spark-csv_2.11:1.3.0,com.amazonaws:aws-java-sdk-pom:1.10.34' '--jars' '<path to R lib>/3.2/sparklyr/java/rspark_utils.jar'  sparkr-shell /tmp/RtmpT31OQT/filecfb07d7f8bfd.out

Ivy Default Cache set to: /home/mpollock/.ivy2/cache
The jars for the packages stored in: /home/mpollock/.ivy2/jars
:: loading settings :: url = jar:file:<path to spark install>/lib/spark-assembly-1.6.1.2.4.2.0-258-hadoop2.7.1.2.4.2.0-258.jar!/org/apache/ivy/core/settings/ivysettings.xml
com.databricks#spark-csv_2.11 added as a dependency
com.amazonaws#aws-java-sdk-pom added as a dependency
:: resolving dependencies :: org.apache.spark#spark-submit-parent;1.0
    confs: [default]
:: resolution report :: resolve 480ms :: artifacts dl 0ms
    :: modules in use:
    -----------------------------------------

Is sparklyr an alternative to SparkR or is it built on top of the SparkR package?

Answer 1

Yes, sparklyr can be used against a yarn-managed cluster. In order to connect to yarn-managed clusters one needs to:

1. Set SPARK_HOME environment variable to point to the right spark home directory.
2. Connect to the spark cluster using the appropriate master location, for instance: sc <- spark_connect(master = "yarn-client")

See also: http://spark.rstudio.com/deployment.html

Answer 2

Yes it can but there is one catch to everything else that has been written, which is very elusive in the blogging literature, and that centers around configuring the resources.

The key is this: when you have it executing in local mode you do not have to configure the resources declaratively, but when you execute in the YARN cluster, you absolutely do have to declare those resources. It took me a long time to find the article that shed some light on this issue but once I tried it, it Worked.

Here's an (arbitrary) example with the key reference:

config <- spark_config()
config$spark.driver.cores <- 32
config$spark.executor.cores <- 32
config$spark.executor.memory <- "40g"

library(sparklyr)

Sys.setenv(SPARK_HOME = "/usr/local/spark")
Sys.setenv(HADOOP_CONF_DIR = '/usr/local/hadoop/etc/hadoop/conf')
Sys.setenv(YARN_CONF_DIR = '/usr/local/hadoop/etc/hadoop/conf')

config <- spark_config()
config$spark.executor.instances <- 4
config$spark.executor.cores <- 4
config$spark.executor.memory <- "4G"

sc <- spark_connect(master="yarn-client", config=config, version = '2.1.0')

R Bloggers Link to Article