Matt Pollock Matt Pollock - 2 months ago 102
R Question

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

Is the

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

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

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