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 Spark

Answer 1

An upgrade to sparklyr version 0.2.30 or newer is recommended for this issue. Upgrade using devtools::install_github("rstudio/sparklyr") followed by restarting the r session.

Answer 2

Are you possibly using Cloudera Hadoop (CDH)?

I am asking as I had the same issue when using the CDH-provided Spark distro:

Sys.getenv('SPARK_HOME')
[1] "/usr/lib/spark"  # CDH-provided Spark
library(sparklyr)
sc <- spark_connect(master = "yarn-client")
Error in sparkapi::start_shell(master = master, spark_home = spark_home,  : 
      Failed to launch Spark shell. Ports file does not exist.
        Path: /usr/lib/spark/bin/spark-submit
        Parameters: --jars, '/u01/app/oracle/product/12.1.0.2/dbhome_1/R/library/sparklyr/java/sparklyr.jar', --packages, 'com.databricks:spark-csv_2.11:1.3.0','com.amazonaws:aws-java-sdk-pom:1.10.34', sparkr-shell, /tmp/Rtmp6RwEnV/file307975dc1ea0.out

Ivy Default Cache set to: /home/oracle/.ivy2/cache
The jars for the packages stored in: /home/oracle/.ivy2/jars
:: loading settings :: url = jar:file:/usr/lib/spark/lib/spark-assembly-1.6.0-cdh5.7.0-hadoop2.6.0-cdh5.7.0.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]
    found com.databricks#spark-csv_2.11;1.3.0 in central
    found org.apache.commons#commons-csv;1.1 in central
    found com.univocity#univocity-parsers;1.5.1 in central
    found com.

However, after I downloaded a pre-built version from Databricks (Spark 1.6.1, Hadoop 2.6) and pointed SPARK_HOME there, I was able to connect successfully:

Sys.setenv(SPARK_HOME = '/home/oracle/spark-1.6.1-bin-hadoop2.6') 
sc <- spark_connect(master = "yarn-client") # OK
library(dplyr)
iris_tbl <- copy_to(sc, iris)
src_tbls(sc)
[1] "iris"

Cloudera does not yet include SparkR in its distribution, and I suspect that sparklyr may still have some subtle dependency on SparkR. Here are the results when trying to work with the CDH-provided Spark, but using the config=list() argument, as suggested in this thread from sparklyr issues at Github:

sc <- spark_connect(master='yarn-client', config=list()) # with CDH-provided Spark
Error in sparkapi::start_shell(master = master, spark_home = spark_home,  : 
  Failed to launch Spark shell. Ports file does not exist.
    Path: /usr/lib/spark/bin/spark-submit
    Parameters: --jars, '/u01/app/oracle/product/12.1.0.2/dbhome_1/R/library/sparklyr/java/sparklyr.jar', sparkr-shell, /tmp/Rtmpi9KWFt/file22276cf51d90.out

Error: sparkr.zip does not exist for R application in YARN mode.

Also, if you check the rightmost part of the Parameters part of the error (both yours and mine), you'll see a reference to sparkr-shell...

(Tested with sparklyr 0.2.28, sparkapi 0.3.15, R session from RStudio Server, Oracle Linux)

Answer 3

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

Answer 4

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