[SPARK-11340][SPARKR] Support setting driver properties when starting Spark from R programmatically or from RStudio

Mapping spark.driver.memory from sparkEnvir to spark-submit commandline arguments.

shivaram suggested that we possibly add other spark.driver.* properties - do we want to add all of those? I thought those could be set in SparkConf?
sun-rui

Author: felixcheung <felixcheung_m@hotmail.com>

Closes #9290 from felixcheung/rdrivermem.

3 files changed, 87 insertions, 13 deletions

diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R
index 043b0057bd..004d08e74e 100644
--- a/R/pkg/R/sparkR.R
+++ b/R/pkg/R/sparkR.R
@@ -77,7 +77,9 @@ sparkR.stop <- function() {
 
 #' Initialize a new Spark Context.
 #'
-#' This function initializes a new SparkContext.
+#' This function initializes a new SparkContext. For details on how to initialize
+#' and use SparkR, refer to SparkR programming guide at 
+#' \url{http://spark.apache.org/docs/latest/sparkr.html#starting-up-sparkcontext-sqlcontext}.
 #'
 #' @param master The Spark master URL.
 #' @param appName Application name to register with cluster manager
@@ -93,7 +95,7 @@ sparkR.stop <- function() {
 #' sc <- sparkR.init("local[2]", "SparkR", "/home/spark",
 #'                  list(spark.executor.memory="1g"))
 #' sc <- sparkR.init("yarn-client", "SparkR", "/home/spark",
-#'                  list(spark.executor.memory="1g"),
+#'                  list(spark.executor.memory="4g"),
 #'                  list(LD_LIBRARY_PATH="/directory of JVM libraries (libjvm.so) on workers/"),
 #'                  c("jarfile1.jar","jarfile2.jar"))
 #'}
@@ -123,16 +125,21 @@ sparkR.init <- function(
     uriSep <- "////"
   }
 
+  sparkEnvirMap <- convertNamedListToEnv(sparkEnvir)
+
   existingPort <- Sys.getenv("EXISTING_SPARKR_BACKEND_PORT", "")
   if (existingPort != "") {
     backendPort <- existingPort
   } else {
     path <- tempfile(pattern = "backend_port")
+    submitOps <- getClientModeSparkSubmitOpts(
+        Sys.getenv("SPARKR_SUBMIT_ARGS", "sparkr-shell"),
+        sparkEnvirMap)
     launchBackend(
         args = path,
         sparkHome = sparkHome,
         jars = jars,
-        sparkSubmitOpts = Sys.getenv("SPARKR_SUBMIT_ARGS", "sparkr-shell"),
+        sparkSubmitOpts = submitOps,
         packages = sparkPackages)
     # wait atmost 100 seconds for JVM to launch
     wait <- 0.1
@@ -171,8 +178,6 @@ sparkR.init <- function(
     sparkHome <- suppressWarnings(normalizePath(sparkHome))
   }
 
-  sparkEnvirMap <- convertNamedListToEnv(sparkEnvir)
-
   sparkExecutorEnvMap <- convertNamedListToEnv(sparkExecutorEnv)
   if(is.null(sparkExecutorEnvMap$LD_LIBRARY_PATH)) {
     sparkExecutorEnvMap[["LD_LIBRARY_PATH"]] <-
@@ -320,3 +325,33 @@ clearJobGroup <- function(sc) {
 cancelJobGroup <- function(sc, groupId) {
   callJMethod(sc, "cancelJobGroup", groupId)
 }
+
+sparkConfToSubmitOps <- new.env()
+sparkConfToSubmitOps[["spark.driver.memory"]]           <- "--driver-memory"
+sparkConfToSubmitOps[["spark.driver.extraClassPath"]]   <- "--driver-class-path"
+sparkConfToSubmitOps[["spark.driver.extraJavaOptions"]] <- "--driver-java-options"
+sparkConfToSubmitOps[["spark.driver.extraLibraryPath"]] <- "--driver-library-path"
+
+# Utility function that returns Spark Submit arguments as a string
+#
+# A few Spark Application and Runtime environment properties cannot take effect after driver
+# JVM has started, as documented in:
+# http://spark.apache.org/docs/latest/configuration.html#application-properties
+# When starting SparkR without using spark-submit, for example, from Rstudio, add them to
+# spark-submit commandline if not already set in SPARKR_SUBMIT_ARGS so that they can be effective.
+getClientModeSparkSubmitOpts <- function(submitOps, sparkEnvirMap) {
+  envirToOps <- lapply(ls(sparkConfToSubmitOps), function(conf) {
+    opsValue <- sparkEnvirMap[[conf]]
+    # process only if --option is not already specified
+    if (!is.null(opsValue) &&
+        nchar(opsValue) > 1 &&
+        !grepl(sparkConfToSubmitOps[[conf]], submitOps)) {
+      # put "" around value in case it has spaces
+      paste0(sparkConfToSubmitOps[[conf]], " \"", opsValue, "\" ")
+    } else {
+      ""
+    }
+  })
+  # --option must be before the application class "sparkr-shell" in submitOps
+  paste0(paste0(envirToOps, collapse = ""), submitOps)
+}
diff --git a/R/pkg/inst/tests/test_context.R b/R/pkg/inst/tests/test_context.R
index e99815ed15..80c1b89a4c 100644
--- a/R/pkg/inst/tests/test_context.R
+++ b/R/pkg/inst/tests/test_context.R
@@ -65,3 +65,30 @@ test_that("job group functions can be called", {
   cancelJobGroup(sc, "groupId")
   clearJobGroup(sc)
 })
+
+test_that("getClientModeSparkSubmitOpts() returns spark-submit args from whitelist", {
+  e <- new.env()
+  e[["spark.driver.memory"]] <- "512m"
+  ops <- getClientModeSparkSubmitOpts("sparkrmain", e)
+  expect_equal("--driver-memory \"512m\" sparkrmain", ops)
+
+  e[["spark.driver.memory"]] <- "5g"
+  e[["spark.driver.extraClassPath"]] <- "/opt/class_path" # nolint
+  e[["spark.driver.extraJavaOptions"]] <- "-XX:+UseCompressedOops -XX:+UseCompressedStrings"
+  e[["spark.driver.extraLibraryPath"]] <- "/usr/local/hadoop/lib" # nolint
+  e[["random"]] <- "skipthis"
+  ops2 <- getClientModeSparkSubmitOpts("sparkr-shell", e)
+  # nolint start
+  expect_equal(ops2, paste0("--driver-class-path \"/opt/class_path\" --driver-java-options \"",
+                      "-XX:+UseCompressedOops -XX:+UseCompressedStrings\" --driver-library-path \"",
+                      "/usr/local/hadoop/lib\" --driver-memory \"5g\" sparkr-shell"))
+  # nolint end
+
+  e[["spark.driver.extraClassPath"]] <- "/" # too short
+  ops3 <- getClientModeSparkSubmitOpts("--driver-memory 4g sparkr-shell2", e)
+  # nolint start
+  expect_equal(ops3, paste0("--driver-java-options \"-XX:+UseCompressedOops ",
+                      "-XX:+UseCompressedStrings\" --driver-library-path \"/usr/local/hadoop/lib\"",
+                      " --driver-memory 4g sparkr-shell2"))
+  # nolint end
+})
diff --git a/docs/sparkr.md b/docs/sparkr.md
index 7139d16b4a..497a276679 100644
--- a/docs/sparkr.md
+++ b/docs/sparkr.md
@@ -29,7 +29,7 @@ All of the examples on this page use sample data included in R or the Spark dist
 The entry point into SparkR is the `SparkContext` which connects your R program to a Spark cluster.
 You can create a `SparkContext` using `sparkR.init` and pass in options such as the application name
 , any spark packages depended on, etc. Further, to work with DataFrames we will need a `SQLContext`,
-which can be created from the  SparkContext. If you are working from the SparkR shell, the
+which can be created from the  SparkContext. If you are working from the `sparkR` shell, the
 `SQLContext` and `SparkContext` should already be created for you.
 
 {% highlight r %}
@@ -37,17 +37,29 @@ sc <- sparkR.init()
 sqlContext <- sparkRSQL.init(sc)
 {% endhighlight %}
 
+In the event you are creating `SparkContext` instead of using `sparkR` shell or `spark-submit`, you 
+could also specify certain Spark driver properties. Normally these
+[Application properties](configuration.html#application-properties) and
+[Runtime Environment](configuration.html#runtime-environment) cannot be set programmatically, as the
+driver JVM process would have been started, in this case SparkR takes care of this for you. To set
+them, pass them as you would other configuration properties in the `sparkEnvir` argument to
+`sparkR.init()`.
+
+{% highlight r %}
+sc <- sparkR.init("local[*]", "SparkR", "/home/spark", list(spark.driver.memory="2g"))
+{% endhighlight %}
+
 </div>
 
 ## Creating DataFrames
 With a `SQLContext`, applications can create `DataFrame`s from a local R data frame, from a [Hive table](sql-programming-guide.html#hive-tables), or from other [data sources](sql-programming-guide.html#data-sources).
 
 ### From local data frames
-The simplest way to create a data frame is to convert a local R data frame into a SparkR DataFrame. Specifically we can use `createDataFrame` and pass in the local R data frame to create a SparkR DataFrame. As an example, the following creates a `DataFrame` based using the `faithful` dataset from R. 
+The simplest way to create a data frame is to convert a local R data frame into a SparkR DataFrame. Specifically we can use `createDataFrame` and pass in the local R data frame to create a SparkR DataFrame. As an example, the following creates a `DataFrame` based using the `faithful` dataset from R.
 
 <div data-lang="r"  markdown="1">
 {% highlight r %}
-df <- createDataFrame(sqlContext, faithful) 
+df <- createDataFrame(sqlContext, faithful)
 
 # Displays the content of the DataFrame to stdout
 head(df)
@@ -96,7 +108,7 @@ printSchema(people)
 </div>
 
 The data sources API can also be used to save out DataFrames into multiple file formats. For example we can save the DataFrame from the previous example
-to a Parquet file using `write.df` 
+to a Parquet file using `write.df`
 
 <div data-lang="r"  markdown="1">
 {% highlight r %}
@@ -139,7 +151,7 @@ Here we include some basic examples and a complete list can be found in the [API
 <div data-lang="r"  markdown="1">
 {% highlight r %}
 # Create the DataFrame
-df <- createDataFrame(sqlContext, faithful) 
+df <- createDataFrame(sqlContext, faithful)
 
 # Get basic information about the DataFrame
 df
@@ -152,7 +164,7 @@ head(select(df, df$eruptions))
 ##2     1.800
 ##3     3.333
 
-# You can also pass in column name as strings 
+# You can also pass in column name as strings
 head(select(df, "eruptions"))
 
 # Filter the DataFrame to only retain rows with wait times shorter than 50 mins
@@ -166,7 +178,7 @@ head(filter(df, df$waiting < 50))
 
 </div>
 
-### Grouping, Aggregation 
+### Grouping, Aggregation
 
 SparkR data frames support a number of commonly used functions to aggregate data after grouping. For example we can compute a histogram of the `waiting` time in the `faithful` dataset as shown below
 
@@ -194,7 +206,7 @@ head(arrange(waiting_counts, desc(waiting_counts$count)))
 
 ### Operating on Columns
 
-SparkR also provides a number of functions that can directly applied to columns for data processing and during aggregation. The example below shows the use of basic arithmetic functions. 
+SparkR also provides a number of functions that can directly applied to columns for data processing and during aggregation. The example below shows the use of basic arithmetic functions.
 
 <div data-lang="r"  markdown="1">
 {% highlight r %}