Manage Spark Connections — spark-connections • sparklyr

These routines allow you to manage your connections to Spark.

Call `spark_disconnect()` on each open Spark connection

Usage

spark_connect(
  master,
  spark_home = Sys.getenv("SPARK_HOME"),
  method = c("shell", "livy", "databricks", "test", "qubole", "synapse"),
  app_name = "sparklyr",
  version = NULL,
  config = spark_config(),
  extensions = sparklyr::registered_extensions(),
  packages = NULL,
  scala_version = NULL,
  ...
)

spark_connection_is_open(sc)

spark_disconnect(sc, ...)

spark_disconnect_all(...)

spark_submit(
  master,
  file,
  spark_home = Sys.getenv("SPARK_HOME"),
  app_name = "sparklyr",
  version = NULL,
  config = spark_config(),
  extensions = sparklyr::registered_extensions(),
  scala_version = NULL,
  ...
)

Arguments

master: Spark cluster url to connect to. Use "local" to connect to a local instance of Spark installed via spark_install.
spark_home: The path to a Spark installation. Defaults to the path provided by the SPARK_HOME environment variable. If SPARK_HOME is defined, it will always be used unless the version parameter is specified to force the use of a locally installed version.
method: The method used to connect to Spark. Default connection method is "shell" to connect using spark-submit, use "livy" to perform remote connections using HTTP, or "databricks" when using a Databricks clusters.
app_name: The application name to be used while running in the Spark cluster.
version: The version of Spark to use. Required for "local" Spark connections, optional otherwise.
config: Custom configuration for the generated Spark connection. See spark_config for details.
extensions: Extension R packages to enable for this connection. By default, all packages enabled through the use of sparklyr::register_extension will be passed here.
packages: A list of Spark packages to load. For example, "delta" or "kafka" to enable Delta Lake or Kafka. Also supports full versions like "io.delta:delta-core_2.11:0.4.0". This is similar to adding packages into the sparklyr.shell.packages configuration option. Notice that the version parameter is used to choose the correct package, otherwise assumes the latest version is being used.
scala_version: Load the sparklyr jar file that is built with the version of Scala specified (this currently only makes sense for Spark 2.4, where sparklyr will by default assume Spark 2.4 on current host is built with Scala 2.11, and therefore `scala_version = '2.12'` is needed if sparklyr is connecting to Spark 2.4 built with Scala 2.12)
...: Additional params to be passed to each `spark_disconnect()` call (e.g., `terminate = TRUE`)
sc: A spark_connection.
file: Path to R source file to submit for batch execution.

Details

By default, when using method = "livy", jars are downloaded from GitHub. But an alternative path (local to Livy server or on HDFS or HTTP(s)) to sparklyr JAR can also be specified through the sparklyr.livy.jar setting.

Examples

conf <- spark_config()
conf$`sparklyr.shell.conf` <- c(
  "spark.executor.extraJavaOptions=-Duser.timezone='UTC'",
  "spark.driver.extraJavaOptions=-Duser.timezone='UTC'",
  "spark.sql.session.timeZone='UTC'"
)

sc <- spark_connect(
  master = "spark://HOST:PORT", config = conf
)
connection_is_open(sc)
#> [1] TRUE

spark_disconnect(sc)