| sparkR.session {SparkR} | R Documentation |
SparkSession is the entry point into SparkR. sparkR.session gets the existing
SparkSession or initializes a new SparkSession.
Additional Spark properties can be set in ..., and these named parameters take priority
over values in master, appName, named lists of sparkConfig.
sparkR.session(master = "", appName = "SparkR",
sparkHome = Sys.getenv("SPARK_HOME"), sparkConfig = list(),
sparkJars = "", sparkPackages = "", enableHiveSupport = TRUE, ...)
master |
the Spark master URL. |
appName |
application name to register with cluster manager. |
sparkHome |
Spark Home directory. |
sparkConfig |
named list of Spark configuration to set on worker nodes. |
sparkJars |
character vector of jar files to pass to the worker nodes. |
sparkPackages |
character vector of package coordinates |
enableHiveSupport |
enable support for Hive, fallback if not built with Hive support; once set, this cannot be turned off on an existing session |
... |
named Spark properties passed to the method. |
When called in an interactive session, this method checks for the Spark installation, and, if not
found, it will be downloaded and cached automatically. Alternatively, install.spark can
be called manually.
A default warehouse is created automatically in the current directory when a managed table is
created via sql statement CREATE TABLE, for example. To change the location of the
warehouse, set the named parameter spark.sql.warehouse.dir to the SparkSession. Along with
the warehouse, an accompanied metastore may also be automatically created in the current
directory when a new SparkSession is initialized with enableHiveSupport set to
TRUE, which is the default. For more details, refer to Hive configuration at
http://spark.apache.org/docs/latest/sql-programming-guide.html#hive-tables.
For details on how to initialize and use SparkR, refer to SparkR programming guide at http://spark.apache.org/docs/latest/sparkr.html#starting-up-sparksession.
sparkR.session since 2.0.0
## Not run:
##D sparkR.session()
##D df <- read.json(path)
##D
##D sparkR.session("local[2]", "SparkR", "/home/spark")
##D sparkR.session("yarn", "SparkR", "/home/spark",
##D list(spark.executor.memory="4g", spark.submit.deployMode="client"),
##D c("one.jar", "two.jar", "three.jar"),
##D c("com.databricks:spark-avro_2.12:2.0.1"))
##D sparkR.session(spark.master = "yarn", spark.submit.deployMode = "client",
## End(Not run)