sdf_copy_to

Copy an Object into Spark

Description

Copy an object into Spark, and return an object wrapping the copied object (typically, a Spark DataFrame).

Usage

sdf_copy_to(sc, x, name, memory, repartition, overwrite, struct_columns, ...)

sdf_import(x, sc, name, memory, repartition, overwrite, struct_columns, ...)

Arguments

Argument Description
sc The associated Spark connection.
x An object from which a Spark DataFrame can be generated.
name The name to assign to the copied table in Spark.
memory Boolean; should the table be cached into memory?
repartition The number of partitions to use when distributing the table across the Spark cluster. The default (0) can be used to avoid partitioning.
overwrite Boolean; overwrite a pre-existing table with the name name if one already exists?
struct_columns (only supported with Spark 2.4.0 or higher) A list of columns from the source data frame that should be converted to Spark SQL StructType columns. The source columns can contain either json strings or nested lists. All rows within each source column should have identical schemas (because otherwise the conversion result will contain unexpected null values or missing values as Spark currently does not support schema discovery on individual rows within a struct column).
Optional arguments, passed to implementing methods.

Examples


sc <- spark_connect(master = "spark://HOST:PORT")
sdf_copy_to(sc, iris)

See Also

Other Spark data frames: sdf_distinct(), sdf_random_split(), sdf_register(), sdf_sample(), sdf_sort(), sdf_weighted_sample()