spark_write_source: Writes a Spark DataFrame into a generic source

In sparklyr: R Interface to Apache Spark

Writes a Spark DataFrame into a generic source

Description

Writes a Spark DataFrame into a generic source.

Usage

spark_write_source(
  x,
  source,
  mode = NULL,
  options = list(),
  partition_by = NULL,
  ...
)

Arguments

x	A Spark DataFrame or dplyr operation
source	A data source capable of reading data.
mode	A character element. Specifies the behavior when data or table already exists. Supported values include: 'error', 'append', 'overwrite' and ignore. Notice that 'overwrite' will also change the column structure. For more details see also https://spark.apache.org/docs/latest/sql-programming-guide.html#save-modes for your version of Spark.
options	A list of strings with additional options.
partition_by	A character vector. Partitions the output by the given columns on the file system.
...	Optional arguments; currently unused.

See Also

Other Spark serialization routines: collect_from_rds(), spark_insert_table(), spark_load_table(), spark_read(), spark_read_avro(), spark_read_binary(), spark_read_csv(), spark_read_delta(), spark_read_image(), spark_read_jdbc(), spark_read_json(), spark_read_libsvm(), spark_read_orc(), spark_read_parquet(), spark_read_source(), spark_read_table(), spark_read_text(), spark_save_table(), spark_write_avro(), spark_write_csv(), spark_write_delta(), spark_write_jdbc(), spark_write_json(), spark_write_orc(), spark_write_parquet(), spark_write_table(), spark_write_text()

sparklyr documentation built on May 29, 2024, 2:58 a.m.