Write a Spark DataFrame to a tabular (typically, comma-separated) file.
spark_write_csv(x, path, header = TRUE, delimiter = ",", quote = "\"",
escape = "\\", charset = "UTF-8", null_value = NULL,
options = list(), mode = NULL, partition_by = NULL, ...)A Spark DataFrame or dplyr operation
The path to the file. Needs to be accessible from the cluster. Supports the "hdfs://", "s3a://" and "file://" protocols.
Should the first row of data be used as a header? Defaults to TRUE.
The character used to delimit each column, defaults to ,.
The character used as a quote. Defaults to '"'.
The character used to escape other characters, defaults to \.
The character set, defaults to "UTF-8".
The character to use for default values, defaults to NULL.
A list of strings with additional options.
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 http://spark.apache.org/docs/latest/sql-programming-guide.html#save-modes for your version of Spark.
A character vector. Partitions the output by the given columns on the file system.
Optional arguments; currently unused.
Other Spark serialization routines: spark_load_table,
spark_read_csv,
spark_read_jdbc,
spark_read_json,
spark_read_libsvm,
spark_read_parquet,
spark_read_source,
spark_read_table,
spark_read_text,
spark_save_table,
spark_write_jdbc,
spark_write_json,
spark_write_parquet,
spark_write_source,
spark_write_table,
spark_write_text