spark_read_text: Read a Text file into a Spark DataFrame

In sparklyr: R Interface to Apache Spark

Read a Text file into a Spark DataFrame

Description

Read a Text file into a Spark DataFrame

Usage

spark_read_text(
  sc,
  name = NULL,
  path = name,
  repartition = 0,
  memory = TRUE,
  overwrite = TRUE,
  options = list(),
  whole = FALSE,
  ...
)

Arguments

sc	A spark_connection.
name	The name to assign to the newly generated table.
path	The path to the file. Needs to be accessible from the cluster. Supports the ‘⁠"hdfs://"⁠’, ‘⁠"s3a://"⁠’ and ‘⁠"file://"⁠’ protocols.
repartition	The number of partitions used to distribute the generated table. Use 0 (the default) to avoid partitioning.
memory	Boolean; should the data be loaded eagerly into memory? (That is, should the table be cached?)
overwrite	Boolean; overwrite the table with the given name if it already exists?
options	A list of strings with additional options.
whole	Read the entire text file as a single entry? Defaults to FALSE.
...	Optional arguments; currently unused.

Details

You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://).

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_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_source(), spark_write_table(), spark_write_text()

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