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Read a Text file into a Spark DataFrame

Source: R/data_interface.R
spark_read_text.Rd

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()