Read binary data into a Spark DataFrame.

Read binary files within a directory and convert each file into a record within the resulting Spark dataframe. The output will be a Spark dataframe with the following columns and possibly partition columns:

path: StringType
modificationTime: TimestampType
length: LongType
content: BinaryType

spark_read_binary(
  sc,
  name = NULL,
  dir = name,
  path_glob_filter = "*",
  recursive_file_lookup = FALSE,
  repartition = 0,
  memory = TRUE,
  overwrite = TRUE
)

Arguments

sc: A spark_connection.
name: The name to assign to the newly generated table.
dir: Directory to read binary files from.
path_glob_filter: Glob pattern of binary files to be loaded (e.g., "*.jpg").
recursive_file_lookup: If FALSE (default), then partition discovery will be enabled (i.e., if a partition naming scheme is present, then partitions specified by subdirectory names such as "date=2019-07-01" will be created and files outside subdirectories following a partition naming scheme will be ignored). If TRUE, then all nested directories will be searched even if their names do not follow a partition naming scheme.
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?

Other Spark serialization routines: collect_from_rds(), spark_insert_table(), spark_load_table(), spark_read(), spark_read_avro(), 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_source(), spark_write_table(), spark_write_text()

Arguments

See also