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


  name = NULL, 
  dir = name, 
  path_glob_filter = "*", 
  recursive_file_lookup = FALSE, 
  repartition = 0, 
  memory = TRUE, 
  overwrite = TRUE 


Arguments Description
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?

See Also

Other Spark serialization routines: collect_from_rds(), spark_insert_table(), spark_load_table(), 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_read(), 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()