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
)A spark_connection.
The name to assign to the newly generated table.
Directory to read binary files from.
Glob pattern of binary files to be loaded (e.g., "*.jpg").
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.
The number of partitions used to distribute the generated table. Use 0 (the default) to avoid partitioning.
Boolean; should the data be loaded eagerly into memory? (That is, should the table be cached?)
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_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()