Read binary data into a Spark DataFrame.
R/data_interface.R
spark_read_binary
Description
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
Usage
spark_read_binary(
sc,
name = NULL,
dir = name,
path_glob_filter = "*",
recursive_file_lookup = FALSE,
repartition = 0,
memory = TRUE,
overwrite = TRUE
) Arguments
| 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()