View source: R/data_interface.R
spark_read_binary | R Documentation |
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
)
sc |
A |
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()
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