Read libsvm file into a Spark DataFrame.
spark_read_libsvm(
sc,
name = NULL,
path = name,
repartition = 0,
memory = TRUE,
overwrite = TRUE,
options = list(),
...
)
A spark_connection
.
The name to assign to the newly generated table.
The path to the file. Needs to be accessible from the cluster. Supports the "hdfs://", "s3a://" and "file://" protocols.
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?
A list of strings with additional options.
Optional arguments; currently unused.
Other Spark serialization routines:
collect_from_rds()
,
spark_insert_table()
,
spark_load_table()
,
spark_read_avro()
,
spark_read_binary()
,
spark_read_csv()
,
spark_read_delta()
,
spark_read_image()
,
spark_read_jdbc()
,
spark_read_json()
,
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()