Read a Text file into a Spark DataFrame
spark_read_text
Description
Read a Text file into a Spark DataFrame
Usage
spark_read_text(
sc,
name = NULL,
path = name,
repartition = 0,
memory = TRUE,
overwrite = TRUE,
options = list(),
whole = FALSE,
...
)Arguments
| Arguments | Description |
|---|---|
| sc | A spark_connection. |
| name | The name to assign to the newly generated table. |
| path | The path to the file. Needs to be accessible from the cluster. Supports the "hdfs://", "s3a://" and "file://" protocols. |
| 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? |
| options | A list of strings with additional options. |
| whole | Read the entire text file as a single entry? Defaults to FALSE. |
| … | Optional arguments; currently unused. |
Details
You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://).
See Also
Other Spark serialization routines: collect_from_rds(), spark_insert_table(), spark_load_table(), spark_read(), spark_read_avro(), spark_read_binary(), 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_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()