spark_read_text {sparklyr} | R Documentation |
Read a text file into a Spark DataFrame.
spark_read_text(sc, name, path, repartition = 0, memory = TRUE, overwrite = TRUE, ...)
sc |
A |
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://", "s3n://" 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? |
... |
Optional arguments; currently unused. |
You can read data from HDFS (hdfs://
), S3 (s3n://
), as well as
the local file system (file://
).
If you are reading from a secure S3 bucket be sure that the AWS_ACCESS_KEY_ID
and
AWS_SECRET_ACCESS_KEY
environment variables are both defined.
Other Spark serialization routines: spark_load_table
,
spark_read_csv
,
spark_read_jdbc
,
spark_read_json
,
spark_read_parquet
,
spark_read_source
,
spark_read_table
,
spark_save_table
,
spark_write_csv
,
spark_write_jdbc
,
spark_write_json
,
spark_write_parquet
,
spark_write_source
,
spark_write_table
,
spark_write_text