spark_load_table
Reads from a Spark Table into a Spark DataFrame.
Reads from a Spark Table into a Spark DataFrame.
Usage
spark_load_table(sc, name, path, options = list(), repartition = 0,
memory = TRUE, overwrite = TRUE)Arguments
- 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.
- options
A list of strings with additional options. See http://spark.apache.org/docs/latest/sql-programming-guide.html#configuration.
- 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: spark_read_csv,
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_csv,
spark_write_jdbc,
spark_write_json,
spark_write_orc,
spark_write_parquet,
spark_write_source,
spark_write_table,
spark_write_text