spark_load_table
Reads from a Spark Table into a Spark DataFrame.
Description
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
| Argument | 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. options | A list of strings with additional options. See https://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: collect_from_rds(), 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_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()