spark_read_table
Reads from a Spark Table into a Spark DataFrame.
Reads from a Spark Table into a Spark DataFrame.
Usage
spark_read_table(
sc,
name,
options = list(),
repartition = 0,
memory = TRUE,
columns = NULL,
...
)Arguments
- sc
A
spark_connection.- name
The name to assign to the newly generated table.
- 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?)
- columns
A vector of column names or a named vector of column types. If specified, the elements can be
"binary"forBinaryType,"boolean"forBooleanType,"byte"forByteType,"integer"forIntegerType,"integer64"forLongType,"double"forDoubleType,"character"forStringType,"timestamp"forTimestampTypeand"date"forDateType.- ...
Optional arguments; currently unused.
See Also
Other Spark serialization routines:
spark_load_table(),
spark_read_avro(),
spark_read_csv(),
spark_read_delta(),
spark_read_jdbc(),
spark_read_json(),
spark_read_libsvm(),
spark_read_orc(),
spark_read_parquet(),
spark_read_source(),
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()