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"
forTimestampType
and"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()