Read from JDBC connection into a Spark DataFrame.
Read from JDBC connection into a Spark DataFrame.
spark_read_jdbc( sc, name, options = list(), repartition = 0, memory = TRUE, overwrite = TRUE, columns = NULL, ... )
sc |
A |
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?) |
overwrite |
Boolean; overwrite the table with the given name if it already exists? |
columns |
A vector of column names or a named vector of column types.
If specified, the elements can be |
... |
Optional arguments; currently unused. |
Other Spark serialization routines:
collect_from_rds(),
spark_load_table(),
spark_read_avro(),
spark_read_csv(),
spark_read_delta(),
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()
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