sparklyr (version 1.5.0)

spark_read_jdbc: Read from JDBC connection into a Spark DataFrame.

Description

Read from JDBC connection into a Spark DataFrame.

Usage

spark_read_jdbc(
  sc,
  name,
  options = list(),
  repartition = 0,
  memory = TRUE,
  overwrite = TRUE,
  columns = NULL,
  ...
)

Arguments

sc

A spark_connection.

name

The name to assign to the newly generated table.

options
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 "binary" for BinaryType, "boolean" for BooleanType, "byte" for ByteType, "integer" for IntegerType, "integer64" for LongType, "double" for DoubleType, "character" for StringType, "timestamp" for TimestampType and "date" for DateType.

...

Optional arguments; currently unused.

See Also

Other Spark serialization routines: 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()