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
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?
- 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:
collect_from_rds(),
spark_insert_table(),
spark_load_table(),
spark_read(),
spark_read_avro(),
spark_read_binary(),
spark_read_csv(),
spark_read_delta(),
spark_read_image(),
spark_read_json(),
spark_read_libsvm(),
spark_read_orc(),
spark_read_parquet(),
spark_read_source(),
spark_read_table(),
spark_read_text(),
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()
Examples
if (FALSE) { # \dontrun{
sc <- spark_connect(
master = "local",
config = list(
`sparklyr.shell.driver-class-path` = "/usr/share/java/mysql-connector-java-8.0.25.jar"
)
)
spark_read_jdbc(
sc,
name = "my_sql_table",
options = list(
url = "jdbc:mysql://localhost:3306/my_sql_schema",
driver = "com.mysql.jdbc.Driver",
user = "me",
password = "******",
dbtable = "my_sql_table"
)
)
} # }