View source: R/data_interface.R
spark_read_jdbc | R Documentation |
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 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 |
... |
Optional arguments; currently unused. |
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()
## Not run:
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"
)
)
## End(Not run)
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