spark_read_jdbc

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Read from JDBC connection into a Spark DataFrame.

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 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.

...

Optional arguments; currently unused.

See Also

Other Spark serialization routines: spark_load_table, spark_read_csv, spark_read_json, spark_read_libsvm, spark_read_parquet, spark_read_source, spark_read_table, spark_read_text, spark_save_table, spark_write_csv, spark_write_jdbc, spark_write_json, spark_write_parquet, spark_write_source, spark_write_table, spark_write_text

Aliases
  • spark_read_jdbc
Documentation reproduced from package sparklyr, version 0.7.0, License: Apache License 2.0 | file LICENSE

Community examples

marcos@kondado.com at Dec 19, 2018 sparklyr v0.9.3

library(sparklyr) config <- spark_config() #config$`sparklyr.shell.driver-class-path` <- "mysql-connector-java-5.1.43/mysql-connector-java-5.1.43-bin.jar" sc <- spark_connect(master = "local") db_tbl <- spark_read_jdbc(sc, name = "table_name", options = list(url = "jdbc:mysql://localhost:3306/schema_name", user = "root", password = "password",