read.jdbc: Create a SparkDataFrame representing the database table...

In SparkR: R Front End for 'Apache Spark'

Description

Additional JDBC database connection properties can be set (...)

Usage

read.jdbc(
  url,
  tableName,
  partitionColumn = NULL,
  lowerBound = NULL,
  upperBound = NULL,
  numPartitions = 0L,
  predicates = list(),
  ...
)

Arguments

url	JDBC database url of the form jdbc:subprotocol:subname
tableName	the name of the table in the external database
partitionColumn	the name of a column of numeric, date, or timestamp type that will be used for partitioning.
lowerBound	the minimum value of partitionColumn used to decide partition stride
upperBound	the maximum value of partitionColumn used to decide partition stride
numPartitions	the number of partitions, This, along with lowerBound (inclusive), upperBound (exclusive), form partition strides for generated WHERE clause expressions used to split the column partitionColumn evenly. This defaults to SparkContext.defaultParallelism when unset.
predicates	a list of conditions in the where clause; each one defines one partition
...	additional JDBC database connection named properties.

Details

Only one of partitionColumn or predicates should be set. Partitions of the table will be retrieved in parallel based on the numPartitions or by the predicates.

Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash your external database systems.

Value

SparkDataFrame

Note

read.jdbc since 2.0.0

Examples

## Not run: 
sparkR.session()
jdbcUrl <- "jdbc:mysql://localhost:3306/databasename"
df <- read.jdbc(jdbcUrl, "table", predicates = list("field<=123"), user = "username")
df2 <- read.jdbc(jdbcUrl, "table2", partitionColumn = "index", lowerBound = 0,
                 upperBound = 10000, user = "username", password = "password")

## End(Not run)

SparkR documentation built on June 3, 2021, 5:05 p.m.