SparkR (version 3.1.2)

read.jdbc: Create a SparkDataFrame representing the database table accessible via JDBC URL

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.

Value

SparkDataFrame

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.

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")
# }