Partition a Spark Dataframe

Partition a Spark DataFrame into multiple groups. This routine is useful for splitting a DataFrame into, for example, training and test datasets.

Usage

sdf_random_split(
  x,
  ...,
  weights = NULL,
  seed = sample(.Machine$integer.max, 1)
)

sdf_partition(x, ..., weights = NULL, seed = sample(.Machine$integer.max, 1))

Arguments

x

An object coercable to a Spark DataFrame.

...

Named parameters, mapping table names to weights. The weights will be normalized such that they sum to 1.

weights

An alternate mechanism for supplying weights – when specified, this takes precedence over the ... arguments.

seed

Random seed to use for randomly partitioning the dataset. Set this if you want your partitioning to be reproducible on repeated runs.

Value

An R list of tbl_sparks.

Details

The sampling weights define the probability that a particular observation will be assigned to a particular partition, not the resulting size of the partition. This implies that partitioning a DataFrame with, for example,

sdf_random_split(x, training = 0.5, test = 0.5)

is not guaranteed to produce training and test partitions of equal size.

See also

Other Spark data frames: sdf_copy_to(), sdf_distinct(), sdf_register(), sdf_sample(), sdf_sort(), sdf_weighted_sample()

Examples

if (FALSE) { # \dontrun{
# randomly partition data into a 'training' and 'test'
# dataset, with 60% of the observations assigned to the
# 'training' dataset, and 40% assigned to the 'test' dataset
data(diamonds, package = "ggplot2")
diamonds_tbl <- copy_to(sc, diamonds, "diamonds")
partitions <- diamonds_tbl %>%
  sdf_random_split(training = 0.6, test = 0.4)
print(partitions)

# alternate way of specifying weights
weights <- c(training = 0.6, test = 0.4)
diamonds_tbl %>% sdf_random_split(weights = weights)
} # }