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Returns a new SparkDataFrame that has exactly numPartitions partitions. This operation results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim 10 of the current partitions. If a larger number of partitions is requested, it will stay at the current number of partitions.


coalesce(x, ...)

# S4 method for SparkDataFrame
coalesce(x, numPartitions)



a SparkDataFrame.


additional argument(s).


the number of partitions to use.


However, if you're doing a drastic coalesce on a SparkDataFrame, e.g. to numPartitions = 1, this may result in your computation taking place on fewer nodes than you like (e.g. one node in the case of numPartitions = 1). To avoid this, call repartition. This will add a shuffle step, but means the current upstream partitions will be executed in parallel (per whatever the current partitioning is).


coalesce(SparkDataFrame) since 2.1.1


if (FALSE) {
path <- "path/to/file.json"
df <- read.json(path)
newDF <- coalesce(df, 1L)