Repartition
repartition.RdThe following options for repartition are possible:
1. Return a new SparkDataFrame that has exactly
numPartitions.2. Return a new SparkDataFrame hash partitioned by the given columns into
numPartitions.3. Return a new SparkDataFrame hash partitioned by the given column(s), using
spark.sql.shuffle.partitionsas number of partitions.
Usage
repartition(x, ...)
# S4 method for SparkDataFrame
repartition(x, numPartitions = NULL, col = NULL, ...)Arguments
- x
a SparkDataFrame.
- ...
additional column(s) to be used in the partitioning.
- numPartitions
the number of partitions to use.
- col
the column by which the partitioning will be performed.
See also
Other SparkDataFrame functions:
SparkDataFrame-class,
agg(),
alias(),
arrange(),
as.data.frame(),
attach,SparkDataFrame-method,
broadcast(),
cache(),
checkpoint(),
coalesce(),
collect(),
colnames(),
coltypes(),
createOrReplaceTempView(),
crossJoin(),
cube(),
dapplyCollect(),
dapply(),
describe(),
dim(),
distinct(),
dropDuplicates(),
dropna(),
drop(),
dtypes(),
exceptAll(),
except(),
explain(),
filter(),
first(),
gapplyCollect(),
gapply(),
getNumPartitions(),
group_by(),
head(),
hint(),
histogram(),
insertInto(),
intersectAll(),
intersect(),
isLocal(),
isStreaming(),
join(),
limit(),
localCheckpoint(),
merge(),
mutate(),
ncol(),
nrow(),
persist(),
printSchema(),
randomSplit(),
rbind(),
rename(),
repartitionByRange(),
rollup(),
sample(),
saveAsTable(),
schema(),
selectExpr(),
select(),
showDF(),
show(),
storageLevel(),
str(),
subset(),
summary(),
take(),
toJSON(),
unionAll(),
unionByName(),
union(),
unpersist(),
unpivot(),
withColumn(),
withWatermark(),
with(),
write.df(),
write.jdbc(),
write.json(),
write.orc(),
write.parquet(),
write.stream(),
write.text()
Examples
if (FALSE) {
sparkR.session()
path <- "path/to/file.json"
df <- read.json(path)
newDF <- repartition(df, 2L)
newDF <- repartition(df, numPartitions = 2L)
newDF <- repartition(df, col = df$"col1", df$"col2")
newDF <- repartition(df, 3L, col = df$"col1", df$"col2")
}