Coalesce
coalesce.RdReturns 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.
Arguments
- x
a SparkDataFrame.
- ...
additional argument(s).
- numPartitions
the number of partitions to use.
Details
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).
See also
repartition, repartitionByRange
Other SparkDataFrame functions:
SparkDataFrame-class,
agg(),
alias(),
arrange(),
as.data.frame(),
attach,SparkDataFrame-method,
broadcast(),
cache(),
checkpoint(),
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(),
repartition(),
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 <- coalesce(df, 1L)
}