| coalesce {SparkR} | R Documentation |
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 signature 'SparkDataFrame' coalesce(x, numPartitions)
x |
a SparkDataFrame. |
... |
additional argument(s). |
numPartitions |
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
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, unionByName,
union, unpersist,
withColumn, withWatermark,
with, write.df,
write.jdbc, write.json,
write.orc, write.parquet,
write.stream, write.text
## Not run:
##D sparkR.session()
##D path <- "path/to/file.json"
##D df <- read.json(path)
##D newDF <- coalesce(df, 1L)
## End(Not run)