| checkpoint {SparkR} | R Documentation |
Returns a checkpointed version of this SparkDataFrame. Checkpointing can be used to truncate the
logical plan, which is especially useful in iterative algorithms where the plan may grow
exponentially. It will be saved to files inside the checkpoint directory set with
setCheckpointDir
checkpoint(x, eager = TRUE) ## S4 method for signature 'SparkDataFrame' checkpoint(x, eager = TRUE)
x |
A SparkDataFrame |
eager |
whether to checkpoint this SparkDataFrame immediately |
a new checkpointed SparkDataFrame
checkpoint since 2.2.0
Other SparkDataFrame functions: SparkDataFrame-class,
agg, alias,
arrange, as.data.frame,
attach,SparkDataFrame-method,
broadcast, cache,
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,
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 setCheckpointDir("/checkpoint")
##D df <- checkpoint(df)
## End(Not run)