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 class 'SparkDataFrame'
checkpoint(x, eager = TRUE)
Arguments
A SparkDataFrame
whether to checkpoint this SparkDataFrame immediately
a new checkpointed SparkDataFrame
Notecheckpoint since 2.2.0
See alsoOther 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()
, unionAll()
, unionByName()
, union()
, unpersist()
, unpivot()
, withColumn()
, withWatermark()
, with()
, write.df()
, write.jdbc()
, write.json()
, write.orc()
, write.parquet()
, write.stream()
, write.text()
if (FALSE) { # \dontrun{
setCheckpointDir("/checkpoint")
df <- checkpoint(df)
} # }
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