# localCheckpoint

Returns a locally 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. Local checkpoints are stored in the executors using the
caching subsystem and therefore they are not reliable.

## Usage

``` r
localCheckpoint(x, eager = TRUE)

# S4 method for class 'SparkDataFrame'
localCheckpoint(x, eager = TRUE)
```

## Arguments

  - x:
    
    A SparkDataFrame

  - eager:
    
    whether to locally checkpoint this SparkDataFrame immediately

## Value

a new locally checkpointed SparkDataFrame

## Note

localCheckpoint since 2.3.0

## 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()`, `dapply()`,
`dapplyCollect()`, `describe()`, `dim()`, `distinct()`, `drop()`,
`dropDuplicates()`, `dropna()`, `dtypes()`, `except()`, `exceptAll()`,
`explain()`, `filter()`, `first()`, `gapply()`, `gapplyCollect()`,
`getNumPartitions()`, `group_by()`, `head()`, `hint()`, `histogram()`,
`insertInto()`, `intersect()`, `intersectAll()`, `isLocal()`,
`isStreaming()`, `join()`, `limit()`, `merge()`, `mutate()`, `ncol()`,
`nrow()`, `persist()`, `printSchema()`, `randomSplit()`, `rbind()`,
`rename()`, `repartition()`, `repartitionByRange()`, `rollup()`,
`sample()`, `saveAsTable()`, `schema()`, `select()`, `selectExpr()`,
`show()`, `showDF()`, `storageLevel()`, `str()`, `subset()`,
`summary()`, `take()`, `toJSON()`, `union()`, `unionAll()`,
`unionByName()`, `unpersist()`, `unpivot()`, `with()`, `withColumn()`,
`withWatermark()`, `write.df()`, `write.jdbc()`, `write.json()`,
`write.orc()`, `write.parquet()`, `write.stream()`, `write.text()`

## Examples

``` r
if (FALSE) { # \dontrun{
df <- localCheckpoint(df)
} # }
```