pyspark.sql.DataFrame.localCheckpoint#

DataFrame.localCheckpoint(eager=True, storageLevel=None)[source]#

Returns a locally checkpointed version of this DataFrame. Checkpointing can be used to truncate the logical plan of this DataFrame, 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.

New in version 2.3.0.

Changed in version 4.0.0: Supports Spark Connect. Added storageLevel parameter.

Parameters

eagerbool, optional, default True: Whether to checkpoint this DataFrame immediately.
storageLevelStorageLevel, optional, default None: The StorageLevel with which the checkpoint will be stored. If not specified, default for RDD local checkpoints.

Returns

DataFrame: Checkpointed DataFrame.

Notes

This API is experimental.

Examples

>>> df = spark.createDataFrame([
...     (14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.localCheckpoint(False)
DataFrame[age: bigint, name: string]