pyspark.sql.DataFrame.persist¶

DataFrame.persist(storageLevel: pyspark.storagelevel.StorageLevel = StorageLevel(True, True, False, True, 1)) → pyspark.sql.dataframe.DataFrame[source]¶

Sets the storage level to persist the contents of the DataFrame across operations after the first time it is computed. This can only be used to assign a new storage level if the DataFrame does not have a storage level set yet. If no storage level is specified defaults to (MEMORY_AND_DISK_DESER)

New in version 1.3.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

storageLevelStorageLevel: Storage level to set for persistence. Default is MEMORY_AND_DISK_DESER.

Returns

DataFrame: Persisted DataFrame.

Notes

The default storage level has changed to MEMORY_AND_DISK_DESER to match Scala in 3.0.

Examples

>>> df = spark.range(1)
>>> df.persist()
DataFrame[id: bigint]

>>> df.explain()
== Physical Plan ==
InMemoryTableScan ...

Persists the data in the disk by specifying the storage level.

>>> from pyspark.storagelevel import StorageLevel
>>> df.persist(StorageLevel.DISK_ONLY)
DataFrame[id: bigint]

pyspark.sql.DataFrame.orderBy pyspark.sql.DataFrame.printSchema