Merk
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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).
Syntax
persist(storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_DESER)
Parameters
| Parameter | Type | Description |
|---|---|---|
storageLevel |
StorageLevel | 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.
Cached data is shared across all Spark sessions on the cluster.
Examples
df = spark.range(1)
df.persist()
# DataFrame[id: bigint]
df.explain()
# == Physical Plan ==
# InMemoryTableScan ...
from pyspark.storagelevel import StorageLevel
df.persist(StorageLevel.DISK_ONLY)
# DataFrame[id: bigint]