cache

Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER).

Syntax

cache()

Returns

DataFrame: Cached 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

Serverless compatibility

Databricks recommends moving away from DataFrame.cache() as it is not compatible with Databricks serverless compute architecture. Materialize intermediate results to a Delta table instead.

Python

df = spark.range(1)
df.cache()
# DataFrame[id: bigint]

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