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If you want to reuse a dataframe df without having to recreate it, you can use df.cache() to tell Spark to keep the dataframe in memory.

cache() is a transformation, which are lazy in Spark, and so won’t take effect until an action is called. To force the effect of cache() (or another transformation), you can use the simple action count(), e.g.:


This is called ‘materializing the cache’.

Note that when the count action has to also cache data, it takes longer than a simple count.

If you want to tell Spark to no longer keep the dataframe in memory, use df.unpersist() to manually evict the data from memory cache.

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