What happens when you enable cache() in PySpark and the dataset exceeds the available memory? How does Spark handle this situation, and what potential issues might arise?

both cache() and persist() store data in memory to speed up the retrieval of intermediate data used for computation. However, persist() is more flexible and allows users to specify storage levels, including disk storage. 

when you enable cache() in PySpark and the dataset exceeds the available memory? How does Spark handle this situation, and what potential issues might arise?

When you enable cache() in PySpark but the data size is larger than the available memory, the following happens:

1️⃣ Spilling to Disk

• PySpark uses lazy evaluation, so when an action (like .count() or .show()) triggers computation, Spark attempts to cache the DataFrame/RDD in memory.
• If there is not enough memory, Spark spills excess data to disk (temporary storage) instead of keeping it all in RAM.
• This can significantly slow down performance because reading/writing from disk is much slower than reading from memory.

2️⃣ Eviction of Cached Data (Least Recently Used – LRU)

• If memory is full, Spark follows an LRU (Least Recently Used) policy and removes the least used partitions from the cache.
• If an evicted partition is needed again, Spark recomputes it from the original source.

3️⃣ Potential Out-of-Memory (OOM) Errors

• If spilling is not enough and the workload is too large, Spark can run out of memory and fail with an OutOfMemoryError (OOM).

🔥 Best Practices to Handle Large Data When Using cache()

✅ Use persist(StorageLevel.DISK_ONLY) Instead of cache()

• If data is too large for memory, store it on disk explicitly:

df.persist(StorageLevel.DISK_ONLY)

✅ Use persist(StorageLevel.MEMORY_AND_DISK) for Balanced Performance

• This keeps as much in memory as possible and spills the rest to disk:

from pyspark.storagelevel import StorageLevel
df.persist(StorageLevel.MEMORY_AND_DISK)

✅ Use persist(StorageLevel.MEMORY_AND_DISK_SER) for Compressed Storage

• If memory is limited, serialize the cached data to save space:

df.persist(StorageLevel.MEMORY_AND_DISK_SER)

✅ Increase Executor Memory (spark.executor.memory)

• If Spark has access to more memory, increase it in your Spark configurations:

spark = SparkSession.builder \
    .config("spark.executor.memory", "8g") \
    .getOrCreate()

✅ Use Checkpointing for Large Datasets

• If the DataFrame is very large, checkpointing stores data in a reliable location and prevents unnecessary recomputation:

spark.sparkContext.setCheckpointDir("/tmp/checkpoints")
df.checkpoint()