Day 19: Spark Broadcasting & Caching

Welcome to Day 19 of the Spark Mastery Series.
Today we focus on memory, the most common reason Spark jobs fail in production.

Most Spark failures are not logic bugs - they are memory mistakes.

🌟 Broadcasting — The Right Way to Join Small Tables

Broadcast joins avoid shuffle and are extremely fast.
But misuse leads to executor crashes.

Golden rule:
-> Broadcast only when the table is small and stable.

Spark automatically decides broadcast sometimes, but explicit broadcast gives you control.

🌟 Caching — Powerful but Dangerous
Caching improves performance only when:

• The same DataFrame is reused
• Computation before cache is heavy

Bad caching causes:

• Executor OOM
• GC thrashing
• Cluster instability

Always ask:
-> Will this DataFrame be reused?

🌟 Persist vs Cache — What to Use?

• cache() → simple, MEMORY_ONLY
• persist(MEMORY_AND_DISK) → production-safe

Use persist() for ETL pipelines.

🌟 Spark Memory Internals
Spark prioritizes execution memory over cached data.

If Spark needs memory for shuffle:

• It evicts cached blocks
• Recomputes them later

This is why caching doesn’t guarantee data stays in memory forever.

🌟 Real-World Example
Bad practice

df1.cache()
df2.cache()
df3.cache()

Good practice

df_silver.persist(StorageLevel.MEMORY_AND_DISK)
df_silver.count()
# use df_silver multiple times
df_silver.unpersist()

🚀 Summary
We learned:

• How broadcast joins work internally
• When to use and avoid broadcast
• Cache vs persist
• Storage levels
• Spark memory model
• How to avoid OOM errors

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