Welcome to Day 23 of the Spark Mastery Series. Yesterday we learned why shuffles are slow.
Today we learn how to beat them.
These techniques are used daily by senior data engineers.
🌟 1*. Broadcast Join — The Fastest Optimization*
Broadcast join removes shuffle entirely.
When used correctly:
- Job runtime drops dramatically
- Cluster cost reduces
- Stability improves
Golden rule:
Broadcast small, stable tables only.
🌟 2. Salting - Fixing the “Last Task Problem”
If your Spark job finishes 99% fast but waits forever for 1 task → data skew.
Salting breaks big keys into smaller chunks so work is evenly distributed.
This is common in:
- Country-level data
- Product category data
- Event-type aggregations
🌟 3. AQE - Let Spark Fix Itself
Adaptive Query Execution allows Spark to:
- Change join strategies
- Reduce partitions
- Fix skew at runtime
This removes the need for many manual optimizations.
If AQE is ON, Spark becomes smarter.
🌟 4. Real-World Optimization Flow
Senior engineers always:
- Check explain plan
- Look for shuffle
- Broadcast where possible
- Aggregate early
- Let AQE optimize
🚀 Summary
We learned:
- Broadcast join internals
- When auto-broadcast works
- How salting fixes skew
- How AQE optimizes at runtime
- A real optimization strategy
Follow for more such content. Let me know if I missed anything. Thank you
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