Welcome to Day 18 of the Spark Mastery Series. Today’s content is about speed, cost, and stability.
You can write correct Spark code - but if it’s slow, it fails in production.
Let’s fix that.
🌟 1. Understand Where Spark Spends Time
In most pipelines:
- 70–80% time → shuffles
- 10–15% → computation
- Rest → scheduling & I/O So optimization = reduce shuffle.
🌟 2. Shuffles — What to Watch For
In explain():
- Look for Exchange
- Look for SortMergeJoin
- Look for too many stages These indicate expensive operations.
🌟 3. Real Optimization Techniques
🔹 Broadcast Small Tables
Use when lookup < 10–50 MB.
🔹 Repartition on Join Keys
Align partitions → less data movement.
🔹 Aggregate Before Join
Reduce data volume early.
🌟 4. Partition Strategy That Works
- For ETL → fewer, larger partitions
- For analytics → partition by date
- Tune
spark.sql.shuffle.partitions
Default (200) is rarely optimal.
🌟 5. Cache Only When Necessary
Bad caching:
df.cache()
without reuse → memory waste.
Good caching:
df.cache()
df.count()
df.join(...)
🌟 6. Explain Plan = Your Debugger
Always use:
df.explain(True)
Learn to read:
- Logical plan
- Optimized plan
- Physical plan This skill alone makes you senior-level.
🌟 7. Real-World Example
Before optimization
- Runtime: 45 minutes
- Multiple shuffles
- UDF usage
After optimization
- Runtime: 6 minutes
- Broadcast join
- Early filtering
- No UDF
🚀 Summary
We learned:
- Why Spark jobs are slow
- How to identify shuffles
- How to reduce shuffles
- Partition & caching strategy
- How to use explain() effectively
Follow for more such content. Let me know if I missed anything. Thank you!!
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