Spark Performance Tuning | Best PySpark & Databricks Online Training

SPARK
PERFORMANCE
TUNING
Optimize Your PySpark Applications with Confidence
AccentFuture – Best PySpark & Databricks Online Training
+91-96400 01789
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W H Y S PA R K P E R F O R M A N C E
T U N I N G M AT T E R S
Apache Spark is a powerful distributed
computing engine, but default settings rarely
deliver peak performance. Performance tuning
allows you to:
o Improve job execution times significantly
o Reduce resource usage (memory, CPU)
o Avoid bottlenecks in data-intensive
workflows
o 🎯 Example: A data ingestion job in PySpark
took 45 minutes to complete. After tuning
shuffle partitions and memory settings,
execution time dropped to just 10 minutes.
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U N D E R S T A N D I N G T H E S P A R K E X E C U T I O N M O D E L
To tune effectively, you must understand how Spark
works internally:
• Driver: Runs the main program and schedules tasks
• Executors: Perform the actual computation across the
cluster
• Tasks and Stages: Spark breaks a job into multiple
stages, which are further divided into tasks
📘 Example: If a PySpark job fails due to memory
overload, it’s often because the executor ran out of
space due to excessive task parallelism or poor
memory allocation.
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M E M O R Y M A N A G E M E N T – G E T T I N G I T
R I G H T
Memory tuning is critical for Spark performance. The main settings include:
o spark.executor.memory: Total memory allocated per executor
o spark.memory.fraction: Controls how much memory is for storage vs execution
o spark.driver.memory: Memory available to the driver
💡 Example: Increasing executor.memory from 4GB to 8GB helped reduce garbage
collection overhead in a machine learning pipeline using PySpark on Databricks.
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S H U F F L E O P T I M I Z A T I O N – T H E H I D D E N
P E R F O R M A N C E K I L L E R
Shuffles happen when Spark moves data between partitions, often due to operations
like groupBy, join, or distinct.
Tuning Tips:
• Replace groupByKey() with reduceByKey()
• Use broadcast joins when one dataset is small
• Set spark.sql.shuffle.partitions to match your data size and cluster
🧪 Example: A PySpark join on two large DataFrames caused a huge shuffle stage.
By using a broadcast join on the smaller table, shuffle size dropped by 80%, and
runtime improved by 3x.
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P A R T I T I O N I N G S T R AT E G Y –
B A L A N C I N G T H E L O A D
Spark parallelizes operations by partitioning data.
Poor partitioning leads to:
• Skewed load across executors
• Underutilization of cluster resources
• Memory errors or slow tasks
Best Practices:
• Use repartition() for full reshuffling
• Use coalesce() to reduce partitions efficiently
• Target partition sizes around 100–200MB
🧪 Example: In a 2TB retail dataset, repartitioning
from 100 to 800 improved query performance
and reduced spill to disk.
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C A C H I N G A N D P E R S I S T E N C E
– A V O I D R E C O M P U T I N G
When a dataset is reused multiple times, caching can
save compute time.
• Use .cache() when dataset fits in memory
• Use .persist(StorageLevel.DISK_ONLY) if
memory is limited
• Always monitor with the Spark UI
💡 Example: A PySpark model training loop that used
cached input features ran 60% faster than the non-
cached version.
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S E R I A L I Z AT I O N & R E S O U R C E
S E T T I N G S
Spark uses Java serialization by default, which is slow and
bulky.
Optimization Tips:
• Use Kryo serialization
(spark.serializer=KryoSerializer) for complex
objects
• Avoid large object graphs in RDDs
• Monitor GC time and tune heap size
🧪 Example: Switching to Kryo reduced job memory usage
by 40% and boosted performance for a financial risk
modeling job in PySpark.
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S U M M A R Y & L E A R N I N G P AT H W I T H
A C C E N T F U T U R E
Key Takeaways:
• Understand the execution model
• Tune memory, partitions, and shuffles
• Use caching and smart joins
• Monitor jobs using Spark UI
🎓 Ready to Master Spark?
Explore our expert-led programs:
✅ Apache PySpark training
✅ Best PySpark course for real-time data pipelines
✅ Databricks online course for enterprise-ready skills
🔗 Start your learning at: www.accentfuture.com
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CO NTACT DE TA ILS
• 📧 contact@accentfuture.com
• 🌐 AccentFuture
• 📞 +91-96400 01789
contact@accentfuture.com +91-96400 01789

Spark Performance Tuning | Best PySpark & Databricks Online Training

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