Below is Module 3 — Spark Memory Management & Performance Engineering at OS + JVM + Distributed Systems depth.
This is the part most engineers never truly understand, but it is what separates:

• ❌ Spark users
• ✅ Spark engineers
• 🧠 Spark architects

I’ll explain Spark memory like an operating system + JVM + distributed engine combined, with simulations, failure cases, and tuning logic.

🧠 MODULE 3 — SPARK MEMORY MANAGEMENT & PERFORMANCE ENGINEERING (DEEPEST LEVEL)

3.0 Mental Model (Critical)

Most people think:

Spark memory = executor memory.

Correct model:

Cluster Memory
 ├── Driver Memory
 ├── Executor Memory
 │     ├── JVM Heap
 │     ├── Off-Heap Memory
 │     ├── Python Worker Memory
 │     ├── Shuffle Memory
 │     ├── Cache Memory
 │     ├── User Memory
 │     └── OS Memory
 └── Disk (spill)

Spark memory ≠ JVM memory ≠ OS memory.

🧱 3.1 JVM Memory Architecture (Foundation)

Spark runs on JVM, so JVM memory rules apply.

3.1.1 JVM Heap Structure

JVM Heap
 ├── Young Generation
 │     ├── Eden
 │     ├── Survivor S0
 │     └── Survivor S1
 ├── Old Generation
 └── Metaspace

Key Insight

• Spark objects live in Old Gen.
• Frequent GC kills Spark performance.

3.1.2 Garbage Collectors (GC) in Spark

Common GCs:

🔥 Interview Insight:

Spark tuning is often GC tuning.

🧠 3.2 Spark Executor Memory Model (Deep)

Executor memory is divided logically.

3.2.1 Unified Memory Model (Spark 1.6+)

Executor Memory
 ├── Reserved Memory (~300MB)
 ├── Unified Memory (spark.memory.fraction)
 │     ├── Execution Memory
 │     └── Storage Memory
 ├── User Memory
 └── Off-Heap Memory

Default:

spark.memory.fraction = 0.6
spark.memory.storageFraction = 0.5

Meaning:

• 60% of executor heap → Spark memory
• 40% → user + metadata + overhead
• Within 60%:
  • 50% storage
  • 50% execution

3.2.2 Execution vs Storage Memory

Execution Memory

Used for:

• Shuffles
• Joins
• Sorts
• Aggregations

Storage Memory

Used for:

• cache()
• persist()
• broadcast variables

3.2.3 Dynamic Borrowing (Critical)

Execution & storage memory can borrow from each other.

Example:

• Shuffle needs more memory → steals from cache.
• Cache needs memory → spills execution.

🔥 This is why cached data disappears unexpectedly.

🧠 3.3 Python Memory vs JVM Memory (PySpark Reality)

PySpark adds another memory layer.

Executor JVM Memory
 ├── JVM Heap
 ├── Python Worker Memory
 ├── Py4J Buffers
 └── Pickle Objects

Key Insight

Increasing executor memory does NOT always fix PySpark OOM.

Because:

• Python memory is separate.
• Pickle objects duplicate memory.

🧠 3.4 Example: Memory Explosion Scenario (Realistic)

Code:

data = list(range(100_000_000))
rdd = sc.parallelize(data)
result = rdd.map(lambda x: x * 2).collect()

What happens?

1. Python list created on driver → huge memory.
2. Serialized → sent to executors.
3. Executors compute.
4. collect() brings data back to driver → driver OOM.

🔥 Lesson:

collect() is the most dangerous Spark operation.

🧠 3.5 Partitioning & Memory Relationship

Golden Rule:

Partition size ≈ 100MB – 256MB

If partitions too large:

• OOM risk.

If partitions too small:

• Scheduling overhead.

Example:

Data size = 1 TB

Recommended partitions:

1 TB / 128 MB ≈ 8000 partitions

🧠 3.6 Shuffle Memory (Most Expensive Operation)

Shuffle process:

Map Task
 ├── Buffer data in memory
 ├── Spill to disk if memory full
 └── Write shuffle files

Reduce Task:

Fetch shuffle files → merge → sort → aggregate

Memory Pressure Points:

• Buffer overflow
• Disk spill
• Network congestion

🧠 3.7 Spark Spill to Disk (Hidden Performance Killer)

Spark spills data when:

• Execution memory full
• Sort buffer full
• Aggregation buffer full

Symptoms:

• Job slow
• Disk IO high
• CPU low

🧠 3.8 Broadcast Variables & Memory

Broadcast variable stored in:

Executor JVM Memory (Storage)

Problem:

Large broadcast table → executor OOM.

🧠 3.9 Real Production Case Study (Deep)

Scenario:

• Join between fact (1 TB) and dimension (10 GB).
• Spark job failing with OOM.

Mistake:

Spark auto-broadcast dimension table.

Fix:

Disable broadcast join:

spark.sql.autoBroadcastJoinThreshold = -1

🧠 3.10 Executor Sizing (Engineering Formula)

Most important topic.

Step 1 — Understand cluster resources

Example cluster:

• 10 nodes
• 32 cores per node
• 128 GB RAM per node

Step 2 — Decide executor cores

Rule:

executor_cores = 3–5

Why?

• Too many cores → GC overhead.
• Too few cores → underutilization.

Assume:

executor_cores = 4

Step 3 — Calculate executors per node

32 cores / 4 cores = 8 executors per node

Step 4 — Calculate executor memory

Available memory per node:

128 GB × 0.9 (OS overhead) ≈ 115 GB

Memory per executor:

115 GB / 8 ≈ 14 GB

Step 5 — Final Spark config

--executor-cores 4
--num-executors 80
--executor-memory 14g
--driver-memory 8g

🔥 This is real-world sizing logic.

🧠 3.11 Spark UI — Memory Debugging (Advanced)

Spark UI tabs:

Example Debugging:

Symptoms:

• Stage slow
• One task slow

Cause:

👉 Data skew.

🧠 3.12 Common Spark Memory Errors (Deep)

1) java.lang.OutOfMemoryError: Java heap space

Cause:

• Large partitions
• Skewed join
• Large broadcast
• Too few partitions

Fix:

• Increase partitions
• Reduce broadcast
• Tune executor memory

2) GC Overhead Limit Exceeded

Cause:

• Too many small objects
• Python UDF
• Inefficient transformations

Fix:

• Use DataFrame instead of RDD
• Avoid Python UDFs

3) Driver OOM

Cause:

• collect()
• toPandas()
• large broadcast

Fix:

• Use take()
• write to disk instead of collect

🧠 3.13 Performance Optimization Techniques (Hardcore)

1) Avoid Shuffles

Replace:

groupByKey()

with:

reduceByKey()

2) Repartition vs Coalesce

3) Cache Strategically

Bad:

df.cache()

Good:

Cache only reused datasets.

4) Predicate Pushdown

Use Parquet/ORC filters.

5) Column Pruning

Select only needed columns.

🧠 3.14 PhD-Level Insight: Spark Performance Equation

Spark performance depends on:

Execution Time =
CPU Time +
Serialization Time +
Shuffle Time +
GC Time +
Network Time +
Disk IO Time

Best Spark engineers optimize all six.

🧠 3.15 Interview-Level Questions (Elite)

Q1

Why does increasing executor memory sometimes make Spark slower?

✅ Because:

• GC overhead increases.

Q2

Why is many small executors better than few large executors?

✅ Because:

• Better parallelism
• Less GC overhead

Q3

Why does Spark spill to disk?

✅ Because execution memory full.

Q4 (Trap)

Increasing partitions always improves performance. True or false?

❌ False.

Too many partitions → overhead.

🧠 3.16 Real Engineering Mindset (Most Important)

Spark optimization is not about code.

It is about:

• memory flow
• data flow
• network flow
• execution graph

🔥 Spark engineers think like OS engineers.

🚀 If you want, next module will be even crazier:

MODULE 4 — Spark Performance Optimization, AQE, Skew, Join Algorithms, Query Planning (Deepest Level)

You will learn:

• Adaptive Query Execution (AQE)
• Join algorithm internals
• Skew join mechanics
• Cost-based optimization
• Real-world tuning recipes
• Spark vs Flink performance
• Spark query plan reading like a compiler engineer
• 200+ tricky interview questions
• Real production debugging cases

If you want, say:

👉 “Start Module 4 at the deepest possible level.”

And if you want, I can also:

• Convert Modules 1–3 into a Spark Bible PDF
• Add diagrams + exercises + assignments
• Add real-world datasets & projects
• Create Spark interview mega-question bank

We are basically building the most advanced Spark course possible.