PySpark & Spark Mastery Course (A→Z, PhD-level)

🔥 Welcome to MODULE 6 — Hardcore Spark Practice + Interview Mastery.
This module is designed like a training camp for Spark architects:

  • ✅ Real-world Spark coding problems (easy → brutal)
  • ✅ LeetCode-style distributed data problems
  • ✅ PySpark + Spark SQL + RDD challenges
  • ✅ Performance & debugging scenarios
  • ✅ System design interview questions
  • ✅ Trick questions & traps
  • ✅ Real datasets + execution reasoning
  • ✅ How interviewers actually judge Spark engineers

Think of this as:

“From Spark engineer → Spark architect → Interview killer.”

🧠 MODULE 6 — HARDCORE SPARK PRACTICE & INTERVIEW MASTERY

🧩 STRUCTURE OF MODULE 6

We’ll train across 5 layers:

Layer 1 — Spark Coding Mastery (RDD + DataFrame + SQL)
Layer 2 — Distributed Thinking Problems (LeetCode-style)
Layer 3 — Performance & Debugging Scenarios
Layer 4 — Spark System Design Interviews
Layer 5 — Trick Questions & Interview Psychology

🔥 LAYER 1 — SPARK CODING MASTERY (REAL-WORLD PROBLEMS)

We’ll use a realistic dataset.

Dataset 1 — Users

users = [
 (1,"Amit","India",28,50000),
 (2,"Rahul","India",35,80000),
 (3,"John","USA",40,120000),
 (4,"Sara","USA",29,70000),
 (5,"Li","China",32,90000),
 (6,"Arjun","India",28,50000),
 (7,"Amit","India",28,50000)
]

Schema:

(user_id, name, country, age, salary)

Dataset 2 — Transactions

transactions = [
 (1,1000),
 (1,2000),
 (2,500),
 (3,7000),
 (3,3000),
 (5,4000)
]

Schema:

(user_id, amount)

🟢 Problem 1 — Find duplicate users (Spark DataFrame)

Expected Output:

All duplicate rows.

Solution:

from pyspark.sql import SparkSession
from pyspark.sql.functions import count

spark = SparkSession.builder.getOrCreate()

df = spark.createDataFrame(users, ["id","name","country","age","salary"])

dup = df.groupBy(df.columns).count().filter("count > 1")
dup.show()

Interview Insight:

❓ Why groupBy(df.columns) instead of groupBy(“name”)?

✅ Because duplicates mean entire row duplication, not just name.

🟢 Problem 2 — Top 2 salaries per country

Solution (Window Functions):

from pyspark.sql.window import Window
from pyspark.sql.functions import dense_rank, col

w = Window.partitionBy("country").orderBy(col("salary").desc())

df.withColumn("rank", dense_rank().over(w)) \
  .filter("rank <= 2") \
  .show()

Interview Trap:

❓ Why dense_rank instead of rank?

✅ rank() skips numbers (1,1,3). dense_rank() doesn’t.

🟢 Problem 3 — Users with no transactions

Solution:

df_users = spark.createDataFrame(users, ["id","name","country","age","salary"])
df_txn = spark.createDataFrame(transactions, ["id","amount"])

result = df_users.join(df_txn, "id", "left_anti")
result.show()

Interview Insight:

🔥 left_anti join is faster than NOT IN.

🟢 Problem 4 — Total salary per country (RDD + DataFrame + SQL)

RDD:

rdd = sc.parallelize(users)
rdd.map(lambda x: (x[2], x[4])).reduceByKey(lambda a,b: a+b)

DataFrame:

df.groupBy("country").sum("salary")

SQL:

SELECT country, SUM(salary)
FROM users
GROUP BY country;

Interview Insight:

RDD = Python execution
DataFrame/SQL = JVM execution

🔥 LAYER 2 — DISTRIBUTED LEETCODE-STYLE PROBLEMS

These are NOT normal SQL questions.
These test distributed thinking.

🔴 Problem 5 — Second highest salary globally

Solution:

df.orderBy(col("salary").desc()).select("salary").distinct().limit(2).collect()[1][0]

Better Spark solution:

from pyspark.sql.functions import dense_rank
w = Window.orderBy(col("salary").desc())
df.withColumn("r", dense_rank().over(w)).filter("r = 2")

🔴 Problem 6 — Countries where avg salary > global avg

Solution:

avg_salary = df.selectExpr("avg(salary) as avg").collect()[0][0]

df.groupBy("country").avg("salary").filter(f"avg(salary) > {avg_salary}")

Interview Trap:

❓ Why is collect() dangerous here?

✅ Because global avg is small result, but collect() on large DF is dangerous.

🔴 Problem 7 — Find skewed keys

Idea:

Detect keys with abnormal frequency.

df.groupBy("country").count().orderBy(col("count").desc())

Interview Insight:

Skew detection is always statistical.

🔥 LAYER 3 — PERFORMANCE & DEBUGGING SCENARIOS (REAL INTERVIEW STYLE)

These are the questions senior engineers get.

🧨 Scenario 1 — Spark job slow, CPU low, network high

Root Cause:

👉 Shuffle heavy workload.

Fix:

  • broadcast join
  • reduce shuffle partitions
  • pre-aggregation

🧨 Scenario 2 — One task takes 10x time

Root Cause:

👉 Data skew.

Fix:

  • salting
  • AQE
  • repartition

🧨 Scenario 3 — Driver OOM

Cause:

  • collect()
  • toPandas()
  • large broadcast

Fix:

  • write to storage instead
  • take()

🧨 Scenario 4 — Executors idle, cluster underutilized

Cause:

  • too few partitions

Fix:

df.repartition(200)

🔥 LAYER 4 — SPARK SYSTEM DESIGN INTERVIEW QUESTIONS

These questions decide senior vs junior engineers.

🧠 Question 1 — Design Spark pipeline for 100 TB/day data

Answer Framework:

  1. Storage: S3/HDFS/Delta
  2. Partition strategy: date + region
  3. Join strategy: broadcast dimensions
  4. Executor sizing
  5. AQE enabled
  6. Skew handling
  7. Fault tolerance
  8. Cost optimization

🔥 Interviewers want structure, not configs.

🧠 Question 2 — Design real-time analytics with Spark

Architecture:

Kafka → Spark Structured Streaming → Delta Lake → BI

Key challenges:

  • latency
  • state management
  • watermarking
  • exactly-once semantics

🧠 Question 3 — How would you debug a slow Spark job?

Answer like a systems engineer:

1) Algorithm (join type?)
2) Data distribution (skew?)
3) Memory (spill? GC?)
4) Network (shuffle?)
5) Cluster (parallelism?)

🔥 This answer alone can get you hired.

🔥 LAYER 5 — INTERVIEW TRAPS (MOST IMPORTANT)

These are questions where 90% candidates fail.

❓ Trap 1

“Spark is always faster than Hadoop. True?”

❌ Wrong
✅ Spark faster only for iterative workloads.

❓ Trap 2

“RDD is obsolete?”

❌ Wrong
✅ RDD still used for low-level control.

❓ Trap 3

“Broadcast join always improves performance?”

❌ Wrong
✅ Large broadcast → OOM.

❓ Trap 4

“More partitions always better?”

❌ Wrong
✅ Too many partitions = overhead.

❓ Trap 5 (Elite)

“Why is Spark not a database?”

✅ Because:

  • no transactions
  • no indexes
  • compute-first, not storage-first

🧠 FINAL LEVEL — SPARK INTERVIEW MINDSET

Most candidates answer like coders.

Top 5% answer like architects.

Example question:

❓ “Why is your Spark job slow?”

❌ Bad answer: “We need more memory.”

✅ Architect answer:

“The job is slow because of skewed join causing uneven partition distribution, leading to executor stragglers and high shuffle overhead. I would fix it using salting or AQE.”

🔥 That answer alone separates you from 95% candidates.

🧠 If you want, I can now build:

🧨 MODULE 7 — Spark Architect Level (Next Level)

This will include:

  • Spark interview question bank (300–500 questions)
  • Spark coding challenge pack (50–100 problems)
  • Real production debugging walkthroughs
  • Spark system design templates
  • PySpark vs Scala Spark deep comparison
  • Spark + Delta + Lakehouse architecture
  • How Netflix/Uber/Airbnb use Spark
  • “How to explain Spark like a principal engineer”

If you want, say:

👉 “Start Module 7 — I want Spark architect mastery.”

🧠 Bonus (If you want)

I can also create for you:

  • 📘 “Spark Bible” — structured book from Modules 1–6
  • 🧪 Daily Spark practice roadmap (30–60 days)
  • 🎯 Interview-ready cheat sheets
  • 💣 Ultra-hard Spark problems (FAANG-level)
  • 🧠 PySpark + SQL + Distributed Systems combined course

If you want, I’ll build it step-by-step with you.

And honestly —
You’re not learning Spark anymore.
You’re building distributed systems thinking.

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