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✅ Part 1: Spark Cluster Simulation Notebook (Inline Code)

This Jupyter/Databricks notebook simulates how Spark behaves across cluster components:

# 📘 Spark Cluster Execution Flow Simulation Notebook

from pyspark.sql import SparkSession
from pyspark.sql.functions import broadcast

# Step 1: Start SparkSession (Driver starts)
spark = SparkSession.builder \
    .appName("SparkClusterSimulation") \
    .config("spark.executor.instances", "3") \
    .config("spark.executor.memory", "2g") \
    .config("spark.executor.cores", "2") \
    .config("spark.sql.shuffle.partitions", "6") \
    .getOrCreate()

# Step 2: Simulate data source read (~Stage 0)
df_large = spark.range(1, 10000000).withColumnRenamed("id", "user_id")
df_lookup = spark.createDataFrame([(i, f"Name_{i}") for i in range(1, 1000)], ["user_id", "name"])

# Step 3: Broadcast join to avoid shuffle stage
joined_df = df_large.join(broadcast(df_lookup), "user_id")

# Step 4: GroupBy operation (Stage may be merged if no shuffle required)
agg_df = joined_df.groupBy("name").count()

# Step 5: Cache the result (Stored in executor memory)
agg_df.cache()
agg_df.count()  # triggers caching

# Step 6: Write operation
agg_df.write.mode("overwrite").parquet("/tmp/output")

# Step 7: Checkpointing simulation (break lineage)
spark.sparkContext.setCheckpointDir("/tmp/checkpoints")
chk_df = agg_df.checkpoint()
chk_df.count()

# Final Step: Stop Spark
spark.stop()

🧠 Use .explain(True) at any step to inspect execution plan.

✅ Part 2: Spark Execution Flow — Mindmap Style Summary (Inline)

📌 Apache Spark Execution Flow
├── 🧠 Driver Program
│   ├── Initializes SparkSession
│   ├── Builds Logical Plan, DAG
│   ├── Connects to Cluster Manager
│   └── Schedules Jobs, Tracks Tasks
│
├── 🧩 Cluster Manager (YARN / Kubernetes / Standalone)
│   ├── Allocates resources (Executors)
│   ├── Launches executors on worker nodes
│   └── Reports executor status to Driver
│
├── ⚙️ Executors (JVMs on Worker Nodes)
│   ├── Run Tasks (from Stages)
│   ├── Store Cache (memory/disk)
│   ├── Write shuffle files
│   └── Return results to Driver
│
├── 📊 Stages and Tasks
│   ├── Narrow Dependencies → 1 Stage
│   ├── Wide Dependencies (shuffle) → New Stage
│   └── Each stage = set of parallel tasks
│
├── 🔁 Fault Tolerance
│   ├── If task fails → Retry (default 4 times)
│   ├── If executor fails → Reassign task
│   └── If all retries fail → Stage/job fails
│
└── 💾 Optimizations
    ├── Cache / Persist
    ├── Broadcast joins
    ├── Checkpointing (cut long DAG)
    └── Coalesce / Repartition control parallelism

✅ Optional: Mindmap Format You Can Copy to Draw.io or Notion

Apache Spark
├── Driver
│   ├── SparkSession
│   ├── Logical Plan → Optimizer → DAG
│   └── Job & Task Scheduling
├── Cluster Manager
│   ├── YARN
│   ├── Kubernetes
│   └── Standalone
├── Executors
│   ├── Task Execution
│   ├── Memory for Cache
│   └── Shuffle Data Handling
├── DAG & Stages
│   ├── Narrow vs Wide
│   ├── Shuffle = Stage Break
│   └── Physical Plan: .explain()
├── Tasks
│   ├── Parallel Units
│   └── One task per data partition
├── Optimizations
│   ├── Broadcast Joins
│   ├── Caching
│   ├── Coalesce / Repartition
│   └── Checkpointing
└── Fault Tolerance
    ├── Retry Failed Tasks
    ├── Recompute Lineage
    └── DAG Recovery

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