Here is a detailed step-by-step lineage of how a Spark job is initiated, executed, and managed in a cluster, including how components interact, programs are called, memory is allocated, and executors are assigned.

🔥 Apache Spark Job Execution – Complete Lifecycle & Architecture

📌 Step 0: Your Program – Spark Application Code

You write a PySpark or Scala/Java application like:

spark = SparkSession.builder.appName("MyJob").getOrCreate()
df = spark.read.csv("input.csv")
result = df.filter(...).groupBy(...).agg(...)
result.write.parquet("output/")

This script constitutes a Spark Application.

✅ Step 1: Driver Process Launch

• The driver is the master JVM process that initiates SparkContext and orchestrates the entire job.
• This is where the SparkSession lives.
• Driver responsibilities:
  • Reads your code
  • Builds DAG of transformations
  • Converts to physical plan
  • Schedules and coordinates executors

✅ Step 2: Cluster Manager Interaction

When you call .getOrCreate(), Spark communicates with a cluster manager:

The cluster manager allocates:

• 1 driver container/process
• N executor containers/processes

⚙️ Resource Allocation Parameters

--num-executors 4
--executor-memory 4G
--executor-cores 2

✅ Step 3: SparkContext + DAG Creation

Logical Plan

• Spark creates a DAG (Directed Acyclic Graph) of transformations (e.g., map, filter, groupBy).
• This is the logical plan: it represents what to do, not how to do it.

✅ Step 4: DAG Scheduler → Stage Creation

• Spark divides the DAG into stages based on shuffle boundaries.
  • Narrow transformations (map, filter): stay in same stage.
  • Wide transformations (groupBy, join): new stage, causes a shuffle.
• Each stage is broken into tasks — one task per partition.

Example:

df = spark.read.csv("...").filter(...).groupBy(...).count()

Stages:

• Stage 1: Read & Filter
• Stage 2: GroupBy (shuffle required)

✅ Step 5: TaskScheduler → Task Execution

The TaskScheduler sends tasks to executors.

🔹 Executors

• JVM processes launched on worker nodes.
• Run tasks assigned by the driver.
• Store data in memory/disk using block manager.
• Cache/persist RDDs/DataFrames.
• Communicate results back to driver.

Each executor has:

• N cores (executor-cores)
• M memory (executor-memory)

✅ Step 6: Execution Flow

1. Driver sends tasks to executors.
2. Executors run tasks in parallel (one per core).
3. Intermediate data is:
   • Held in memory (if caching)
   • Written to disk (if shuffling)
4. Shuffled data is pulled by downstream stages.

✅ Step 7: Memory Management

Spark divides executor memory:

In Spark 3.x (Unified Memory Manager), execution and storage share memory dynamically.

Let’s now deep dive into Step 7: Memory Allocation per Executor in Spark, including how memory is calculated, what config values matter, and how dynamic memory sharing works in Spark’s Unified Memory Manager.

📦 Step 7: Memory Allocation per Executor (in Spark 3.x+)

🔥 What Is Executor Memory?

Each Spark executor runs on a worker node and has a JVM with its own memory space.

This memory is divided into:

✅ Key Configuration Parameters

🧮 How Spark Memory Is Calculated

Let’s assume:

spark.executor.memory = 4g

🔢 Breakdown:

And…

| Non-heap Overhead | spark.executor.memoryOverhead | ~384 MB (default) |

✅ Unified Memory Manager (Spark 2.0+)

Execution and storage memory are not fixed:

• If your job does no caching, storage memory is released to execution.
• If execution needs memory and storage is underused → it borrows it.
• If caching is needed and there’s space → storage can grow.
• Eviction happens if execution needs more and storage has cached data.

🔀 Dynamic Memory Sharing

Scenario A: Execution-intensive stage

• Sorts, joins, shuffles → use most of Unified Memory
• Spark evicts cached data if needed (LRU policy)

Scenario B: Heavy caching

• Many .cache() or .persist() calls
• Execution will fail if it can’t get enough memory (OOM)

✅ Memory Tuning Tips

🧠 Spark UI & Monitoring

• Executor tab: shows memory used for storage, execution, overhead.
• Storage tab: shows cached RDD/DataFrame sizes.
• Task tab: shows spills to disk (bad sign = not enough memory).

🧪 Example:

spark-submit \
  --executor-memory 4G \
  --executor-cores 4 \
  --conf spark.memory.fraction=0.6 \
  --conf spark.memory.storageFraction=0.5 \
  --conf spark.executor.memoryOverhead=512M \
  app.py

Memory layout:

4 GB executor heap
- 300 MB reserved
= 3.7 GB usable

0.6 × 3.7 = 2.2 GB Unified Memory
→ 1.1 GB Execution
→ 1.1 GB Storage
~ 1.5 GB User Memory
+ 512 MB memoryOverhead (off-heap)

🔚 Summary

✅ Step 8: Final Result Collection or Write

• If .collect(): data is pulled to the driver
• If .write(): data is written from executors to storage (HDFS, S3, DB)

Here’s a deep dive into Step 8: Final Result Collection or Write in Apache Spark, covering:

• What happens when results are collected or written
• Differences in Databricks vs. on-prem BDL (Big Data Lake) Spark
• Memory, network, disk behavior
• Failure scenarios and performance tips

⚠️ .collect() – Result Collection to Driver

🔍 What happens:

1. Spark triggers a job.
2. Tasks run on executors.
3. Results are pulled back to the driver node via network.
4. Returned as a list/array in local driver memory.

result = df.filter(...).collect()

🔥 Internals:

• Uses network (not HDFS) to transfer data from executors.
• If result size exceeds driver.memory → 💥 OutOfMemoryError
• Can overwhelm driver if data > few MBs.

✅ Use when:

• You need small datasets (like < 100K rows).
• For debugging, testing, or local export.

❌ Avoid when:

• Result is large (millions of rows or >1GB).
• You are working in a distributed compute flow.

⚙️ Common Configs:

--conf spark.driver.maxResultSize=2g

✅ .write() – Write Results to External Storage

This is the most production-safe way to handle large outputs.

df.write.mode("overwrite").parquet("s3://my-bucket/output/")

🔍 What happens:

1. Data is partitioned across executors.
2. Each task writes its portion of data:
   • HDFS / DBFS / S3 / ADLS
   • JDBC / Hive / Delta Lake
3. Metadata (schema, partition info) is optionally written.

✳️ Output Paths and Formats

✅ Behavior in Databricks vs. On-Prem BDL

🟦 In Databricks

• Uses DBFS or ADLS/S3 paths: df.write.format("delta").save("/mnt/datalake/output/")
• Uses Delta Lake format by default for tables
• Can write to:
  • Managed/External Delta Tables
  • Unity Catalog tables
  • SQL Warehouses

Example:

df.write.format("delta").mode("overwrite").saveAsTable("sales_summary")

Benefits:

• Auto-commits via Delta Transaction Log
• Can use VACUUM, OPTIMIZE, ZORDER post-write
• Fully scalable + trackable via job UI

🟩 In On-Prem BDL or Hadoop Cluster

• Uses HDFS or NFS-mounted output paths: df.write.format("orc").save("/data/warehouse/output/")
• Needs correct permissions + HDFS quotas
• More manual cleanup and checkpointing

🧠 Behind the Scenes: Write Path

1. Executor tasks write files in parallel (1 file per partition).
2. Temporary files written in _temporary directory.
3. Upon success:
   • _SUCCESS marker file is added
   • Temp files renamed and committed
4. Driver finalizes metadata (for Hive/Delta)

⚠️ Failure Points

✅ Best Practices

🧪 Example: Complete Script in Databricks

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("WriteExample").getOrCreate()

df = spark.read.option("header", True).csv("/mnt/raw/sales.csv")

df_clean = df.filter("amount > 1000").withColumnRenamed("amount", "total")

# Write as a Delta Table
df_clean.write.format("delta").mode("overwrite").saveAsTable("sales_summary")

# Optional: Optimize
spark.sql("OPTIMIZE sales_summary ZORDER BY (region)")

🧪 Example: BDL On-Prem Script (HDFS Output)

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("OnPremWrite") \
    .getOrCreate()

df = spark.read.json("hdfs:///data/in/orders")

df2 = df.select("order_id", "user_id", "amount")

# Repartition to reduce small files
df2.repartition(4).write \
    .mode("overwrite") \
    .parquet("hdfs:///warehouse/processed/orders/")

✅ Step 9: Job/Stage/Task Completion

Spark shows the progress in:

• Spark UI (Driver web UI) – available at http://<driver-host>:4040
• Databricks: Task DAG + Job metrics
• Includes: Stages, Tasks, Shuffle Reads/Writes, GC Time, Input Size, etc.

✅ Step 10: Cleanup

• Driver process exits
• Executors shut down
• Cluster resources are released

🧠 Diagram: Spark Architecture (Simplified)

+---------------------+     submits job to     +----------------------+
| Your Spark Program  |----------------------->| Spark Driver         |
+---------------------+                        +----------------------+
                                                      |
              +---------------------------------------+
              |
              v
     +--------------------+
     |  DAG Scheduler     |    → divides into stages
     +--------------------+
              |
              v
     +--------------------+     → tasks created per partition
     | Task Scheduler     |
     +--------------------+
              |
              v
+--------------------+  +--------------------+   +--------------------+
|  Executor on Node1 |  | Executor on Node2  |   | Executor on NodeN  |
+--------------------+  +--------------------+   +--------------------+

✅ Example: Real Job Flow

df = spark.read.csv("data.csv")          # Stage 1: File read
df2 = df.filter("value > 100")           # Still Stage 1 (narrow)
df3 = df2.groupBy("category").count()    # Stage 2 (wide, shuffle)
df3.write.parquet("out/")                # Stage 3: File write

🔍 Monitoring Spark Execution

• Driver UI (port 4040): Stages, DAG, Storage, Executors
• Databricks: “View” → “Job/Task Graph”
• Use .explain() on DataFrames to inspect the physical plan.

⚠️ Spark Optimization Tips

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