📘 Introduction

When you run a PySpark job, Spark doesn’t immediately execute each transformation. Instead, it constructs something called a DAG (Directed Acyclic Graph) — a roadmap of all the operations that need to happen.

This DAG is the heart of Spark’s execution engine. It tells Spark how your data should flow through transformations, where shuffles occur, and how tasks can be optimized and executed in parallel. Understanding DAGs helps you optimize performance, debug jobs, and grasp Spark’s lazy evaluation model — one of the keys to writing efficient distributed code.

In this guide, you’ll learn what a DAG is, how Spark builds and executes it, and how to inspect and interpret it.

💡 What Is a DAG in Spark?

A Directed Acyclic Graph (DAG) is a structure that represents a sequence of data transformations in Spark.

  • Directed → Data flows in one direction, from one transformation to the next.
  • Acyclic → No loops or cycles — Spark transformations always move forward.

Whenever you perform operations like filter(), select(), or join(), Spark doesn’t run them immediately. Instead, it records these steps as nodes and edges in a DAG.

The actual computation happens only when you call an action (like show(), count(), or collect()), triggering Spark to execute the DAG.

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This concept is known as lazy evaluation, and it’s what makes Spark both fast and flexible.

🧠 Why Spark Uses DAGs

Spark’s DAG-based architecture provides three major advantages:

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