How Spark Repartitions a DataFrame Across 4 Executors

Step through exactly what happens when you call repartition(4) on a 10GB DataFrame. Watch tasks split, shuffle bytes move across the network, and new partitions land on executor JVM memory with animated diagrams.

Animated Explanation

STEP 1 / 5

When you call df.repartition(4), Spark creates 4 new partitions by applying a hash function to every row. Rows with matching hash values land in the same partition.

Key Concepts

Shuffle mechanicsHash partitionerTask schedulingNetwork I/O cost

🎯 You'll Learn

Know when to repartition vs coalesce and why partition count affects parallelism

📋 Prerequisites

Basic Spark DataFrame API

Stack

Apache Spark

5 tutorials

Progress

Step 1 of 520%

Spark AQE: Adaptive Query Execution Deep Dive

▶ 12 min5.7k views

How Spark's Catalyst Optimizer Rewrites Your Query Plan

▶ 15 min3.8k views

When to Use Broadcast Joins and How Spark Decides

▶ 8 min9.1k views

Liked this tutorial?

View full Apache Spark path →