🔥 Day 4: RDD Internals - Partitions, Shuffles & Repartitioning Demystified

Welcome to Day 4 of the Spark Mastery Series.

Yesterday we learned RDD basics. Today we go deeper into partitions, shuffles, coalesce, repartition, and persistence—core concepts that define Spark performance.

⚡ 1. Understanding Partitions
A partition is Spark’s basic unit of parallel processing.

Think of partitions like:

• Slices of a pizza
• Each slice handled by one executor core
• More partitions = more parallel workers

Why partitions matter?

• Too few partitions → cluster underutilized
• Too many partitions → scheduler overhead

Default number of partitions:
`parallelize() → spark.default.parallelism (usually number of cores × 2)

File reads → based on block size`

Check partitions:

rdd.getNumPartitions()

🔁 2. Narrow vs Wide Transformations (The Real Reason Your Jobs Are Slow)

Narrow transformations:

• No data movement
• No shuffle
• Faster

Examples: map, filter, union

Wide transformations:

• Data movement between executors
• Causes shuffle
• Creates new stage

Examples: reduceByKey, groupByKey, join, distinct

🔥 3. Shuffle — Spark’s Most Expensive Operation

During shuffle, Spark:

• Writes data to disk
• Transfers it over network
• Reorganizes partitions

This is why shuffle-heavy jobs run slow. Huge companies spend millions reducing shuffle.

🔄 4. Repartition vs Coalesce
This is one of the most misunderstood concepts.

Repartition:

• Used to increase OR decrease partitions
• Causes full shuffle
• Data gets evenly distributed
• Good for large operations like joins

df2 = df.repartition(50)

When to use?

• Before joins
• Before large aggregations
• When dealing with skew

Coalesce:

• Used to reduce partitions only
• No shuffle
• Much faster than repartition
• Moves minimal data

df2 = df.coalesce(5)

When to use?

• Writing to small number of output files
• Improving file compactness
• When merging small partitions

📦 5. Persistence & Caching-Boosting Performance

Spark recomputes transformations unless cached.

Example:

processed = rdd.map(...).filter(...)
processed.persist()
processed.count()
processed.collect()

Without persist → Spark computes twice
With persist → second action reads from cache

🧠 Summary
Today we learned:

• How partitions work
• What causes shuffle
• Difference between narrow and wide transformations
• When to use repartition vs coalesce
• How caching helps performance

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