Great to check the reshuffling and broadcast logic in the author's slides! Use them as references if needed to check!

Step Snap 1: [Mastering Spark Join Strategies]

Imagine organizing a massive party where guests (data) from different venues (tables) need to meet up (join). How do we make this happen efficiently? Let's explore Spark's join strategies!

🌟 Join Strategy #1: The Big Party Meetup (Joining Two Large Tables) Think of two large groups from different cities meeting up:

• Everyone needs to travel to a common location (Shuffle)
• Traffic (data transfer) can get heavy
• Need proper planning (partitioning) to avoid chaos

# Example of a standard shuffle join
large_table1.join(large_table2, "common_key")

🌟 Join Strategy #2: The Sorted Line Dance (Merge Sort Join) Like two lines of dancers already arranged by height:

• Both lines are pre-sorted (data sorted by join key)
• Partners can easily find each other by walking forward (sequential matching)
• Very efficient, but requires pre-sorted data

# Enable sort merge join
spark.conf.set("spark.sql.join.preferSortMergeJoin", "true")
# Join with sorted data
sorted_table1.join(sorted_table2, "join_key")

🌟 Join Strategy #3: The VIP List Strategy (Broadcast Join) Like having a small VIP list at multiple party entrances:

• One small list (table) copied to all security checkpoints (nodes)
• Everyone from the main party (large table) can be checked quickly
• No need for people to move between entrances (reduced shuffle)

from pyspark.sql.functions import broadcast
# Broadcast the smaller table
large_table.join(broadcast(small_table), "join_key")