1. Left table alias
orders o aliases the left, typically large, table as o for short references.
FROM orders oTyporithm · medium
Joins
spark-joinsparkjoinbroadcastdistributed
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Step 1 of 8 · intro card
Learn Joins
Spark SQL
Join distributed tables on a key with JOIN ... ON; broadcast the smaller table with /*+ BROADCAST(c) */ to skip an expensive shuffle.
A join pairs rows from two distributed tables on a key, here orders to customers on customer_id = id.
By default Spark shuffles both sides across the network to colocate matching keys, which is costly for large tables.
When one side is small, a broadcast join ships that whole table to every executor — add a /*+ BROADCAST(c) */ hint to avoid the shuffle entirely.
01
JOIN ... ON pairs rows on a key.
02
Default joins shuffle both sides.
03
Broadcast a small table to skip the shuffle.
SELECT o.id, c.name FROM orders o JOIN customers c ON o.customer_id = c.idRead the implementation
Connect each part of the code to the concept before you type.
2. Join the tables
JOIN customers c brings in the second table, aliased c, to combine matching rows.
JOIN customers c3. Join key
ON defines the equality key that pairs each order with its customer.
ON o.customer_id = c.id4. Qualified columns
Qualifying columns with the alias avoids ambiguity when both tables share a name.
SELECT o.id, c.nameA complete learning loop
01
Understand
Read the concept map and follow the code breakdown.
02
Reconstruct
Type the implementation, repair mistakes and work through every recall exercise.
03
Verify
Use the final check without hints; revisit weak concepts in short practice sessions.