Join Type vs Join Strategy

Concepts covered: paDistributedPrimitives

Two vocabularies collide in every conversation about joins, and keeping them apart is a mark of someone who understands the system. The first vocabulary is the join type: inner, left outer, right outer, full outer, semi, anti. That is logic. It answers one question: which rows appear in the result. The second vocabulary is the join strategy: broadcast the small side, or shuffle both sides. That is physics. It answers a different question: how do matching rows physically meet. The type is in your code and changes the answer; the strategy is in the engine and changes the runtime. The two are almost fully orthogonal. The same inner join of orders to products produces identical results whether Spark broadcasts products or shuffles both tables; only the cost differs. And a left outer join can r

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This section is part of the Joins: Beginner lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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