Exact duplicates are a hash and a group-by. Near-duplicates are a similarity search over billions of documents, which becomes a shuffle rather than a computation. The signature arithmetic, why the shuffle is the expensive stage, and where a GPU classifier fits in a pipeline that is otherwise all CPU.
Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.
Exact duplicates are a hash and a group-by. Near-duplicates are a similarity search over billions of documents, which becomes a shuffle rather than a computation. The signature arithmetic, why the shuffle is the expensive stage, and where a GPU classifier fits in a pipeline that is otherwise all CPU.
Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.
Scored on separating exact from near-duplicate detection by cost, on identifying the LSH shuffle rather than the hashing as the bottleneck, and on placing quality filtering as a separate GPU-side pass.
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