25Fuzzy-match entities across two customer lists: normalization, edit distance, and scaling past O(n×m)▼hardPalantirScaleGlean1 replies◆ premiumPalantir's bread and butter: 'Acme Corp.' and 'ACME Corporation, Inc.' are the same customer; prove it in code. Normalization does more work than the clever algorithm, and blocking is what makes it run before the heat death of the universe.Open full answer →
73Compute the edit distance (Levenshtein) between two strings.▼hard★ EssentialGoogleMetaAdobe1 replies◆ premiumA DP classic that screens for one thing: can you define the right subproblem and fill the table without fumbling the indices. The strong version adds the O(min(m,n)) space trick and reconstructs the actual edits when the follow-up lands.Open full answer →