Algorithms and Their Habitat
summary
Covers concrete techniques: data normalization turns product lists into ID‑keyed maps for constant time lookups and replaces N×M scans with linear joins, memoization caches selector results (reselect style) to avoid repeated heavy computations, and Levenshtein distance powers a demo 404 fixer that sorts routes by edit distance, applies a threshold, and redirects on close matches. Shows using libs like ngrx/entity and simple caching so the demo suggestion feature actually runs fast and stable.
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chapters (13)
- 0:00Start
- 3:22TIME and SPACE Complexity
- 6:45Data with relations - get entity
- 7:33Recursive data - get entity
- 8:51Normalized data - get entity
- 10:50WHEN?
- 11:47Fibonacci numbers
- 12:01Call tree
- 12:30Memoized
- 14:07Albert Levenstein
- 14:29Distance matrix
- 19:16Sort by Levenshtein distance
- 19:51Suggest route
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