Top Algorithms for Performance and Efficiency
summary
Covers a practical seven-step problem method for real production pain points, walks through concrete algorithms (two-array common element, max-water, backspace-compare, BFS/DFS, Dijkstra, Kruskal/Prim, knapsack) and complexity tradeoffs, and shows tooling like generateData.com and Spark plus mention of RDDs and Google’s MapCruncher 2.0. Emphasizes memoization and caching to handle millions of records, explains when to choose sorting, hashing, or graph algorithms, and notes live demos partially fail due to internet but core patterns and optimizations are clearly demonstrated.
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chapters (16)
- 0:00Start
- 1:36Steps to Cracking the Algorithms
- 3:26Practice on Paper
- 4:12Do Mock Challenges
- 4:32Draw a Specific and Sufficiently Large Example
- 4:48Design an Algorithm and Optimize
- 5:33Test Your Code
- 15:13Negative tests
- 17:46Brute Force Approach: Using two loops
- 18:22Using a Hash Table
- 20:01Backspace String Compare
- 25:29Collisions: Separate Chaining
- 27:58Red Black Trees
- 28:16Binary Heap
- 31:04Undirected/Unweighted Graph
- 49:56Kruskal's Minimum Spanning Tree
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