Algorithms and Data Structures

Classic CS fundamentals. The pieces underneath search ranking, recommendation, and model inference. Each post walks the problem and a clean implementation.

Data structures

• LRU Cache. O(1) get/put with a hash map and a doubly linked list.
• Flatten Nested List. Iterator pattern over arbitrarily nested structures.
• Stack, Queue, Heap. Core data structure implementations.

Graph and tree

• Connected Islands. BFS/DFS to find the largest connected component.
• Graph Algorithms. Traversal, shortest path, related problems.
• Tree Algorithms. Traversal, search, tree manipulation.

Sorting and searching

• Sort Algorithms. Merge sort, quicksort, when to use which.
• K-Nearest Points. Heap vs. sort approach for nearest-neighbor.
• String Match. Pattern matching algorithms.
• K Complementary. Finding pair sums in arrays.

Strings and parsing

• Rule Parser. String parsing with structured rules.
• Split String. Word-boundary-aware string splitting.
• String Rotation. Detecting rotation via the concatenation trick.
• Sub Domain Hits. Counting subdomain visit frequencies.

Classics

• FizzBuzz. The classic, done cleanly.
• Merge Sorted Lists. Two-pointer merge.
• Large File Processing. Top-K from a 50GB file using heaps.