Results for “data-structures”

Golang data structures — slices (internals, capacity growth, preallocation, slices package), maps (internals, hash buckets, maps package), arrays, container/list/heap/ring, strings.Builder vs bytes.Buffer, generic collections, pointers (unsafe.Pointer, weak.Pointer), and copy semantics. Use when choosing or optimizing Go data structures, implementing generic containers, using container/ packages, unsafe or weak pointers, or questioning slice/map internals. Not for applying optimization patterns once profiling has identified a bottleneck (→ See `samber/cc-skills-golang@golang-performance` skill).

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Algorithms and data structures from first principles through advanced analysis. Covers sorting (bubble, insertion, selection, merge, quick, heap, radix), searching (linear, binary, BFS, DFS, Dijkstra, A*), fundamental data structures (arrays, linked lists, stacks, queues, hash tables, trees, heaps, graphs, tries), complexity analysis (Big-O, Big-Omega, Big-Theta, amortized), recurrence relations, and algorithm design paradigms (divide-and-conquer, greedy, dynamic programming, backtracking). Use when analyzing, selecting, implementing, or comparing algorithms and data structures.

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Reference for advanced data structure patterns used in competitive programming and technical interviews. Covers heaps, monotonic stacks, tries, segment trees, Fenwick trees, stack-based parsing, and ordered sets with Python templates, recognition signals, and edge case guidance.

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Use when working with Redis - in-memory database, caching, pub/sub, sessions, rate limiting, RESP3, asyncio, Redis Stack

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Expert-level data structures covering arrays, linked lists, trees, heaps, hash tables, graphs, and advanced structures like segment trees, tries, and union-find.

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Expert en algorithmes (Big-O, dynamic programming, graphs, sorting, LeetCode patterns)

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