Data Structures Hub

Data structures are the specialized formats for organizing, processing, retrieving, and storing data. This hub organizes Wikantik's content on the fundamental building blocks of efficient software — the analysis toolkit, the design paradigms, and the structures themselves, from primitives to vector indexes.

Analysis: Costing Algorithms

The tools for measuring and understanding computational efficiency.

• Asymptotic Analysis and Recurrences — Big-O/Theta/Omega precisely, recursion trees, and the Master Theorem
• Amortized Analysis — Analyzing the average performance of an operation over a worst-case sequence of events
• Data Structures — Overarching survey of the field and the fundamental space/time trade-offs

Design Paradigms

How algorithms are actually invented.

• Algorithm Design Paradigms — Divide and conquer, greedy with exchange arguments, DP, and backtracking/branch-and-bound
• Dynamic Programming — State design, memoization vs tabulation, knapsack, edit distance, and DP on DAGs
• Foundational Algorithms for Computer Scientists — The essential per-algorithm toolkit

Basic Structures

The ubiquitous primitives used in almost every software system.

• Hash Table Design — Collision resolution, load factors, and the trade-offs of the most versatile data structure
• Heap and Priority Queues — Implementing efficient min/max retrieval and its use in scheduling and graph search
• Sorting Algorithms Comparison — Trade-offs between O(n log n) and O(n²) algorithms, stability, and in-place sorting
• Bloom Filters — Space-efficient probabilistic data structures for set membership testing

Tree and Hierarchical Structures

Structures optimized for ordered search and range queries.

• Balanced Search Trees — AVL, Red-Black, and the mechanics of keeping search trees performant
• B+ Trees — The workhorse of database indexing and filesystem design (optimized for disk I/O)
• Trie Data Structure — Prefix trees for efficient string search and autocomplete
• Interval Trees — Managing and querying overlapping intervals and ranges

Graph and String Algorithms

Handling complex relational data and unstructured text.

• Graph Algorithms Deep Dive — Traversals, shortest paths, spanning trees, and network flow
• Graph Coloring Deep Dive — Specialized graph algorithms for resource allocation and scheduling
• String Matching Algorithms — From naive search to Boyer-Moore and KMP for efficient pattern matching

Specialized and Advanced Structures

• CRDT Data Structures — Conflict-free Replicated Data Types for distributed systems and collaborative editing
• Topological Data Analysis — Using topology to understand the "shape" of high-dimensional data
• HNSW — Hierarchical Navigable Small World graphs for high-dimensional vector search
• Vector Indexing Internals — Comparing HNSW and IVF-PQ for ANN retrieval

Adjacent Hubs

• Computer Science Foundations Hub — Theory of computation and the systems stack
• Software Engineering Practices Hub — Applying these structures in production codebases
• Mathematics Hub — The discrete math and logic underlying algorithm design
• Distributed Systems Hub — Scaling data structures across multiple nodes