26 docs tagged with "fundamentals"

Master essential array operations including traversal, manipulation, rotation, reversal, and subarray problems critical for efficient algorithms.

Master the most fundamental data structures: arrays and strings. Learn essential techniques like two pointers, sliding window, and string manipulation.

Learn fundamental data structures including arrays, linked lists, stacks, queues, and hash tables that form the foundation of algorithmic problem-solving.

Master foundational programming concepts including variables, control flow, functions, recursion, and iteration essential for algorithm development.

Master binary search with standard templates, left/right bounds, and off-by-one handling for logarithmic search problems.

Understand the fundamental trade-offs: Consistency, Availability, Partition tolerance, and how PACELC refines these choices for modern systems.

Favor object composition over inheritance to achieve flexibility, reduce fragility, and improve code reuse.

Understand overlapping subproblems, optimal substructure, memoization vs tabulation, and the core principles of dynamic programming.

Master DRY principle: eliminate knowledge duplication for maintainable code, improved consistency, and reduced bugs.

Identify volatile aspects of your system and hide them behind stable interfaces to reduce coupling and change impact.

Detect and report errors immediately to prevent silent failures, data corruption, and cascading problems.

Understand the eight false assumptions that undermine distributed systems and learn to design systems that don't depend on them.

Build your foundation in distributed systems theory. Master the fallacies, theorems, and consistency models that guide all architecture decisions.

Essential foundations for algorithmic problem-solving: complexity analysis, basic data structures, and fundamental programming techniques.

Learn graph representations (adjacency list and matrix), graph traversal algorithms (BFS and DFS), and connected components analysis.

Master greedy algorithm principles including greedy choice property, optimal substructure, proof techniques, and when greedy applies.

Master binary heap operations including insert, extract, heapify, and sift operations for efficient priority queue implementation.

Build systems where components relate strongly to their purpose while remaining loosely connected to other components.

Prefer immutable data structures to reduce bugs, improve thread safety, and simplify reasoning about code.

Simplicity first: write code that humans understand before optimizing for machines.

Master naming conventions that clarify code intent and improve readability across all programming contexts.

Design systems where behavior matches user expectations, minimizing surprise and confusion.

Design systems where different areas address different aspects of functionality, improving modularity and maintainability.

Master Big O notation, complexity classes, and amortized analysis to evaluate algorithm efficiency and make informed optimization decisions.

Master tree data structures including traversals (inorder, preorder, postorder, level-order), height calculations, and properties.

Avoid speculative generality: implement only features needed today, not imagined future requirements.