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Hash Function
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A hash function is any function that can be used to map data of arbitrary size to fixed-size values. The values returned by a hash function are called hash values, hash codes, digests, or simply hashes. The values are used to index a fixed-size table called a hash table. Use of a hash function to index a hash table is called hashing or scatter storage addressing.
- Snippet from Wikipedia: Hash function
A hash function is any function that can be used to map data of arbitrary size to fixed-size values, though there are some hash functions that support variable length output. The values returned by a hash function are called hash values, hash codes, hash digests, digests, or simply hashes. The values are usually used to index a fixed-size table called a hash table. Use of a hash function to index a hash table is called hashing or scatter storage addressing.
Hash functions and their associated hash tables are used in data storage and retrieval applications to access data in a small and nearly constant time per retrieval. They require an amount of storage space only fractionally greater than the total space required for the data or records themselves. Hashing is a computationally and storage space-efficient form of data access that avoids the non-constant access time of ordered and unordered lists and structured trees, and the often exponential storage requirements of direct access of state spaces of large or variable-length keys.
Use of hash functions relies on statistical properties of key and function interaction: worst-case behaviour is intolerably bad but rare, and average-case behaviour can be nearly optimal (minimal collision).: 527
Hash functions are related to (and often confused with) checksums, check digits, fingerprints, lossy compression, randomization functions, error-correcting codes, and ciphers. Although the concepts overlap to some extent, each one has its own uses and requirements and is designed and optimized differently. The hash function differs from these concepts mainly in terms of data integrity.
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Algorithms: Big O Notation, Iterative method Chase algorithm, Grokking Algorithms, Edsger Dijkstra, Donald Knuth: The Art of Computer Programming (TAOCP), Analysis of algorithms. Algorithms GitHub. (navbar_algorithms)
Algorithms: Algorithms Fundamentals, Algorithms Inventor: Algorithms Language Designer: ZZZ on DATE, YEAR; Algorithms DevOps - Algorithms SRE, Cloud Native Algorithms (Algorithms on Kubernetes - Algorithms on AWS - Algorithms on Azure - Algorithms on GCP), Algorithms Microservices, Algorithms Containerization (Algorithms Docker - Algorithms on Docker Hub), Serverless Algorithms, Algorithms Data Science - Algorithms DataOps - Algorithms and Databases (Algorithms ORM), Algorithms ML - Algorithms DL, Functional Algorithms (1. Algorithms Immutability, 2. Algorithms Purity - Algorithms No Side-Effects, 3. Algorithms First-Class Functions - Algorithms Higher-Order Functions, Algorithms Lambdas - Algorithms Anonymous Functions - Algorithms Closures, Algorithms Lazy Evaluation, 4. Algorithms Recursion), Reactive Algorithms), Algorithms Concurrency - Algorithms Parallel Programming - Async Algorithms, Algorithms Networking, Algorithms Security - Algorithms DevSecOps - Algorithms OAuth, Algorithms Memory Allocation (Algorithms Heap - Algorithms Stack - Algorithms Garbage Collection), Algorithms CI/CD - Algorithms Dependency Management - Algorithms DI - Algorithms IoC - Algorithms Build Pipeline, Algorithms Automation - Algorithms Scripting, Algorithms Package Managers, Algorithms Modules - Algorithms Packages, Algorithms Installation (Algorithms Windows - Chocolatey Algorithms, Algorithms macOS - Homebrew Algorithms, Algorithms on Linux), Algorithms Configuration, Algorithms Observability (Algorithms Monitoring, Algorithms Performance - Algorithms Logging), Algorithms Language Spec - Algorithms RFCs - Algorithms Roadmap, Algorithms Keywords, Algorithms Operators, Algorithms Functions, Algorithms Data Structures - Algorithms Algorithms, Algorithms Syntax, Algorithms OOP (1. Algorithms Encapsulation - 2. Algorithms Inheritance - 3. Algorithms Polymorphism - 4. Algorithms Abstraction), Algorithms Design Patterns - Algorithms Best Practices - Algorithms Style Guide - Clean Algorithms - Algorithms BDD, Algorithms Generics, Algorithms I/O, Algorithms Serialization - Algorithms Deserialization, Algorithms APIs, Algorithms REST - Algorithms JSON - Algorithms GraphQL, Algorithms gRPC, Algorithms Virtualization, Algorithms Development Tools: Algorithms SDK, Algorithms Compiler - Algorithms Transpiler, Algorithms Interpreter - Algorithms REPL, Algorithms IDEs (JetBrains Algorithms, Algorithms Visual Studio Code), Algorithms Linter, Algorithms Community - Algorithmsaceans - Algorithms User, Algorithms Standard Library - Algorithms Libraries - Algorithms Frameworks, Algorithms Testing - Algorithms TDD, Algorithms History, Algorithms Research, Algorithms Topics, Algorithms Uses - List of Algorithms Software - Written in Algorithms - Algorithms Popularity, Algorithms Bibliography - Algorithms Courses, Algorithms Glossary - Algorithms Official Glossary, Algorithms GitHub, Awesome Algorithms. (navbar_Algorithms)
Algorithms: Algorithms Fundamentals, Data Structures, Mathematical Algorithms, Data Algorithms, Algorithms and Data Structures, Algorithms Syntax, Algorithms and OOP - Algorithms and Design Patterns, Algorithms Best Practices, Algorithms and Containerization, Algorithms and IDEs (IntelliSense), Algorithms and Development Tools, Algorithms and Compilers, Algorithms and Data Science - Algorithms and DataOps, Machine Learning Algorithms - Algorithms and MLOps, Deep Learning Algorithms, Functional Algorithms, Algorithms and Concurrency - Algorithms and Parallel Programming, Algorithms Libraries, Algorithms History, Algorithms Bibliography, Algorithms Courses, Algorithms Glossary, Algorithms Topics, Algorithms Research, Algorithms GitHub, Written in Algorithms, Algorithms Popularity, Algorithms Awesome. (navbar_math_algorithms, navbar_data_algorithms - See also navbar_data_structures)
Data Structures: Array, Linked List, Stack, Queue, Binary Tree, Binary Search Tree, Heap, Hash Table, Graph, Trie, Skip List, Red-Black Tree, AVL Tree, B-Tree, B+ Tree, Splay Tree, Fibonacci Heap, Disjoint Set, Adjacency Matrix, Adjacency List, Circular Linked List, Doubly Linked List, Priority Queue, Dynamic Array, Bloom Filter, Segment Tree, Fenwick Tree, Cartesian Tree, Rope, Suffix Array, Suffix Tree, Ternary Search Tree, Radix Tree, Quadtree, Octree, KD Tree, Interval Tree, Sparse Table, Union-Find, Min-Max Heap, Binomial Heap, And-Or Graph, Bit Array, Bitmask, Circular Buffer, Concurrent Data Structures, Content Addressable Memory, Deque, Directed Acyclic Graph (DAG), Edge List, Eulerian Path and Circuit, Expression Tree, Huffman Tree, Immutable Data Structure, Indexable Skip List, Inverted Index, Judy Array, K-ary Tree, Lattice, Linked Hash Map, Linked Hash Set, List, Matrix, Merkle Tree, Multimap, Multiset, Nested Data Structure, Object Pool, Pairing Heap, Persistent Data Structure, Quad-edge, Queue (Double-ended), R-Tree, Radix Sort Tree, Range Tree, Record, Ring Buffer, Scene Graph, Scapegoat Tree, Soft Heap, Sparse Matrix, Spatial Index, Stack (Min/Max), Suffix Automaton, Threaded Binary Tree, Treap, Triple Store, Turing Machine, Unrolled Linked List, Van Emde Boas Tree, Vector, VList, Weak Heap, Weight-balanced Tree, X-fast Trie, Y-fast Trie, Z-order, Zero-suppressed Decision Diagram, Zigzag Tree
Data Structures Fundamentals - Algorithms Fundamentals, Algorithms, Data Types; Primitive Types (Boolean data type, Character (computing), Floating-point arithmetic, Single-precision floating-point format - Double-precision floating-point format, IEEE 754, Category:Floating point types, Fixed-point arithmetic, Integer (computer science), Reference (computer science), Pointer (computer programming), Enumerated type, Date Time);
Composite Types or Non-Primitive Types: Array data structure, String (computer science) (Array of characters), Record (computer science) (also called Struct (C programming language)), Union type (Tagged union, also called Variant type, Variant record, Discriminated union, or Disjoint union);
Abstract Data Types: Container (data structure), List (abstract data type), Tuple, Associative array (also called Map, Multimap, Set (abstract data type), Multiset (abstract data type) (also called Multiset (bag)), Stack (abstract data type), Queue (abstract data type), (e.g. Priority queue), Double-ended queue, Graph (data structure) (e.g. Tree (data structure), Heap (data structure))
Data Structures and Algorithms, Data Structures Syntax, Data Structures and OOP - Data Structures and Design Patterns, Data Structures Best Practices, Data Structures and Containerization, Data Structures and IDEs (IntelliSense), Data Structures and Development Tools, Data Structures and Compilers, Data Structures and Data Science - Data Structures and DataOps, Machine Learning Data Structures - Data Structures and MLOps, Deep Learning Data Structures, Functional Data Structures, Data Structures and Concurrency - Data Structures and Parallel Programming, Data Structure Libraries, Data Structures History, Data Structures Bibliography (Grokking Data Structures), Data Structures Courses, Data Structures Glossary, Data Structures Topics, Data Structures Research, Data Structures GitHub, Written in Data Structures, Data Structures Popularity, Data Structures Awesome. (navbar_data_structures - see also navbar_cpp_containers, navbar_math_algorithms, navbar_data_algorithms, navbar_design_patterns, navbar_software_architecture)
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