Skip to main content

Menu

LEVEL 0
0/5 XP
HomeAboutTopicsPricingMy VaultStats

Categories

πŸ€– Artificial Intelligence
☁️ Cloud and Infrastructure
πŸ’Ύ Data and Databases
πŸ’Ό Professional Skills
🎯 Programming and Development
πŸ”’ Security and Networking
πŸ“š Specialized Topics
HomeAboutTopicsPricingMy VaultStats
LEVEL 0
0/5 XP
GitHub
Β© 2026 CheatGridβ„’. All rights reserved.
Privacy PolicyTerms of UseAboutContact

Hash Tables & Hash Maps Cheat Sheet

Hash Tables & Hash Maps Cheat Sheet

Back to Mathematics and Algorithms
Updated 2026-04-29
Next Topic: Heaps and Priority Queue Algorithms Cheat Sheet

Hash tables (also called hash maps or associative arrays) are data structures that implement an associative array abstract data type, mapping keys to values for rapid lookup. At their core, they use a hash function to compute an index into an array of buckets from which the desired value can be retrieved. Hash tables are ubiquitous in computer science β€” powering dictionaries in Python, maps in Go, HashMaps in Java, unordered_map in C++, and countless database indexes and caches. The magic lies in achieving O(1) average-case time complexity for insertions, deletions, and lookups, though the worst case remains O(n) when collisions overwhelm the structure. Understanding how hash tables manage collisions, resize dynamically, and balance speed with memory efficiency is essential for writing high-performance code β€” a key insight is that performance is determined as much by the hash function and collision strategy as by the underlying array.

What This Cheat Sheet Covers

This topic spans 20 focused tables and 119 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core ConceptsTable 2: Hash Function PropertiesTable 3: Collision Resolution β€” Separate ChainingTable 4: Collision Resolution β€” Open AddressingTable 5: Advanced Collision StrategiesTable 6: Hash Function AlgorithmsTable 7: Cryptographic Hash FunctionsTable 8: Dynamic Resizing and Load FactorTable 9: Deletion Handling in Open AddressingTable 10: Clustering ProblemsTable 11: Time Complexity AnalysisTable 12: Space ComplexityTable 13: Language-Specific ImplementationsTable 14: Performance Optimization TechniquesTable 15: Security ConsiderationsTable 16: Specialized Hash Table VariantsTable 17: Hash Table vs Alternative Data StructuresTable 18: Common ApplicationsTable 19: Hash Table Design DecisionsTable 20: Performance Pitfalls and Best Practices

Table 1: Core Concepts

ConceptExampleDescription
Hash Function
hash("John") β†’ 42
table[42] = "John's data"
β€’ Maps keys of arbitrary size to fixed-size integer indices (hash codes)
β€’ must be deterministic β€” same input always yields same output.
Hash Table
table = [None] * 16
table[hash(key) % 16] = value
β€’ Array of buckets/slots indexed by hash codes
β€’ uses modulo operation to wrap hash values into valid array indices.
Bucket
bucket_3 = [(k1,v1), (k2,v2)]
β€’ A slot in the hash table array
β€’ may hold one entry (open addressing) or multiple entries (chaining) when collisions occur.
Collision
hash("cat") == hash("dog")
β†’ both map to index 5
β€’ Occurs when two different keys produce the same hash index
β€’ requires collision resolution to store both entries.
Load Factor
Ξ» = n / m
Ξ» = 10 / 16 = 0.625
β€’ Ratio of number of entries (n) to table capacity (m)
β€’ typical resize threshold is 0.7–0.75.

More in Mathematics and Algorithms

  • Graphs Cheat Sheet
  • Heaps and Priority Queue Algorithms Cheat Sheet
  • Abstract Algebra Essentials Cheat Sheet
  • Complex Analysis Cheat Sheet
  • Graph Algorithms Shortest Paths and Network Search Cheat Sheet
  • Number Theory Cheat Sheet
View all 57 topics in Mathematics and Algorithms