Basics of Hashing

Duration: 9 min

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Module outline

  1. About Course: About the Course
  2. Programming, Data Structures, OOP & Java: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling, Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Java, OOPS with C++, Java Fundamentals, Array, String, IO & References, Language Design, Intro to Compilers, Visual Basics, Syntax Analysis: Bottom-Up, Object Oriented Technology
  3. Computer Networks, TCP/IP & Network Security: Introduction to CN, Data Communication, DLL: Flow Control, DLL: Access Control, DLL: Error Control, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Access Technologies, Network Security, Society, Law & Ethics, Cloud Computing, Mobile Computing, Net Layer:Routing Protocol, Cyber Safety and Security
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  5. Digital Electronics / Boolean Logic: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation, Analog-Digital Conversion
  6. Operating Systems & Linux: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, File Management, Disc Scheduling, Unix OS, Linux OS, Distributed Systems, Windows OS
  7. Data Base Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, Relational Algebra, SQL, File Organization & Indexing, Transaction Management, Concurrency Control, Database Security & Authorization, Database Recovery, Data Warehousing & Mining
  8. Internet & Web Technologies: Website, Browser & Services, Internet Fundamentals, Web Design & Development, Protocols & Services, Web Publishing & Hosting, Server Side & Security, HTML Basics, CSS, Emerging Technologies, JavaScript-Basics, .Net Programming, Electronic Data Interchange and E-Commerce, XML
  9. Computer Fundamentals, IT Basics & Architecture: Basics of COA, Cache Memory Organization, Input Output Organisation, Register Transfer and Microoperations, Control Unit Design, Programming the Basic Computer, Terms & Abbreviations, MS Office Applications, Instr Formats & Modes, Floating Point Rep
  10. Python Language: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis
  11. Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management, extras, Fundamentals of Systems and Information Systems
  12. Design & Analysis of Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  13. Mathematics - I, II, III, IV: Permutation and Combination, Logarithm, Linear Algebra, Algebra, Calculus, Trigonometry, Vectors, Set Theory, Functions, Relations, Propositional and Predicate Logic, Research Process Steps, Graph Theory
  14. Computer Graphics, Multimedia & Mobile Computing: Basics of Computer Graphics, 2-D Geometrical Transforms and Viewing, 3-D Object Representation, Geometric Transformations and Viewing, Multimedia
  15. Emerging Trends: Emerging Tech Trends, Knowledge Representation, Natural Language Processing
  16. Statistical Techniques: Probability, Statistics
  17. Interpolation - DI: Data Interpretation
  18. Basis of Physics: Physics
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AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

This lecture provides an introduction to hashing, a data structure technique designed to optimize search operations. The instructor begins by contextualizing hashing within the broader scope of data structures, noting that while storage is the primary goal, search is the most frequent operation. He reviews the search complexities of various structures, such as O(n) for unsorted arrays and O(logn) for sorted arrays and AVL trees. The core of the lecture defines hashing as a method to achieve constant time O(1) search by mapping keys directly to memory addresses. It concludes by defining hash functions and introducing the concept of collisions, where distinct keys map to the same location, necessitating collision resolution strategies. The lecture sets the foundation for understanding how hashing improves efficiency over traditional linear or tree-based searches.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The instructor starts by emphasizing that the most common operation in data structures is search, not insertion or deletion. He presents a slide listing search complexities for different structures: Unsorted array O(n), sorted array O(logn), linked list O(n), BT O(n), BST O(n), and AVL O(logn). To illustrate why these take time, he draws red diagrams next to the list. He sketches rectangular blocks for arrays and linked nodes for linked lists, showing arrows indicating a sequential traversal to find an element. He specifically crosses out 'Unsorted array' and 'BT' and 'BST' while keeping 'sorted array' and 'AVL' to contrast complexities. He underlines that search time depends on the number of elements and the structure type.

  2. 2:00 – 5:00 02:00-05:00

    The topic shifts to hashing, defined on the slide as a technique where search time is independent of the number of items. The instructor explains the basic idea: using the key itself to find the memory address. He gives examples like phone numbers, roll numbers, or Aadhar cards. A diagram shows a list of keys on the left mapping to a table of buckets in the middle. He explains that a hash function converts a key into a smaller practical number to use as an index. He highlights the text 'access the element in O(1) time' with a red circle. He also points out 'overflow entries' in the diagram, showing how extra data is handled when a bucket is full. He draws a red arrow from a key to a bucket index, illustrating the direct mapping process. He mentions that the hash function must be modified so a great deal of space is not wasted.

  3. 5:00 – 8:50 05:00-08:50

    The instructor formally defines the hash function as a conversion from a set of keys K to a set of memory locations L, written as H: K -> L. He explains that it maps a big number or string to a small integer. He then introduces 'Collision', explaining that two different keys K1 and K2 might yield the same hash address. He writes H(K1) and H(K2) on the board with arrows pointing to the same location L. He states that the technique to resolve this is called collision resolution. He draws a diagram showing n < m (keys less than locations) but notes collisions can still happen. He underlines the definition of collision on the slide. He explains that even if the number of keys is less than the number of locations, collisions are possible.

The lecture effectively bridges the gap between traditional data structures and hashing. It starts by establishing the problem: search operations in structures like arrays and trees are not constant time. It then proposes hashing as the solution, offering O(1) access. The instructor details the mechanism of hash functions and the inevitable issue of collisions, setting the stage for learning how to handle them. This progression moves from problem identification to solution definition and finally to the specific challenges of the solution, providing a comprehensive overview of the hashing concept.

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