Basics of Hashing

Duration: 9 min

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Module outline

  1. General Intelligence & Reasoning: Analogy, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Series (Number and Letter Series) (Numerical Relations and Reasoning), Coding Decoding, Blood Relations, Directions (Direction Test), Ranking, Order and Sequence, Logical Sequence Of Words, Syllogisms, Venn Diagram, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Selection Decision Table (Decision Making), Data Sufficiency, Puzzle Solving (Puzzles), Data Arrangement, Cubes & Dices, Calendar, Clock, Arithmetic, Number System, HCF LCM, Verbal Analogy (Word Based Analogy), Visual Recognition and Figure Completion, Paper Folding and Cutting, Transformation of Shapes, Clocks and Calendars, Direction Sense, Cubes and Dice, Patterns in Two-Dimensional Figures, Seating Arrangements, Analogy and Classification, Logical Deduction and Induction, Alphabet and Word Tests, Mathematical Operations, Series Completion, Simplification and Approximation, Coding and Decoding, Numerical Reasoning, Assembling and Grouping of Shapes, Three-Dimensional Patterns and Visualization, Vocabulary, Number System, Averages, Mixtures and Alligation, Simple and Compound Interest, Percentages, Algebra, Speed, Time and Distance, Seating and Arrangement Puzzles, Traverse Surveying, Order, Ranking and Sequence, Constraint Puzzles, Syllogisms and Venn Diagrams, Reading Comprehension, Critical Reasoning, Permutations and Combinations, Narrative Sequencing, Project Monitoring, Resources and Safety, Profit, Loss and Discount, Ratio and Proportion, Time and Work, Mensuration, Data Interpretation, Powers, Surds and Logarithms, Sequences and Series, Articles and Determiners, Sentence Completion, Data Sufficiency, Blood Relations, Simple Interest and Compound Interest, Mixture and Alligation, Sequence and Series (Series), Speed Time and Distance, Algebra, Profit and Loss, Powers and Exponents (Surds and Indices), Permutation and Combination
  2. General Awareness: Physics, Chemistry, Biology, History, Geography, Polity, Indian Economy, Environment, Static General Knowledge, GK- Current Affairs, Artificial Intelligence, MS Office Applications, Terms & Abbreviations, Keyboard Shortcuts, Evolution of Indian Banking (Banking & Financial Awareness), Emerging Tech Trends
  3. Engineering Mathematics: Set Theory, Relations, Functions, Graph Theory, Group Theory, Propositional and Predicate Logic, Linear Algebra, Calculus, Probability, Statistics, Signal Space and Optimum Detection, Probability, Statistics
  4. Digital Logic: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation, Floating Point Rep, Number Systems and Codes, Registers and Counters, Boolean Algebra and Logic Gates, Microprocessors and Microcontrollers, Logic Families and CMOS Logic, Microprocessors, Logic Gates and Boolean Algebra, Minimization of Boolean Functions, Timing, Hazards and Testing, Logic Families, Combinational Logic Design, Arithmetic Circuits, Waveform Generators and Timer Circuits, Semiconductor Memories and Programmable Logic Devices, Combinational Logic Circuits, Latches and Flip-Flops, Finite State Machines, Sequential Circuits and State Machines, Minimization of Boolean Functions, Arithmetic Circuits, Latches and Flip-Flops, Number Systems and Codes, Registers and Counters
  5. Computer Organisation & Architecture: Basics of COA, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Control Unit Design, Programming the Basic Computer, Register Transfer and Microoperations, Semiconductor Memories and Programmable Logic, Processor Organization and Instructions, Memory Hierarchy and Input/Output, Registers and Counters
  6. Programming & Data Structures: 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, HTML Basics, OOPS with C++, Java, Object Oriented Technology
  7. Databases: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, SQL, Relational Calculus, Transaction Management, Concurrency Control, Database Recovery, Database Security & Authorization
  8. Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos
  9. Theory of Computation: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Moore & Mealy Machines, Pushdown Automata & CFG, Turing Machines, Complexity Theory
  10. Compiler Design: Intro to Compilers, Lexical Analysis, Grammar & CFG, Syntax Analysis: Top-Down, Syntax Analysis: Bottom-Up, Semantic Analysis & SDT, Intermediate Code Gen, Code Optimization, Run Time Environment
  11. Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, Disc Scheduling, File Management, Security, Windows OS, Distributed Systems, Unix OS, Linux OS
  12. Computer Networks: Introduction to CN, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware Basics, Data Communication, Website, Browser & Services, Web Design & Development, Network Security, Cyber Threats and Malware Attacks, Cyber Safety and Security
  13. Full Length Mock Tests:
  14. Sectional Tests:
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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