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Sunil Notebook
Interview Preparation
  • 📒Notebook
    • What is this about ?
  • System Design
    • 💡Key Concepts
      • 🌐Scalability
      • 🌐Latency Vs Throughput
      • 🌐Databases
      • 🌐CAP Theorem
      • 🌐ACID Transactions
      • 🌐Rate limiting
      • 🌐API Design
      • 🌐Strong Vs eventual consistency
      • 🌐Distributed tracing
      • 🌐Synchronous Vs asynchronous Communication
      • 🌐Batch Processing Vs Stream Processing
      • 🌐Fault Tolerance
    • 💎Building Blocks
      • 🔹Message
      • 🔹Cache
      • 🔹Load Balancer Vs API Gateway
    • 🖥️Introduction to system design
    • ⏱️Step By Step Guide
    • ♨️Emerging Technologies in System Design
    • ☑️System design component checklist
      • 🔷Azure
      • 🔶AWS
      • ♦️Google Cloud
    • 🧊LinkedIn feed Design
    • 🏏Scalable Emoji Broadcasting System - Hotstar
    • 💲UPI Payment System Design
    • 📈Stock Broker System Design - Groww
    • 🧑‍🤝‍🧑Designing Instagram's Collaborative Content Creation - Close Friends Only
    • 🌳Vending Machines - Over the air Systems
    • Reference Links
  • DSA
    • Topics
      • Introduction
      • Algorithm analysis
        • Asymptotic Notation
        • Memory
      • Sorting
        • Selection Sort
        • Insertion Sort
        • Merge Sort
        • Quick Sort
        • Quick'3 Sort
        • Shell Sort
        • Shuffle sort
        • Heap Sort
        • Arrays.sort()
        • Key Points
        • Problems
          • Reorder Log files
      • Stacks and Queues
        • Stack Implementations
        • Queue Implementations
        • Priority Queues
        • Problems
          • Dijkstra's two-stack algorithm
      • Binary Search Tree
        • Left Leaning Red Black Tree
          • Java Implementations
        • 2-3 Tree
          • Search Operation - 2-3 Tree
          • Insert Operation - 2-3 Tree
        • Geometric Applications of BST
      • B-Tree
      • Graphs
        • Undirected Graphs
        • Directed Graphs
        • Topological Sort
      • Union Find
        • Dynamic Connectivity
        • Quick Find - Eager Approach
        • Quick Find - Lazy Approach
        • Defects
        • Weighted Quick Union
        • Quick Union + path comparison
        • Amortized Analysis
      • Convex Hull
      • Binary Heaps and Priority Queue
      • Hash Table vs Binary Search Trees
  • Concurrency and Multithreading
    • Introduction
    • Visibility Problem
    • Interview Questions
    • References
      • System design
  • Design Patterns
    • ℹ️Introduction
    • 💠Classification of patterns
    • 1️⃣Structural Design Patterns
      • Adapter Design Pattern
      • Bridge Design Pattern
      • Composite Design Pattern
      • Decorator Design Pattern
      • Facade Design Pattern
      • Flyweight Design Pattern
      • Private Class Data Design Pattern
      • Proxy Design Pattern
    • 2️⃣Behavioral Design Patterns
      • Chain Of Responsibility
      • Command Design Pattern
      • Interpreter Design Pattern
      • Iterator Design Pattern
      • Mediator Design Pattern
      • Memento Design Pattern
      • Null Object Design Pattern
      • Observer Design Pattern
      • State Design Pattern
      • Strategy Design Pattern
      • Template Design Pattern
    • 3️⃣Creational Design Patterns
      • Abstract Factory Design Pattern
      • Builder Design Pattern
      • Factory Method Design Pattern
      • Object Pool Design Pattern
      • Prototype Design Pattern
      • Singleton Design Pattern
    • Java Pass by Value or Pass by Reference
  • Designing Data-Intensive Applications - O'Reilly
    • Read Me
    • 1️⃣Reliable, Scalable, and Maintainable Applications
      • Reliability
      • Scalability
      • Maintainability
      • References
    • 2️⃣Data Models and Query Languages
      • Read me
      • References
    • Miscellaneous
  • Preparation Manual
    • Disclaimer
    • What is it all about?
    • About a bunch of links
    • Before you start preparing
    • Algorithms and Coding
    • Concurrency and Multithreading
    • Programming Language and Fundementals
    • Best Practices and Experience
  • Web Applications
    • Typescript Guidelines
  • Research Papers
    • Research Papers
      • Real-Time Data Infrastructure at Uber
      • Scaling Memcache at Facebook
  • Interview Questions
    • Important links for preparation
    • Google Interview Questions
      • L4
        • Phone Interview Questions
      • L3
        • Interview Questions
      • Phone Screen Questions
  • Miscellaneous
    • 90 Days Preparation Schedule
    • My Preparation for Tech Giants
    • Top Product Based Companies
  • Links
    • Github
    • LinkedIn
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  • Data Structures and Algorithms
  • Data Structures
  • Algorithms

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  1. DSA
  2. Topics

Introduction

Data Structures and Algorithms

Data Structures

Data structures define a way to store data to allow it to be accessed and modified. How data is stored can cause some operations to be faster while others to be slower. There isn't a one size fits all type of data structure, it depends entirely on what you plan to do with the data. This course will survey some common data structures but in practice even these often need to augmented and refined to support a specific operation or application. The key is to understand how some common problems have been solved and techniques used to solve the problem.

Algorithms

Given a set of input data, an algorithm defines a sequence of steps that will produce an output. From our point of view an algorithm solves a computational problem, which specifies what the result of a given input should be. (Given X we want to produce Y).

An algorithm describes the steps needed to solve a problem. If we can prove that an algorithm can always produce the correct result for any given valid input then we consider the algorithm to be correct. Some algorithms do not guarantee correctness but they can still be very useful.

This Algorithm notes took from the Algorithms - Part I, Algorithms - Part II from Princeton University.

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Last updated 10 months ago

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