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DSA Channel: The Ultimate Destination for Learning Data Structures and Algorithms from Basics to Advanced
DSA mastery stands vital for successful software development and competitive programming in the current digital world that operates at high speeds. People at every skill level from beginner to advanced developer will find their educational destination at the DSA Channel.
Why is DSA Important?
Software development relies on data structures together with algorithms as its essential core components. Code optimization emerges from data structures and algorithms which produces better performance and leads to successful solutions of complex problems. Strategic knowledge of DSA serves essential needs for handling job interviews and coding competitions while enhancing logical thinking abilities. Proper guidance makes basic concepts of DSA both rewarding and enjoyable to study.
What Makes DSA Channel Unique?
The DSA Channel exists to simplify both data structures along algorithms and make them accessible to all users. Here’s why it stands out:
The channel provides step-by-step learning progress which conservatively begins by teaching arrays and linked lists and continues to dynamic programming and graph theory.
Each theoretical concept gets backed through coding examples practically to facilitate easier understanding and application in real-life situations.
Major companies like Google, Microsoft, and Amazon utilize DSA knowledge as part of their job recruiter process. Through their DSA Channel service candidates can perform mock interview preparation along with receiving technical interview problem-solving advice and interview cracking techniques.
Updates Occur Regularly Because the DSA Channel Matches the Ongoing Transformation in the Technology Industry. The content uses current algorithm field trends and new elements for constant updates.
DSAC channels will be covering the below key topics
DSA Channel makes certain you have clear ideas that are necessary for everything from the basics of data structures to the most sophisticated methods and use cases. Highlights :
1. Introduction Basic Data Structures
Fundamentals First, You Always Need To Start With the Basics. Some of the DSA Channel topics are:
Memories storing and manipulating elements of Arrays
Linked Lists — learn linked lists: Singly Linked lists Dually linked lists and Circular linked list
Implementing Stacks and Queues — linear data structure with these implementations.
Hash Table: Understanding Hashing and its impact in the retrieval of Data.
2. Advanced Data Structures
If you want to get Intense: the DSA channel has profound lessons:
Graph bases Types- Type of Graph Traversals: BFS, DFS
Heaps — Come to know about Min Heap and Max Heap
Index Tries – How to store and retrieve a string faster than the fastest possible.
3. Algorithms
This is especially true for efficient problem-solving. The DSA Channel discusses in-depth:
Searching Algorithms Binary Search and Linear Search etc.
Dynamic Programming: Optimization of subproblems
Recursion and Backtracking: How to solve a problem by recursion.
Graph Algorithms — Dijkstra, Bellman-Ford and Floyd-Warshall etc
4. Applications of DSA in Real life
So one of the unique things (About the DSA channel) is these real-world applications of his DSA Channel.
Instead of just teaching Theory the channel gives a hands-on to see how it's used in world DSA applications.
Learning about Database Management Systems — Indexing, Query Optimization, Storage Techniques
Operating Systems – study algorithms scheduling, memory management,t, and file systems.
Machine Learning and AI — Learning the usage of algorithms in training models, and optimizing computations.
Finance and Banking — data structures that help us in identifying risk scheme things, fraud detection, transaction processing, etc.
This hands-on approach to working out will ensure that learners not only know how to use these concepts in real-life examples.
How Arena Fincorp Benefits from DSA?
Arena Fincorp, a leading financial services provider, understands the importance of efficiency and optimization in the fintech sector. The financial solutions offered through Arena Fincorp operate under the same principles as data structures and algorithms which enhance coding operations. Arena Fincorp guarantees perfect financial transactions and data protection through its implementation of sophisticated algorithms. The foundational principles of DSA enable developers to build strong financial technological solutions for contemporary financial complications.
How to Get Started with DSA Channel?
New users of the DSA Channel should follow these instructions to maximize their experience:
The educational process should start with fundamental videos explaining arrays together with linked lists and stacks to establish a basic knowledge base.
The practice of DSA needs regular exercise and time to build comprehension. Devote specific time each day to find solutions for problems.
The platforms LeetCode, CodeChef, and HackerRank provide various DSA problems for daily problem-solving which boosts your skills.
Join community discussions where you can help learners by sharing solutions as well as working with fellow participants.
Students should do Mock Interviews through the DSA Channel to enhance their self-confidence and gain experience in actual interview situations.
The process of learning becomes more successful when people study together in a community. Through the DSA Channel students find an energetic learning community to share knowledge about doubts and project work and they exchange insight among themselves.
Conclusion
Using either data structures or algorithms in tech requires mastery so they have become mandatory in this sector. The DSA Channel delivers the best learning gateway that suits students as well as professionals and competitive programmers. Through their well-organized educational approach, practical experience and active learner network the DSA Channel builds a deep understanding of DSA with effective problem-solving abilities.
The value of data structures and algorithms and their optimized algorithms and efficient coding practices allows companies such as Arena Fincorp to succeed in their industries. New learners should begin their educational journey right now with the DSA Channel to master data structures and algorithms expertise.
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Data Structures and Algorithms from Zero to Hero and Crack Top Companies 100+ Interview questions (Java Coding)
What you’ll learn
Java Data Structures and Algorithms Masterclass
Learn, implement, and use different Data Structures
Learn, implement and use different Algorithms
Become a better developer by mastering computer science fundamentals
Learn everything you need to ace difficult coding interviews
Cracking the Coding Interview with 100+ questions with explanations
Time and Space Complexity of Data Structures and Algorithms
Recursion
Big O
Dynamic Programming
Divide and Conquer Algorithms
Graph Algorithms
Greedy Algorithms
Requirements
Basic Java Programming skills
Description
Welcome to the Java Data Structures and Algorithms Masterclass, the most modern, and the most complete Data Structures and Algorithms in Java course on the internet.
At 44+ hours, this is the most comprehensive course online to help you ace your coding interviews and learn about Data Structures and Algorithms in Java. You will see 100+ Interview Questions done at the top technology companies such as Apple, Amazon, Google, and Microsoft and how-to face Interviews with comprehensive visual explanatory video materials which will bring you closer to landing the tech job of your dreams!
Learning Java is one of the fastest ways to improve your career prospects as it is one of the most in-demand tech skills! This course will help you in better understanding every detail of Data Structures and how algorithms are implemented in high-level programming languages.
We’ll take you step-by-step through engaging video tutorials and teach you everything you need to succeed as a professional programmer.
After finishing this course, you will be able to:
Learn basic algorithmic techniques such as greedy algorithms, binary search, sorting, and dynamic programming to solve programming challenges.
Learn the strengths and weaknesses of a variety of data structures, so you can choose the best data structure for your data and applications
Learn many of the algorithms commonly used to sort data, so your applications will perform efficiently when sorting large datasets
Learn how to apply graph and string algorithms to solve real-world challenges: finding shortest paths on huge maps and assembling genomes from millions of pieces.
Why this course is so special and different from any other resource available online?
This course will take you from the very beginning to very complex and advanced topics in understanding Data Structures and Algorithms!
You will get video lectures explaining concepts clearly with comprehensive visual explanations throughout the course.
You will also see Interview Questions done at the top technology companies such as Apple, Amazon, Google, and Microsoft.
I cover everything you need to know about the technical interview process!
So whether you are interested in learning the top programming language in the world in-depth and interested in learning the fundamental Algorithms, Data Structures, and performance analysis that make up the core foundational skillset of every accomplished programmer/designer or software architect and is excited to ace your next technical interview this is the course for you!
And this is what you get by signing up today:
Lifetime access to 44+ hours of HD quality videos. No monthly subscription. Learn at your own pace, whenever you want
Friendly and fast support in the course Q&A whenever you have questions or get stuck
FULL money-back guarantee for 30 days!
This course is designed to help you to achieve your career goals. Whether you are looking to get more into Data Structures and Algorithms, increase your earning potential, or just want a job with more freedom, this is the right course for you!
The topics that are covered in this course.
Section 1 – Introduction
What are Data Structures?
What is an algorithm?
Why are Data Structures And Algorithms important?
Types of Data Structures
Types of Algorithms
Section 2 – Recursion
What is Recursion?
Why do we need recursion?
How does Recursion work?
Recursive vs Iterative Solutions
When to use/avoid Recursion?
How to write Recursion in 3 steps?
How to find Fibonacci numbers using Recursion?
Section 3 – Cracking Recursion Interview Questions
Question 1 – Sum of Digits
Question 2 – Power
Question 3 – Greatest Common Divisor
Question 4 – Decimal To Binary
Section 4 – Bonus CHALLENGING Recursion Problems (Exercises)
power
factorial
products array
recursiveRange
fib
reverse
palindrome
some recursive
flatten
capitalize first
nestedEvenSum
capitalize words
stringifyNumbers
collects things
Section 5 – Big O Notation
Analogy and Time Complexity
Big O, Big Theta, and Big Omega
Time complexity examples
Space Complexity
Drop the Constants and the nondominant terms
Add vs Multiply
How to measure the codes using Big O?
How to find time complexity for Recursive calls?
How to measure Recursive Algorithms that make multiple calls?
Section 6 – Top 10 Big O Interview Questions (Amazon, Facebook, Apple, and Microsoft)
Product and Sum
Print Pairs
Print Unordered Pairs
Print Unordered Pairs 2 Arrays
Print Unordered Pairs 2 Arrays 100000 Units
Reverse
O(N) Equivalents
Factorial Complexity
Fibonacci Complexity
Powers of 2
Section 7 – Arrays
What is an Array?
Types of Array
Arrays in Memory
Create an Array
Insertion Operation
Traversal Operation
Accessing an element of Array
Searching for an element in Array
Deleting an element from Array
Time and Space complexity of One Dimensional Array
One Dimensional Array Practice
Create Two Dimensional Array
Insertion – Two Dimensional Array
Accessing an element of Two Dimensional Array
Traversal – Two Dimensional Array
Searching for an element in Two Dimensional Array
Deletion – Two Dimensional Array
Time and Space complexity of Two Dimensional Array
When to use/avoid array
Section 8 – Cracking Array Interview Questions (Amazon, Facebook, Apple, and Microsoft)
Question 1 – Missing Number
Question 2 – Pairs
Question 3 – Finding a number in an Array
Question 4 – Max product of two int
Question 5 – Is Unique
Question 6 – Permutation
Question 7 – Rotate Matrix
Section 9 – CHALLENGING Array Problems (Exercises)
Middle Function
2D Lists
Best Score
Missing Number
Duplicate Number
Pairs
Section 10 – Linked List
What is a Linked List?
Linked List vs Arrays
Types of Linked List
Linked List in the Memory
Creation of Singly Linked List
Insertion in Singly Linked List in Memory
Insertion in Singly Linked List Algorithm
Insertion Method in Singly Linked List
Traversal of Singly Linked List
Search for a value in Single Linked List
Deletion of a node from Singly Linked List
Deletion Method in Singly Linked List
Deletion of entire Singly Linked List
Time and Space Complexity of Singly Linked List
Section 11 – Circular Singly Linked List
Creation of Circular Singly Linked List
Insertion in Circular Singly Linked List
Insertion Algorithm in Circular Singly Linked List
Insertion method in Circular Singly Linked List
Traversal of Circular Singly Linked List
Searching a node in Circular Singly Linked List
Deletion of a node from Circular Singly Linked List
Deletion Algorithm in Circular Singly Linked List
A method in Circular Singly Linked List
Deletion of entire Circular Singly Linked List
Time and Space Complexity of Circular Singly Linked List
Section 12 – Doubly Linked List
Creation of Doubly Linked List
Insertion in Doubly Linked List
Insertion Algorithm in Doubly Linked List
Insertion Method in Doubly Linked List
Traversal of Doubly Linked List
Reverse Traversal of Doubly Linked List
Searching for a node in Doubly Linked List
Deletion of a node in Doubly Linked List
Deletion Algorithm in Doubly Linked List
Deletion Method in Doubly Linked List
Deletion of entire Doubly Linked List
Time and Space Complexity of Doubly Linked List
Section 13 – Circular Doubly Linked List
Creation of Circular Doubly Linked List
Insertion in Circular Doubly Linked List
Insertion Algorithm in Circular Doubly Linked List
Insertion Method in Circular Doubly Linked List
Traversal of Circular Doubly Linked List
Reverse Traversal of Circular Doubly Linked List
Search for a node in Circular Doubly Linked List
Delete a node from Circular Doubly Linked List
Deletion Algorithm in Circular Doubly Linked List
Deletion Method in Circular Doubly Linked List
Entire Circular Doubly Linked List
Time and Space Complexity of Circular Doubly Linked List
Time Complexity of Linked List vs Arrays
Section 14 – Cracking Linked List Interview Questions (Amazon, Facebook, Apple, and Microsoft)
Linked List Class
Question 1 – Remove Dups
Question 2 – Return Kth to Last
Question 3 – Partition
Question 4 – Sum Linked Lists
Question 5 – Intersection
Section 15 – Stack
What is a Stack?
What and Why of Stack?
Stack Operations
Stack using Array vs Linked List
Stack Operations using Array (Create, isEmpty, isFull)
Stack Operations using Array (Push, Pop, Peek, Delete)
Time and Space Complexity of Stack using Array
Stack Operations using Linked List
Stack methods – Push, Pop, Peek, Delete, and isEmpty using Linked List
Time and Space Complexity of Stack using Linked List
When to Use/Avoid Stack
Stack Quiz
Section 16 – Queue
What is a Queue?
Linear Queue Operations using Array
Create, isFull, isEmpty, and enQueue methods using Linear Queue Array
Dequeue, Peek and Delete Methods using Linear Queue Array
Time and Space Complexity of Linear Queue using Array
Why Circular Queue?
Circular Queue Operations using Array
Create, Enqueue, isFull and isEmpty Methods in Circular Queue using Array
Dequeue, Peek and Delete Methods in Circular Queue using Array
Time and Space Complexity of Circular Queue using Array
Queue Operations using Linked List
Create, Enqueue and isEmpty Methods in Queue using Linked List
Dequeue, Peek and Delete Methods in Queue using Linked List
Time and Space Complexity of Queue using Linked List
Array vs Linked List Implementation
When to Use/Avoid Queue?
Section 17 – Cracking Stack and Queue Interview Questions (Amazon, Facebook, Apple, Microsoft)
Question 1 – Three in One
Question 2 – Stack Minimum
Question 3 – Stack of Plates
Question 4 – Queue via Stacks
Question 5 – Animal Shelter
Section 18 – Tree / Binary Tree
What is a Tree?
Why Tree?
Tree Terminology
How to create a basic tree in Java?
Binary Tree
Types of Binary Tree
Binary Tree Representation
Create Binary Tree (Linked List)
PreOrder Traversal Binary Tree (Linked List)
InOrder Traversal Binary Tree (Linked List)
PostOrder Traversal Binary Tree (Linked List)
LevelOrder Traversal Binary Tree (Linked List)
Searching for a node in Binary Tree (Linked List)
Inserting a node in Binary Tree (Linked List)
Delete a node from Binary Tree (Linked List)
Delete entire Binary Tree (Linked List)
Create Binary Tree (Array)
Insert a value Binary Tree (Array)
Search for a node in Binary Tree (Array)
PreOrder Traversal Binary Tree (Array)
InOrder Traversal Binary Tree (Array)
PostOrder Traversal Binary Tree (Array)
Level Order Traversal Binary Tree (Array)
Delete a node from Binary Tree (Array)
Entire Binary Tree (Array)
Linked List vs Python List Binary Tree
Section 19 – Binary Search Tree
What is a Binary Search Tree? Why do we need it?
Create a Binary Search Tree
Insert a node to BST
Traverse BST
Search in BST
Delete a node from BST
Delete entire BST
Time and Space complexity of BST
Section 20 – AVL Tree
What is an AVL Tree?
Why AVL Tree?
Common Operations on AVL Trees
Insert a node in AVL (Left Left Condition)
Insert a node in AVL (Left-Right Condition)
Insert a node in AVL (Right Right Condition)
Insert a node in AVL (Right Left Condition)
Insert a node in AVL (all together)
Insert a node in AVL (method)
Delete a node from AVL (LL, LR, RR, RL)
Delete a node from AVL (all together)
Delete a node from AVL (method)
Delete entire AVL
Time and Space complexity of AVL Tree
Section 21 – Binary Heap
What is Binary Heap? Why do we need it?
Common operations (Creation, Peek, sizeofheap) on Binary Heap
Insert a node in Binary Heap
Extract a node from Binary Heap
Delete entire Binary Heap
Time and space complexity of Binary Heap
Section 22 – Trie
What is a Trie? Why do we need it?
Common Operations on Trie (Creation)
Insert a string in Trie
Search for a string in Trie
Delete a string from Trie
Practical use of Trie
Section 23 – Hashing
What is Hashing? Why do we need it?
Hashing Terminology
Hash Functions
Types of Collision Resolution Techniques
Hash Table is Full
Pros and Cons of Resolution Techniques
Practical Use of Hashing
Hashing vs Other Data structures
Section 24 – Sort Algorithms
What is Sorting?
Types of Sorting
Sorting Terminologies
Bubble Sort
Selection Sort
Insertion Sort
Bucket Sort
Merge Sort
Quick Sort
Heap Sort
Comparison of Sorting Algorithms
Section 25 – Searching Algorithms
Introduction to Searching Algorithms
Linear Search
Linear Search in Python
Binary Search
Binary Search in Python
Time Complexity of Binary Search
Section 26 – Graph Algorithms
What is a Graph? Why Graph?
Graph Terminology
Types of Graph
Graph Representation
The graph in Java using Adjacency Matrix
The graph in Java using Adjacency List
Section 27 – Graph Traversal
Breadth-First Search Algorithm (BFS)
Breadth-First Search Algorithm (BFS) in Java – Adjacency Matrix
Breadth-First Search Algorithm (BFS) in Java – Adjacency List
Time Complexity of Breadth-First Search (BFS) Algorithm
Depth First Search (DFS) Algorithm
Depth First Search (DFS) Algorithm in Java – Adjacency List
Depth First Search (DFS) Algorithm in Java – Adjacency Matrix
Time Complexity of Depth First Search (DFS) Algorithm
BFS Traversal vs DFS Traversal
Section 28 – Topological Sort
What is Topological Sort?
Topological Sort Algorithm
Topological Sort using Adjacency List
Topological Sort using Adjacency Matrix
Time and Space Complexity of Topological Sort
Section 29 – Single Source Shortest Path Problem
what is Single Source Shortest Path Problem?
Breadth-First Search (BFS) for Single Source Shortest Path Problem (SSSPP)
BFS for SSSPP in Java using Adjacency List
BFS for SSSPP in Java using Adjacency Matrix
Time and Space Complexity of BFS for SSSPP
Why does BFS not work with Weighted Graph?
Why does DFS not work for SSSP?
Section 30 – Dijkstra’s Algorithm
Dijkstra’s Algorithm for SSSPP
Dijkstra’s Algorithm in Java – 1
Dijkstra’s Algorithm in Java – 2
Dijkstra’s Algorithm with Negative Cycle
Section 31 – Bellman-Ford Algorithm
Bellman-Ford Algorithm
Bellman-Ford Algorithm with negative cycle
Why does Bellman-Ford run V-1 times?
Bellman-Ford in Python
BFS vs Dijkstra vs Bellman Ford
Section 32 – All Pairs Shortest Path Problem
All pairs shortest path problem
Dry run for All pair shortest path
Section 33 – Floyd Warshall
Floyd Warshall Algorithm
Why Floyd Warshall?
Floyd Warshall with negative cycle,
Floyd Warshall in Java,
BFS vs Dijkstra vs Bellman Ford vs Floyd Warshall,
Section 34 – Minimum Spanning Tree
Minimum Spanning Tree,
Disjoint Set,
Disjoint Set in Java,
Section 35 – Kruskal’s and Prim’s Algorithms
Kruskal Algorithm,
Kruskal Algorithm in Python,
Prim’s Algorithm,
Prim’s Algorithm in Python,
Prim’s vs Kruskal
Section 36 – Cracking Graph and Tree Interview Questions (Amazon, Facebook, Apple, Microsoft)
Section 37 – Greedy Algorithms
What is a Greedy Algorithm?
Well known Greedy Algorithms
Activity Selection Problem
Activity Selection Problem in Python
Coin Change Problem
Coin Change Problem in Python
Fractional Knapsack Problem
Fractional Knapsack Problem in Python
Section 38 – Divide and Conquer Algorithms
What is a Divide and Conquer Algorithm?
Common Divide and Conquer algorithms
How to solve the Fibonacci series using the Divide and Conquer approach?
Number Factor
Number Factor in Java
House Robber
House Robber Problem in Java
Convert one string to another
Convert One String to another in Java
Zero One Knapsack problem
Zero One Knapsack problem in Java
Longest Common Sequence Problem
Longest Common Subsequence in Java
Longest Palindromic Subsequence Problem
Longest Palindromic Subsequence in Java
Minimum cost to reach the Last cell problem
Minimum Cost to reach the Last Cell in 2D array using Java
Number of Ways to reach the Last Cell with given Cost
Number of Ways to reach the Last Cell with given Cost in Java
Section 39 – Dynamic Programming
What is Dynamic Programming? (Overlapping property)
Where does the name of DC come from?
Top-Down with Memoization
Bottom-Up with Tabulation
Top-Down vs Bottom Up
Is Merge Sort Dynamic Programming?
Number Factor Problem using Dynamic Programming
Number Factor: Top-Down and Bottom-Up
House Robber Problem using Dynamic Programming
House Robber: Top-Down and Bottom-Up
Convert one string to another using Dynamic Programming
Convert String using Bottom Up
Zero One Knapsack using Dynamic Programming
Zero One Knapsack – Top Down
Zero One Knapsack – Bottom Up
Section 40 – CHALLENGING Dynamic Programming Problems
Longest repeated Subsequence Length problem
Longest Common Subsequence Length problem
Longest Common Subsequence problem
Diff Utility
Shortest Common Subsequence problem
Length of Longest Palindromic Subsequence
Subset Sum Problem
Egg Dropping Puzzle
Maximum Length Chain of Pairs
Section 41 – A Recipe for Problem Solving
Introduction
Step 1 – Understand the problem
Step 2 – Examples
Step 3 – Break it Down
Step 4 – Solve or Simplify
Step 5 – Look Back and Refactor
Section 41 – Wild West
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To download more paid courses for free visit course catalog where 1000+ paid courses available for free. You can get the full course into your device with just a single click. Follow the link above to download this course for free.
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Circular Queue - Insertion/Deletion - With Example in Hindi
This is a Hindi video tutorial that Circular Queue - Insertion/Deletion - With Example in Hindi. You will learn c program to implement circular queue using array in hindi, circular queue using array in c and circular queue in c using linked list. This video will make you understand circular queue tutorialspoint, circular queue algorithm, circular queue example, circular queue in data structure pdf, insertion and deletion in circular queue in data structure, implement circular queue in hindi, circular queue c, types of queue in hindi, double ended queue in hindi. If you are looking for Circular Queue in Hindi, Circular Queue Array Implementation in Hindi, Circular Queue Programming using Array (Hindi), Circular Queue | Data Structures in Hindi or Array Implementation of Circular Queue in Hindi, then this video is for you. You can download the program from the following link: http://bmharwani.com/circularqueuearr.c For more videos on Data Structures, visit: https://www.youtube.com/watch?v=TRXkTGu0n9g&list=PLuDr_vb2LpAxZdUV5gyea-TsEJ06k_-Aw&index=14 To get notification for latest videos uploaded, subscribe to my channel: https://youtube.com/c/bintuharwani To see more videos on different computer subjects, visit: http://bmharwani.com
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Learning JavaScript Data Structures and Algorithms - Second Edition - Loiane Groner
Learning JavaScript Data Structures and Algorithms - Second Edition Loiane Groner Genre: Computers Price: $35.99 Publish Date: June 23, 2016 Publisher: Packt Publishing Seller: Ingram DV LLC Hone your skills by learning classic data structures and algorithms in JavaScript About This Book • Understand common data structures and the associated algorithms, as well as the context in which they are used. • Master existing JavaScript data structures such as array, set and map and learn how to implement new ones such as stacks, linked lists, trees and graphs. • All concepts are explained in an easy way, followed by examples. Who This Book Is For If you are a student of Computer Science or are at the start of your technology career and want to explore JavaScript's optimum ability, this book is for you. You need a basic knowledge of JavaScript and programming logic to start having fun with algorithms. What You Will Learn • Declare, initialize, add, and remove items from arrays, stacks, and queues • Get the knack of using algorithms such as DFS (Depth-first Search) and BFS (Breadth-First Search) for the most complex data structures • Harness the power of creating linked lists, doubly linked lists, and circular linked lists • Store unique elements with hash tables, dictionaries, and sets • Use binary trees and binary search trees • Sort data structures using a range of algorithms such as bubble sort, insertion sort, and quick sort In Detail This book begins by covering basics of the JavaScript language and introducing ECMAScript 7, before gradually moving on to the current implementations of ECMAScript 6. You will gain an in-depth knowledge of how hash tables and set data structure functions, as well as how trees and hash maps can be used to search files in a HD or represent a database. This book is an accessible route deeper into JavaScript. Graphs being one of the most complex data structures you'll encounter, we'll also give you a better understanding of why and how graphs are largely used in GPS navigation systems in social networks. Toward the end of the book, you'll discover how all the theories presented by this book can be applied in real-world solutions while working on your own computer networks and Facebook searches. Style and approach This book gets straight to the point, providing you with examples of how a data structure or algorithm can be used and giving you real-world applications of the algorithm in JavaScript. With real-world use cases associated with each data structure, the book explains which data structure should be used to achieve the desired results in the real world. http://bit.ly/2VsJZnv
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September in Seville – 6 reasons to visit now!
The nights are drawing in and the mornings are dark and misty but it is not quite time to dust off the woolly jumpers yet. A visit to Seville in late September can provide the ultimate last blast of Summer sun and we couldn’t recommend it more! Here are six reasons why you should visit this beautiful city in the Autumn.
1. Amazing food
Seville offers everything from tightly packed, atmospheric little tapas bars with jamones hanging from the ceiling to more upscale, sophisticated dining in beautiful historic surroundings. Try La Quinta in Santa Catalina for incredible Galician beef and Bellota pork. This is more of a restaurant than a tapas bar and has a calm, grown-up atmosphere. It is set in a stunning Sevillian townhouse – one to leave the children at home for!
For an old-school tapas bar that has been in the same family since 1850 visit Casa Morales near the cathedral; be warned, it can be noisy and crowded but has an authentic atmosphere and tapas to die for! If you fancy a change from the typical Spanish cuisine then we would recommend La Gallina Bianca in Barrio Santa Cruz. An array of fresh pasta and pizzas – try the Formaggio de Capra e Patanegra pizza for fabulous Italian food with a Spanish twist.
2. Wonderful weather
With an average high of 32 degrees and only two days of rain, it is hard to argue with the September climate in Seville. The mornings are cool but by late afternoon temperatures will peak in the early thirties before cooling off again overnight. It is dry and hot with low humidity and little more than a gentle breeze. It is not as stifling as July and August when temperatures can peak at over 40 degrees – it is not known as ‘the frying pan of Europe’ for nothing! In fact, visitors in the mid-summer months may find that the Sevillians have sensibly escaped the heat and headed to the (relatively) cooler coastal regions like the beaches of Cádiz and Tarifa.
3. Easy to get around
Seville airport is an easy twenty minute taxi ride from the town centre and costs approximately 25 euros. UBER is starting to make an appearance although it is early days and we found the drivers to be lacking in local knowledge, which in a city with this many narrow, cobbled streets could be a problem. You’re better off with a local who knows the best and fastest routes around town. Though once you are settled in your hotel or apartment, walking is your best bet around the centre as it is usually quicker and gives you a chance to take in the beautiful surroundings.
There is also a decent bus network and tram line which runs south from Plaza Nueva and has four stops, covering a total distance of 1.4km, as well as an extensive network of cycle lanes and bicycle rental stations (belonging to the Sevici bike hire programme). Lime and Bird Scooter Hire companies also have a presence in Seville although there is little in the way of regulation or safety and no one seems to wear a helmet so you might be taking your life into your hands!
4. Child-friendly
Seville is a wonderful city to bring children to; the Spanish are on a completely different time schedule to the British but it is amazing how quickly children can adapt to the later evenings (and hopefully later mornings!). The Spanish, like many Europeans, are very child-friendly and welcoming; restaurants often have children’s menus or are happy to provide smaller portions of adult dishes. The tapas style of eating also appeals to children who can try small portions of lots of different foods and it is certainly good for encouraging adventurous eating. Churros (a Spanish version of donuts) dipped in chocolate sauce usually go down well.
A highlight for children is the amazing Isla Mágica Theme Park (to the north west of the city centre, a ten-minute taxi ride) which was built on the site of the Expo 1992 World Fair and has been open for just over twenty years. It comprises both a theme park, with Spain’s first inverted roller coaster (not for the faint-hearted!) and log flume rides but also a water park (a more recent addition) within the main park. Isla Mágica has six ‘worlds’ to explore with individual themes such as Puerto de Indias and Amazonia and each has its own rides and restaurants and offers hours and hours of fun and adventure. The most heart-stopping ride being El Desafio which is a free fall from a 68 metre high tower, not one for after lunch! We’d recommend booking your tickets in advance to avoid the risk of finding yourself in a long queue to get in.
Agua Mágica is the water park area and requires its own additional ticket. It offers respite from the heat so is a good late afternoon option and provides an array of open and closed tunnel slides and a lazy river for those wanting some time to relax and recover from the more frightening rides! Some of the larger slides have minimum weight and height restrictions which will limit the options for younger children although the lazy river and children’s splash pool area and wave pool should keep them happy for hours.
For somewhere quieter to chill out, Parque María Luisa, Jardines de Murillo and the Alameda de Hercules are good options for shady cool areas for an afternoon walk or visit to the playground. The largest is Parque María Luisa located on the site of the Expo 1929, whose crowning glory was the amazing, imposing Plaza de España. This is a huge semi-circular brick building with forty eight alcoves (one for each province of Spain), each with painted ceramic tiles depicting the province. The Plaza de España today is used as government offices and also for events and as a film location, for Star Wars among others. A canal runs around the front of the building where you can hire small rowing boats.
Whilst the María Luisa Park offers an opportunity for peace and quiet, there is also the option of an unusual biking excursion; you can hire two-, four- or six-man double-width bicycles with sun shade, each seat with their own pedals and race (or meander!) around the park. Horse and carriage rides are also an option.
5. Architectural beauty
Obviously the beauty of this city and the historic Moorish influence may be lost on little children but there is something magical about the architecture that has been well preserved. The Royal Alcazar of Seville, a UNESCO world heritage site showcasing an incredible mixture of Moorish and Christian influences, with its cool gardens and stunning mosaics and the Roman Catholic Cathedral with its Giralda (bell tower) are two such examples and well worth a visit. You can take a guided tour of the cathedral including seeing where Christopher Columbus is buried and also climb up to the Giralda Tower if you are feeling energetic, you will be rewarded with stunning views across the city. We would recommend buying tickets in advance to avoid queuing in the hot sunshine and at certain times entry to free so it is worth investigating this before you visit.
6. Luxury hotels
Seville has an array of luxury places to stay. The large, imposing Hotel Alfonso XIII opened in 1928 aspiring to be the best hotel in Europe and today is arguably the grandest and smartest place to stay in Seville. It is located next to the university which historically was an old tobacco factory made famous in Bizet’s opera Carmen. The Alfonso XIII offers fine dining as well as more casual snacks by the beautiful outdoor pool (only open to hotel guests) and lavish guest rooms and suites.
For something luxe but low key in style try Corral Del Rey (sister hotel to the highly recommended Hacienda San Raphael – an hour south of Seville in the Andalusian countryside) or Hospes Las Casas Del Rey De Baeza both much smaller and rustic chic in design. If you are looking for something more modern try the EME Catedral Hotel, as the name suggests just a stone’s throw from the famous landmark and with a stunning rooftop pool and bar.
Source
source https://highvoltagearea.com/september-in-seville-6-reasons-to-visit-now/?utm_source=rss&utm_medium=rss&utm_campaign=september-in-seville-6-reasons-to-visit-now
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GET THIS BOOK
Author:
Catherine Wilson
Published in: Princeton University Press Release Year: 1997 ISBN: 978-8193-24527-9 Pages: 145 Edition: 1st File Size: 17 MB File Type: pdf Language: English
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Description of Data Structures and Algorithms Made Easy
Please hold on! I know many people typically do not read the Preface of a book. But I strongly recommend that you read this particular Preface. It is not the main objective of Data Structures and Algorithms Made Easybook to present you with the theorems and proofs on data structures and algorithms. I have followed a pattern of improving the problem solutions with different complexities (for each problem, you will find multiple solutions with different, and reduced, complexities). Basically, it’s an enumeration of possible solutions. With this approach, even if you get a new question, it will show you a way to think about the possible solutions. You will find Data Structures and Algorithms Made Easy book useful for interview preparation, competitive exams preparation, and campus interview preparations.
As a job seeker, if you read the complete book, I am sure you will be able to challenge the interviewers. If you read it as an instructor, it will help you to deliver lectures with an approach that is easy to follow, and as a result your students will appreciate the fact that they have opted for Computer Science / Information Technology as their degree. Data Structures and Algorithms Made Easy book is also useful for Engineering degree students and Masters degree students during their academic preparations. In all the chapters you will see that there is more emphasis on problems and their analysis rather than on theory. In each chapter, you will first read about the basic required theory, which is then followed by a section on problem sets.
In total, there are approximately 700 algorithmic problems, all with solutions. If you read the book as a student preparing for competitive exams for Computer Science / Information Technology, the content covers all the required topics in full detail. While writing Data Structures and Algorithms Made Easy book, my main focus was to help students who are preparing for these exams. In all the chapters you will see more emphasis on problems and analysis rather than on theory. In each chapter, you will first see the basic required theory followed by various problems. For many problems, multiple solutions are provided with different levels of complexity. We start with the brute force solution and slowly move toward the best solution possible for that problem. For each problem, we endeavor to understand how much time the algorithm takes and how much memory the algorithm uses.
Table Contents of Data Structures and Algorithms Made Easy
1. Introduction
1.1 Variables
1.2 Data Types
1.3 Data Structures
1.4 Abstract Data Types (ADTs)
1.5 What is an Algorithm?
1.6 Why the Analysis of Algorithms?
1.7 Goal of the Analysis of Algorithms
1.8 What is Running Time Analysis?
1.9 How to Compare Algorithms
1.10 What is Rate of Growth?
1.11 Commonly Used Rates of Growth
1.12 Types of Analysis
1.13 Asymptotic Notation
1.14 Big-O Notation [Upper Bounding Function]
1.15 Omega-Q Notation [Lower Bounding Function]
1.16 Theta-Θ Notation [Order Function]
1.17 Important Notes
1.18 Why is it called Asymptotic Analysis?
1.19 Guidelines for Asymptotic Analysis
1.20 Simplyfying properties of asymptotic notations
1.21 Commonly used Logarithms and Summations
1.22 Master Theorem for Divide and Conquer Recurrences
1.23 Divide and Conquer Master Theorem: Problems & Solutions
1.24 Master Theorem for Subtract and Conquer Recurrences
1.25 Variant of Subtraction and Conquer Master Theorem
1.26 Method of Guessing and Confirming
1.27 Amortized Analysis
1.28 Algorithms Analysis: Problems & Solutions
2. Recursion and Backtracking
2.1 Introduction
2.2 What is Recursion?
2.3 Why Recursion?
2.4 Format of a Recursive Function
2.5 Recursion and Memory (Visualization)
2.6 Recursion versus Iteration
2.7 Notes on Recursion
2.8 Example Algorithms of Recursion
2.9 Recursion: Problems & Solutions
2.10 What is Backtracking?
2.11 Example Algorithms of Backtracking
2.12 Backtracking: Problems & Solutions
3. Linked Lists
3.1 What is a Linked List?
3.2 Linked Lists ADT
3.3 Why Linked Lists?
3.4 Arrays Overview
3.5 Comparison of Linked Lists with Arrays & Dynamic Arrays
3.6 Singly Linked Lists
3.7 Doubly Linked Lists
3.8 Circular Linked Lists
3.9 A Memory-efficient Doubly Linked List
3.10 Unrolled Linked Lists
3.11 Skip Lists
3.12 Linked Lists: Problems & Solutions
4. Stacks
4.1 What is a Stack?
4.2 How Stacks are used
4.3 Stack ADT
4.4 Applications
4.5 Implementation
4.6 Comparison of Implementations
4.7 Stacks: Problems & Solutions
5. Queues
5.1 What is a Queue?
5.2 How are Queues Used?
5.3 Queue ADT
5.4 Exceptions
5.5 Applications
5.6 Implementation
5.7 Queues: Problems & Solutions
6. Trees
6.1 What is a Tree?
6.2 Glossary
6.3 Binary Trees
6.4 Types of Binary Trees
6.5 Properties of Binary Trees
6.6 Binary Tree Traversals
6.7 Generic Trees (N-ary Trees)
6.8 Threaded Binary Tree Traversals (Stack or Queue-less Traversals)
6.9 Expression Trees
6.10 XOR Trees
6.11 Binary Search Trees (BSTs)
6.12 Balanced Binary Search Trees
6.13 AVL(Adelson-Velskii and Landis) Trees
6.14 Other Variations on Trees
7. Priority Queues and Heaps
7.1 What is a Priority Queue?
7.2 Priority Queue ADT
7.3 Priority Queue Applications
7.4 Priority Queue Implementations
7.5 Heaps and Binary Heaps
7.6 Binary Heaps
7.7 Heapsort
7.8 Priority Queues [Heaps]: Problems & Solutions
8. Disjoint Sets ADT
8.1 Introduction
8.2 Equivalence Relations and Equivalence Classes
8.3 Disjoint Sets ADT
8.4 Applications
8.5 Tradeoffs in Implementing Disjoint Sets ADT
8.8 Fast UNION Implementation (Slow FIND)
8.9 Fast UNION Implementations (Quick FIND)
8.10 Summary
8.11 Disjoint Sets: Problems & Solutions
9. Graph Algorithms
9.1 Introduction
9.2 Glossary
9.3 Applications of Graphs
9.4 Graph Representation
9.5 Graph Traversals
9.6 Topological Sort
9.7 Shortest Path Algorithms
9.8 Minimal Spanning Tree
9.9 Graph Algorithms: Problems & Solutions
10. Sorting
10.1 What is Sorting?
10.2 Why is Sorting Necessary?
10.3 Classification of Sorting Algorithms
10.4 Other Classifications
10.5 Bubble Sort
10.6 Selection Sort
10.7 Insertion Sort
10.8 Shell Sort
10.9 Merge Sort
10.10 Heap Sort
10.11 Quick Sort
10.12 Tree Sort
10.13 Comparison of Sorting Algorithms
10.14 Linear Sorting Algorithms
10.15 Counting Sort
10.16 Bucket Sort (or Bin Sort)
10.17 Radix Sort
10.18 Topological Sort
10.19 External Sorting
10.20 Sorting: Problems & Solutions
11. Searching
11.1 What is Searching?
11.2 Why do we need Searching?
11.3 Types of Searching
11.4 Unordered Linear Search
11.5 Sorted/Ordered Linear Search
11.6 Binary Search
11.7 Interpolation Search
11.8 Comparing Basic Searching Algorithms
11.9 Symbol Tables and Hashing
11.10 String Searching Algorithms
11.11 Searching: Problems & Solutions
12. Selection Algorithms [Medians]
12.1 What are Selection Algorithms?
12.2 Selection by Sorting
12.3 Partition-based Selection Algorithm
12.4 Linear Selection Algorithm - Median of Medians Algorithm
12.5 Finding the K Smallest Elements in Sorted Order
12.6 Selection Algorithms: Problems & Solutions
13. Symbol Tables
13.1 Introduction
13.2 What are Symbol Tables?
13.3 Symbol Table Implementations
13.4 Comparison Table of Symbols for Implementations
14. Hashing
14.1 What is Hashing?
14.2 Why Hashing?
14.3 HashTable ADT
14.4 Understanding Hashing
14.5 Components of Hashing
14.6 Hash Table
14.7 Hash Function
14.8 Load Factor
14.9 Collisions
14.10 Collision Resolution Techniques
14.11 Separate Chaining
14.12 Open Addressing
14.13 Comparison of Collision Resolution Techniques
14.14 How Hashing Gets O(1) Complexity?
14.15 Hashing Techniques
14.16 Problems for which Hash Tables are not suitable
14.17 Bloom Filters
14.18 Hashing: Problems & Solutions
15. String Algorithms
15.1 Introduction
15.2 String Matching Algorithms
15.3 Brute Force Method
15.4 Rabin-Karp String Matching Algorithm
15.5 String Matching with Finite Automata
15.6 KMP Algorithm
15.7 Boyer-Moore Algorithm
15.8 Data Structures for Storing Strings
15.9 Hash Tables for Strings
15.10 Binary Search Trees for Strings
15.11 Tries
15.12 Ternary Search Trees
15.13 Comparing BSTs, Tries and TSTs
15.14 Suffix Trees
15.15 String Algorithms: Problems & Solutions
16. Algorithms Design Techniques
16.1 Introduction
16.2 Classification
16.3 Classification by Implementation Method
16.4 Classification by Design Method
16.5 Other Classifications
17. Greedy Algorithms
17.1 Introduction
17.2 Greedy Strategy
17.3 Elements of Greedy Algorithms
17.4 Does Greedy Always Work?
17.5 Advantages and Disadvantages of Greedy Method
17.6 Greedy Applications
17.7 Understanding Greedy Technique
17.8 Greedy Algorithms: Problems & Solutions
18. Divide and Conquer Algorithms
18.1 Introduction
18.2 What is the Divide and Conquer Strategy?
18.3 Does Divide and Conquer Always Work?
18.4 Divide and Conquer Visualization
18.5 Understanding Divide and Conquer
18.6 Advantages of Divide and Conquer
18.7 Disadvantages of Divide and Conquer
18.8 Master Theorem
18.9 Divide and Conquer Applications
18.10 Divide and Conquer: Problems & Solutions
19. Dynamic Programming
19.1 Introduction
19.2 What is Dynamic Programming Strategy?
19.3 Properties of Dynamic Programming Strategy
19.4 Can Dynamic Programming Solve All Problems?
19.5 Dynamic Programming Approaches
19.6 Examples of Dynamic Programming Algorithms
19.7 Understanding Dynamic Programming
19.8 Longest Common Subsequence
19.9 Dynamic Programming: Problems & Solutions
20. Complexity Classes
20.1 Introduction
20.2 Polynomial/Exponential Time
20.3 What is a Decision Problem?
20.4 Decision Procedure
20.5 What is a Complexity Class?
20.6 Types of Complexity Classes
20.7 Reductions
20.8 Complexity Classes: Problems & Solutions
21. Miscellaneous Concepts
21.1 Introduction
21.2 Hacks on Bit-wise Programming
21.3 Other Programming Questions
References
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Allocation Efficiency in High-Performance Go Services
Memory management can be tricky, to say the least. However, after reading the literature, one might be led to believe that all the problems are solved: sophisticated automated systems that manage the lifecycle of memory allocation free us from these burdens.
However, if you’ve ever tried to tune the garbage collector of a JVM program or optimized the allocation pattern of a Go codebase, you understand that this is far from a solved problem. Automated memory management helpfully rules out a large class of errors, but that’s only half the story. The hot paths of our software must be built in a way that these systems can work efficiently.
We found inspiration to share our learnings in this area while building a high-throughput service in Go called Centrifuge, which processes hundreds of thousands of events per second. Centrifuge is a critical part of Segment’s infrastructure. Consistent, predictable behavior is a requirement. Tidy, efficient, and precise use of memory is a major part of achieving this consistency.
In this post we’ll cover common patterns that lead to inefficiency and production surprises related to memory allocation as well as practical ways of blunting or eliminating these issues. We’ll focus on the key mechanics of the allocator that provide developers a way to get a handle on their memory usage.
Our first recommendation is to avoid premature optimization. Go provides excellent profiling tools that can point directly to the allocation-heavy portions of a code base. There’s no reason to reinvent the wheel, so instead of taking readers through it here, we’ll refer to this excellent post on the official Go blog. It has a solid walkthrough of using pprof for both CPU and allocation profiling. These are the same tools that we use at Segment to find bottlenecks in our production Go code, and should be the first thing you reach for as well.
Use data to drive your optimization!
Analyzing Our Escape
Go manages memory allocation automatically. This prevents a whole class of potential bugs, but it doesn’t completely free the programmer from reasoning about the mechanics of allocation. Since Go doesn’t provide a direct way to manipulate allocation, developers must understand the rules of this system so that it can be maximized for our own benefit.
If you remember one thing from this entire post, this would be it: stack allocation is cheap and heap allocation is expensive. Now let’s dive into what that actually means.
Go allocates memory in two places: a global heap for dynamic allocations and a local stack for each goroutine. Go prefers allocation on the stack — most of the allocations within a given Go program will be on the stack. It’s cheap because it only requires two CPU instructions: one to push onto the stack for allocation, and another to release from the stack.
Unfortunately not all data can use memory allocated on the stack. Stack allocation requires that the lifetime and memory footprint of a variable can be determined at compile time. Otherwise a dynamic allocation onto the heap occurs at runtime. malloc must search for a chunk of free memory large enough to hold the new value. Later down the line, the garbage collector scans the heap for objects which are no longer referenced. It probably goes without saying that it is significantly more expensive than the two instructions used by stack allocation.
The compiler uses a technique called escape analysis to choose between these two options. The basic idea is to do the work of garbage collection at compile time. The compiler tracks the scope of variables across regions of code. It uses this data to determine which variables hold to a set of checks that prove their lifetime is entirely knowable at runtime. If the variable passes these checks, the value can be allocated on the stack. If not, it is said to escape, and must be heap allocated.
The rules for escape analysis aren’t part of the Go language specification. For Go programmers, the most straightforward way to learn about these rules is experimentation. The compiler will output the results of the escape analysis by building with go build -gcflags '-m'. Let’s look at an example:
package main import "fmt" func main() { x := 42 fmt.Println(x) }
$ go build -gcflags '-m' ./main.go # command-line-arguments ./main.go:7: x escapes to heap ./main.go:7: main ... argument does not escape
See here that the variable x “escapes to the heap,” which means it will be dynamically allocated on the heap at runtime. This example is a little puzzling. To human eyes, it is immediately obvious that x will not escape the main() function. The compiler output doesn’t explain why it thinks the value escapes. For more details, pass the -m option multiple times, which makes the output more verbose:
$ go build -gcflags '-m -m' ./main.go # command-line-arguments ./main.go:5: cannot inline main: non-leaf function ./main.go:7: x escapes to heap ./main.go:7: from ... argument (arg to ...) at ./main.go:7 ./main.go:7: from *(... argument) (indirection) at ./main.go:7 ./main.go:7: from ... argument (passed to call[argument content escapes]) at ./main.go:7 ./main.go:7: main ... argument does not escape
Ah, yes! This shows that x escapes because it is passed to a function argument which escapes itself — more on this later.
The rules may continue to seem arbitrary at first, but after some trial and error with these tools, patterns do begin to emerge. For those short on time, here’s a list of some patterns we’ve found which typically cause variables to escape to the heap:
Backing arrays of slices that get reallocated because an append would exceed their capacity. In cases where the initial size of a slice is known at compile time, it will begin its allocation on the stack. If this slice’s underlying storage must be expanded based on data only known at runtime, it will be allocated on the heap.
In our experience these four cases are the most common sources of mysterious dynamic allocation in Go programs. Fortunately there are solutions to these problems! Next we’ll go deeper into some concrete examples of how we’ve addressed memory inefficiencies in our production software.
Some Pointers
The rule of thumb is: pointers point to data allocated on the heap. Ergo, reducing the number of pointers in a program reduces the number of heap allocations. This is not an axiom, but we’ve found it to be the common case in real-world Go programs.
It has been our experience that developers become proficient and productive in Go without understanding the performance characteristics of values versus pointers. A common hypothesis derived from intuition goes something like this: “copying values is expensive, so instead I’ll use a pointer.” However, in many cases copying a value is much less expensive than the overhead of using a pointer. “Why” you might ask?
Copying objects within a cache line is the roughly equivalent to copying a single pointer. CPUs move memory between caching layers and main memory on cache lines of constant size. On x86 this is 64 bytes. Further, Go uses a technique called Duff’s device to make common memory operations like copies very efficient.
Pointers should primarily be used to reflect ownership semantics and mutability. In practice, the use of pointers to avoid copies should be infrequent. Don’t fall into the trap of premature optimization. It’s good to develop a habit of passing data by value, only falling back to passing pointers when necessary. An extra bonus is the increased safety of eliminating nil.
Reducing the number of pointers in a program can yield another helpful result as the garbage collector will skip regions of memory that it can prove will contain no pointers. For example, regions of the heap which back slices of type []byte aren’t scanned at all. This also holds true for arrays of struct types that don’t contain any fields with pointer types.
Not only does reducing pointers result in less work for the garbage collector, it produces more cache-friendly code. Reading memory moves data from main memory into the CPU caches. Caches are finite, so some other piece of data must be evicted to make room. Evicted data may still be relevant to other portions of the program. The resulting cache thrashing can cause unexpected and sudden shifts the behavior of production services.
Digging for Pointer Gold
Reducing pointer usage often means digging into the source code of the types used to construct our programs. Our service, Centrifuge, retains a queue of failed operations to retry as a circular buffer with a set of data structures that look something like this:
type retryQueue struct { buckets [][]retryItem // each bucket represents a 1 second interval currentTime time.Time currentOffset int } type retryItem struct { id ksuid.KSUID // ID of the item to retry time time.Time // exact time at which the item has to be retried }
The size of the outer array in buckets is constant, but the number of items in the contained []retryItem slice will vary at runtime. The more retries, the larger these slices will grow.
Digging into the implementation details of each field of a retryItem, we learn that KSUID is a type alias for [20]byte, which has no pointers, and therefore can be ruled out. currentOffset is an int, which is a fixed-size primitive, and can also be ruled out. Next, looking at the implementation of time.Time type[1]:
type Time struct { sec int64 nsec int32 loc *Location // pointer to the time zone structure }
The time.Time struct contains an internal pointer for the loc field. Using it within the retryItem type causes the GC to chase the pointers on these structs each time it passes through this area of the heap.
We’ve found that this is a typical case of cascading effects under unexpected circumstances. During normal operation failures are uncommon. Only a small amount of memory is used to store retries. When failures suddenly spike, the number of items in the retry queue can increase by thousands per second, bringing with it a significantly increased workload for the garbage collector.
For this particular use case, the timezone information in time.Time isn’t necessary. These timestamps are kept in memory and are never serialized. Therefore these data structures can be refactored to avoid this type entirely:
type retryItem struct { id ksuid.KSUID nsec uint32 sec int64 } func (item *retryItem) time() time.Time { return time.Unix(item.sec, int64(item.nsec)) } func makeRetryItem(id ksuid.KSUID, time time.Time) retryItem { return retryItem{ id: id, nsec: uint32(time.Nanosecond()), sec: time.Unix(), }
Now the retryItem doesn’t contain any pointers. This dramatically reduces the load on the garbage collector as the entire footprint of retryItem is knowable at compile time[2].
Pass Me a Slice
Slices are fertile ground for inefficient allocation behavior in hot code paths. Unless the compiler knows the size of the slice at compile time, the backing arrays for slices (and maps!) are allocated on the heap. Let’s explore some ways to keep slices on the stack and avoid heap allocation.
Centrifuge uses MySQL intensively. Overall program efficiency depends heavily on the efficiency of the MySQL driver. After using pprof to analyze allocator behavior, we found that the code which serializes time.Time values in Go’s MySQL driver was particularly expensive.
The profiler showed a large percentage of the heap allocations were in code that serializes a time.Time value so that it can be sent over the wire to the MySQL server.
This particular code was calling the Format() method on time.Time, which returns a string. Wait, aren’t we talking about slices? Well, according to the official Go blog, a string is just a “read-only slices of bytes with a bit of extra syntactic support from the language.” Most of the same rules around allocation apply!
The profile tells us that a massive 12.38% of the allocations were occurring when running this Format method. What does Format do?
It turns out there is a much more efficient way to do the same thing that uses a common pattern across the standard library. While the Format() method is easy and convenient, code using AppendFormat() can be much easier on the allocator. Peering into the source code for the time package, we notice that all internal uses are AppendFormat() and not Format(). This is a pretty strong hint that AppendFormat() is going to yield more performant behavior.
In fact, the Format method just wraps the AppendFormat method:
func (t Time) Format(layout string) string { const bufSize = 64 var b []byte max := len(layout) + 10 if max < bufSize { var buf [bufSize]byte b = buf[:0] } else { b = make([]byte, 0, max) } b = t.AppendFormat(b, layout) return string(b) }
Most importantly, AppendFormat() gives the programmer far more control over allocation. It requires passing the slice to mutate rather than returning a string that it allocates internally like Format(). Using AppendFormat() instead of Format() allows the same operation to use a fixed-size allocation[3] and thus is eligible for stack placement.
Let’s look at the change we upstreamed to Go’s MySQL driver in this PR.
The first thing to notice is that var a [64]byte is a fixed-size array. Its size is known at compile-time and its use is scoped entirely to this function, so we can deduce that this will be allocated on the stack.
However, this type can’t be passed to AppendFormat(), which accepts type []byte. Using the a[:0] notation converts the fixed-size array to a slice type represented by b that is backed by this array. This will pass the compiler’s checks and be allocated on the stack.
Most critically, the memory that would otherwise be dynamically allocated is passed to AppendFormat(), a method which itself passes the compiler’s stack allocation checks. In the previous version, Format() is used, which contains allocations of sizes that can’t be determined at compile time and therefore do not qualify for stack allocation.
The result of this relatively small change massively reduced allocations in this code path! Similar to using the “Append pattern” in the MySQL driver, an Append() method was added to the KSUID type in this PR. Converting our hot paths to use Append() on KSUID against a fixed-size buffer instead of the String() method saved a similarly significant amount of dynamic allocation. Also noteworthy is that the strconv package has equivalent append methods for converting strings that contain numbers to numeric types.
Interface Types and You
It is fairly common knowledge that method calls on interface types are more expensive than those on struct types. Method calls on interface types are executed via dynamic dispatch. This severely limits the ability for the compiler to determine the way that code will be executed at runtime. So far we’ve largely discussed shaping code so that the compiler can understand its behavior best at compile-time. Interface types throw all of this away!
Unfortunately interface types are a very useful abstraction — they let us write more flexible code. A common case of interfaces being used in the hot path of a program is the hashing functionality provided by standard library’s hash package. The hash package defines a set of generic interfaces and provides several concrete implementations. Let’s look at an example:
package main import ( "fmt" "hash/fnv" ) func hashIt(in string) uint64 { h := fnv.New64a() h.Write([]byte(in)) out := h.Sum64() return out } func main() { s := "hello" fmt.Printf("The FNV64a hash of '%v' is '%v'\n", s, hashIt(s)) }
Building this code with escape analysis output yields the following:
./foo1.go:9:17: inlining call to fnv.New64a ./foo1.go:10:16: ([]byte)(in) escapes to heap ./foo1.go:9:17: hash.Hash64(&fnv.s·2) escapes to heap ./foo1.go:9:17: &fnv.s·2 escapes to heap ./foo1.go:9:17: moved to heap: fnv.s·2 ./foo1.go:8:24: hashIt in does not escape ./foo1.go:17:13: s escapes to heap ./foo1.go:17:59: hashIt(s) escapes to heap ./foo1.go:17:12: main ... argument does not escape
This means the hash object, input string, and the []byte representation of the input will all escape to the heap. To human eyes these variables obviously do not escape, but the interface type ties the compilers hands. And there’s no way to safely use the concrete implementations without going through the hash package’s interfaces. So what is an efficiency-concerned developer to do?
We ran into this problem when constructing Centrifuge, which performs non-cryptographic hashing on small strings in its hot paths. So we built the fasthash library as an answer. It was straightforward to build — the code that does the hard work is part of the standard library. fasthash just repackages the standard library code with an API that is usable without heap allocations.
Let’s examine the fasthash version of our test program:
package main import ( "fmt" "http://ift.tt/2w635Uc" ) func hashIt(in string) uint64 { out := fnv1a.HashString64(in) return out } func main() { s := "hello" fmt.Printf("The FNV64a hash of '%v' is '%v'\n", s, hashIt(s)) }
And the escape analysis output?
./foo2.go:9:24: hashIt in does not escape ./foo2.go:16:13: s escapes to heap ./foo2.go:16:59: hashIt(s) escapes to heap ./foo2.go:16:12: main ... argument does not escape
The only remaining escapes are due to the dynamic nature of the fmt.Printf() function. While we’d strongly prefer to use the standard library from an ergonomics perspective, in some cases it is worth the trade-off to go to such lengths for allocation efficiency.
One Weird Trick
Our final anecdote is more amusing than practical. However, it is a useful example for understanding the mechanics of the compiler’s escape analysis. When reviewing the standard library for the optimizations covered, we came across a rather curious piece of code.
// noescape hides a pointer from escape analysis. noescape is // the identity function but escape analysis doesn't think the // output depends on the input. noescape is inlined and currently // compiles down to zero instructions. // USE CAREFULLY! //go:nosplit func noescape(p unsafe.Pointer) unsafe.Pointer { x := uintptr(p) return unsafe.Pointer(x ^ 0) }
This function will hide the passed pointer from the compiler’s escape analysis functionality. What does this actually mean though? Well, let’s set up an experiment to see!
package main import ( "unsafe" ) type Foo struct { S *string } func (f *Foo) String() string { return *f.S } type FooTrick struct { S unsafe.Pointer } func (f *FooTrick) String() string { return *(*string)(f.S) } func NewFoo(s string) Foo { return Foo{S: &s} } func NewFooTrick(s string) FooTrick { return FooTrick{S: noescape(unsafe.Pointer(&s))} } func noescape(p unsafe.Pointer) unsafe.Pointer { x := uintptr(p) return unsafe.Pointer(x ^ 0) } func main() { s := "hello" f1 := NewFoo(s) f2 := NewFooTrick(s) s1 := f1.String() s2 := f2.String() }
This code contains two implementations that perform the same task: they hold a string and return the contained string using the String() method. However, the escape analysis output from the compiler shows us that the FooTrick version does not escape!
./foo3.go:24:16: &s escapes to heap ./foo3.go:23:23: moved to heap: s ./foo3.go:27:28: NewFooTrick s does not escape ./foo3.go:28:45: NewFooTrick &s does not escape ./foo3.go:31:33: noescape p does not escape ./foo3.go:38:14: main &s does not escape ./foo3.go:39:19: main &s does not escape ./foo3.go:40:17: main f1 does not escape ./foo3.go:41:17: main f2 does not escape
These two lines are the most relevant:
./foo3.go:24:16: &s escapes to heap ./foo3.go:23:23: moved to heap: s
This is the compiler recognizing that the NewFoo() function takes a reference to the string and stores it in the struct, causing it to escape. However, no such output appears for the NewFooTrick() function. If the call to noescape() is removed, the escape analysis moves the data referenced by the FooTrick struct to the heap. What is happening here?
func noescape(p unsafe.Pointer) unsafe.Pointer { x := uintptr(p) return unsafe.Pointer(x ^ 0) }
The noescape() function masks the dependency between the input argument and the return value. The compiler does not think that p escapes via x because the uintptr() produces a reference that is opaque to the compiler. The builtin uintptr type’s name may lead one to believe this is a bona fide pointer type, but from the compiler’s perspective it is just an integer that just happens to be large enough to store a pointer. The final line of code constructs and returns an unsafe.Pointer value from a seemingly arbitrary integer value. Nothing to see here folks!
noescape() is used in dozens of functions in the runtime package that use unsafe.Pointer. It is useful in cases where the author knows for certain that data referenced by an unsafe.Pointer doesn’t escape, but the compiler naively thinks otherwise.
Just to be clear — we’re not recommending the use of such a technique. There’s a reason why the package being referenced is called unsafe and the source code contains the comment “USE CAREFULLY!”
Takeaways
Building a state-intensive Go service that must be efficient and stable under a wide range of real world conditions has been a tremendous learning experience for our team. Let’s review our key learnings:
Don’t prematurely optimize! Use data to drive your optimization work.
Stack allocation is cheap, heap allocation is expensive.
Understanding the rules of escape analysis allows us to write more efficient code.
Pointers make stack allocation mostly infeasible.
Look for APIs that provide allocation control in performance-critical sections of code.
Use interface types sparingly in hot paths.
We’ve used these relatively straightforward techniques to improve our own Go code, and hope that others find these hard-earned learnings helpful in constructing their own Go programs.
Happy coding, fellow gophers!
Notes
[1] The time.Time struct type has changed in Go 1.9.
[2] You may have also noticed that we switched the order of the nsec and sec fields, the reason is that due to the alignment rules, Go would generate a 4 bytes padding after the KSUID. The nanosecond field happens to be 4 bytes so by placing it after the KSUID Go doesn’t need to add padding anymore because the fields are already aligned. This dropped the size of the data structure from 40 to 32 bytes, reducing by 20% the memory used by the retry queue.
[3] Fixed-size arrays in Go are similar to slices, but have their size encoded directly into their type signature. While most APIs accept slices and not arrays, slices can be made out of arrays!
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Gaudi Research
Gaudi Research
Barcelona is rich in culture and modern history and continues to be a top destination for travel hungry tourists. The city rests on the Mediterranean coast of Spain, less than 100 miles from southern France. Like most European cities during the industrial revolution, Barcelona enjoyed growth and expansion. It was considered cosmopolitan and the home of many creative artists, such as Joan Miro and Pablo Picasso. Most of the buildings of note in Barcelona were designed by Catalan architect Antoni Gaudi.
Gaudi
Antoni Gaudi was born south of Barcelona in the town of Reus, Tarragona, in 1852. His parents were Francesc Gaudi, an ironmonger/coppersmith and his mother Antonia; he had four siblings. After serving in the Spanish Military Service, Antoni trained to be an architect in his home town. There are lots of variations as to the name of the school he attended, but the one that occurs most frequently is Escola Technica Superior d’Architectura.
After graduating, he exhibited at the Paris World Fair in 1878. His designs attracted attention, leading to some of his first commissions, namely with the Guell family whom he worked with on various designs and later, La Sagrada Familia Cathedral, which he worked on throughout his life. The cathedral is still under construction and is expected to be complete 100 years after his death, in 2026.
Gaudi was designing during the Modernism period (1890 – 1910). This was linked to the distinct Art Nouveau style, where inspiration was derived mainly from nature, both in the design and the colour of his buildings.
His style was buildings on a grand scale. They were impressive and included elements of fantasy, gothic, medieval, modernist, surrealist and romantic. Many of the same elements also showed in his other buildings, such as the high curved archways, blank spaces filled with light and colourful mosaics.
Gaudi’s work in Barcelona;
· La Sagrada Familia (1882 - @2026)
· Casa Vicens (1883 – 1888)
· Palau Guell (1885 – 1890)
· Casa Calvet (1898 – 1900)
· Casa Figuera (1900 – 1909)
· Park Guell (1900 – 1914)
· Casa Batllo (1905 – 1907)
· Casa Mila (La Pedrera) – (1905 – 1907)
· Colonia Guell (1908 – 1914)
Outside of Barcelona, he was commissioned for;
· Casa Botines (Leon, 1891 – 1893)
· El Capricho (Comillas, 1883 – 1885)
· Episcopal Palace of Astorga (Leon, 1883 – 1913)
· Bodegas Guell (Leon, 1891 – 1893)
· Artigas Gardens (La Poblet de Lillet, 1905 – 1907)
You could call Gaudi a pioneer of his profession and his style was way ahead of the time. He was one of the first architects to use biomimicry, finding a solution to design problems, without harming the environment. This uses techniques which can be found in nature and as an example, Gaudi took inspiration from the design of tree canopies that branch out, to help with the vault structure in La Sagrada Familia. He was one of the first architects to use the hyperbolic and paraboloid technique, which have the benefit of enhancing the stability of tall structures. A series of three dimensional intersecting straight lines make the building very stable and if one part of the building were to become damaged, it wouldn’t affect the remaining structure. He was also a fan of recycling as in his many mosaics, he reused glass and ceramic tiles to create his colourful designs.
Casa Batllo
I visited Casa Batllo in the summer of 2016 and the first thing I noticed while queuing at the entrance, were the unique hexagonal pavement tiles. Each section uniformly creates a different pattern when layed next to the other tiles. Even though they are flat, they have a three-dimensional quality about them. On their own, they seem to be a random, complicated design, with dots, swirls, wavy lines and curves. When put together, it forms a never ending carpet of interconnecting patterns, forming a different design from each point.
The history of the tiles was a little different to what I had imagined; they were laid initially in 1976, and were a glazed blueish/green. These were prone to breakage and were replaced with the current, more hard wearing style between 1997 and 2001. It takes seven of these hexagonal tiles to make up the complete pattern. Some were incorrectly laid, which spoils the effect, but some art students from the local university campaigned for them to be re-layed and they began to be replaced in 2014. The design does belong to Gaudi though and they were originally destined for the floor inside the house, but instead were used in Casa Mila (La Pedrera).
Walking around the inside of Casa Batllo, its very evident that the sea is part of his inspiration. Inside, the glass above the doorways are reminiscent of sea shells and the lights on the ceiling are formed at the centre of a whirlpool like shape. The colours, including the vast stairwell, are sea blues and greens. The ceilings are smooth, curved lines, which seem to reflect all the available light and in some rooms, there are shapes that seem like water dropping from above, perhaps from a cave, onto your head. Some of the heavy wooden doors have shapes carved into them which appear to be seahorses. Perhaps even the honey coloured wood on the staircase, skirting board and doors, could be compared to the sandy seabed.
La Pedrera
The doors of La Pedrera were designed to allow as much light as possible into the building, together with ventilation. It is an interesting design, reminiscent of an ammonite fossil, but perhaps not as structured. Each section of the door was glazed separately, which was an ingenious design as sheets of glass on this scale were not yet available. This large door opens onto the street and is divided into three different sections. At the side is an entrance for pedestrians, while the main, larger section was to allow access for vehicles and the fan lights at the top allow for ventilation in warmer months.
La Pedrera was very modern for its time, as are most of the Gaudi buildings. Cars could gain access through the doors, down a curved ramp and into one of two car parks. Underground car parks were a first for Barcelona at the time, but now common place in the city. One of the car parks was a circular design, with exposed steel ‘spokes’ in the roof, similar to the design of spokes from a bicycle wheel. This helps to distribute weight, so the usual pillars dotted about a room to support the roof weren’t required. Instead, the pillars are placed underneath the edge of the spokes, maximising the available space in the centre, to park.
The iconic decorative stone figures on the roof of the building, some of which resemble gothic style soldiers, serve an important purpose. They cleverly disguise stairwells, vents and chimneys and some are carefully covered in mosaics. As with the rest of the buildings style, they have curved sides and there are no straight lines in sight. Some of them form a three dimensional cross at the top, which is known as Gaudis ‘four armed cross’. Night is the best time to visit the roof top of La Pedrera, due to the colourful lighting effects and projected images on display, together with the accompanying dramatic music.
Park Guell
Built by Gaudi between 1900 and 1914, the Park is accessed first by public transport which stops short of the venue, then up a fairly steep hill. It was initially privately owned by Eusebi Guell, and transferred to the citys ownership in 1926, after Guells death. Once inside, the park winds around shady areas which keeps you out of the hot sun. Despite the visitors, there is still an air of tranquillity. At the top on the terrace, there is a large curved seated zone where brightly coloured mosaics decorate the area. This is said to emulate a serpent, probably linking to the dragon at the lower entrance to the park. Under the terrace, is the lower court with an array of tall columns supporting the structure. Looking up, decorative circular ceiling roses can be seen, adorned with more brightly coloured mosaics. This is rather grand, with stairs curving up, either side of the dragon with the columns of the lower terrace greeting you majestically at the top.
The site was meant to be a housing development, but never took off due to lack of interest from potential buyers. Subsequently, there are only two houses on the site; one is at the pavilion entrance and has a decorative roof like many of his buildings, complete with turret shapes, looking a bit like a gingerbread house.
The other house belonged to Gaudi. He lived on the estate from 1906 until 1925, when he went to live at his workshop in La Sagrada Familia, so he could concentrate solely on this project. His home changed hands a couple of times, before it was purchased in 1960 by a campaign group who wanted to turn it into a museum. Most of the house is accessible to the public, in the grounds of Park Guell.
La Sagrada Familia
The construction of this gothic style church began in 1882, as a Roman Catholic Church, but not by Gaudi. He took over as architect in 1883. 135 years later, its still under construction, with an expected finish date of 2026, a span of 144 years. It has since been upgraded to a cathedral and then a basilica by the pope in 2010. All work on the building has been funded by donations from both private parties and by donations from the public, together with the cost of entrance fees.
The outside of the church is just as impressive as the inside. Gaudi wanted a total of 18 tower of varying heights from 100 metres to 172.5 metres. He had to carefully plan this part, due to the potential problem of them toppling over, using models, weights and mathematical calculations, to ensure his vision would be practical.
There are three different faces on the building, depicting the Nativity, the Crucifixion, and the Glory of Jesus, all meticulously carved out of stone. Figures adorn what seem like every available space and certainly give those waiting in the never-ending queues something to see. The scary looking soldiers above the entrance are very similar to those on the roof at Casa Mila.
This was Gaudi’s last project as he was killed in a tram accident in June 1926. His resting place was the crypt in La Sagrada Familia. Gaudi was a popular architect during his lifetime in his home country, but his popularity waned following the Spanish Civil war (1936 – 1939) and his workshop was plundered, destroying some important artefacts. His work enjoyed a revival in the 1950s helped by Spanish surrealist artist Salvador Dali and in recent years, UNESCO gave some of his designs the recognition of ‘world heritage status’
Modern design influence on textiles
Given his colourful unique designs, it’s not surprising that modern designs share some of his influences.
Herve Leger and Max Azria
Herve Leger designers, husband and wife team Max and Lubov Azria, prepared an autumn collection in 2015 for New York Fashion Week. This was inspired by Gaudi, following a trip to Barcelona. Photos were taken at La Sagrada Familia and were depicted on the fitted dresses, which were printed with bright symmetrical patterns. Some of the plainer dresses were adorned with sequins, beads, straps or studs and most seem to be above the knee in length.
Ezra Santos
Middle Eastern designer Ezra Santos produced a collection for ‘Fashion Forward’ Dubai, in April 2013 using Gaudi as inspiration. The flamboyant designs are based on La Sagrada Familia. Unusually, the colour of the dresses are plain, but they include complicated details to reflect the style of the building and have a constructed element to them.
Fabryan
Fabryan is a UK womens clothing brand, based in London. Designed by Samantha-Jane, who bought out an eye-catching and brightly coloured scarf collection which was inspired by Gaudi buildings and Barcelona. They clearly depict the brightly coloured stained glass windows of La Sagrada Familia.
Debbie Sun
Caribbean textile artist Debbie Sun lived in Barcelona for several years. Taking her inspiration from nature and organic forms, much like Gaudi, and architecture. She uses elements of Gaudi’s colourful mosaics and the stained-glass windows in her fabric design.
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In this video we're going to be implementing a Generic Object Pool. Object Pooling is a design pattern focused on removing expensive operations such as instantiation or deletion of objects and instead relying upon a constant "pool" of objects which can be activated or deactivated as needed without needing to actually create new ones. The advantage of this approach is that it removes significant CPU spikes when large number of instantiations might occur such as in a bullet hell, when emitting particles or, in our case, when a large number of asteroids come into range. The downside of this implementation is the introduction of some memory overhead to store a large number of objects that may not necessarily ever be needed. However, the cost of there is relatively minor in modern hardware compared to the significant gains we get by avoiding slow garbage collections caused by instantiating and then deleting objects. To implement our pool we are going to use a circular array of prefabs. When we start our game we will instantiate a preset number of prefabs and immediately deactivate them. Then, when we need to instantiate a prefab we will cycle through our array starting from the index immediately after the last object we created until we iterate over the entire list. We'll look for any deactivated instance that we can use. Once we find one, we activate it and position and rotate it in space just like the normal GameObject.Instantiate would function. This removes the object from the available items in the queue (it's active now). When we need to destroy an instance we simply deactivate the instance. This causes it to appear as available again in our queue. The full implementation isn't very difficult, but the advantages it gives are actually fairly significant and we'll be able to take advantage of them more and more as we add more instances. Want to use this pool in your own projects or just want to browse the source code? Check out the GIST on GitHub: http://ift.tt/2lYS8iM Some additional resources if you want to learn more: Official Unity example of an Object Pool implemented in Unity 4.3 in order to create a bullet hell game: http://ift.tt/2l4dZmQ A text based tutorial and example of using an object pool to create a custom particle system implementation via Cat Like Coding: http://ift.tt/1M8oigh
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Generic programming in java
Generic programming in java example
Generic programming is those type of programming concept by which we can create type independent code for all the type which maintains same algorithms. In JAVA programming language large number of generic classes is present to implement any type of concept. Generally the generic classes are vector, stack, queue, array list, circular queue, B tree etc. In JAVA the concept of generic functions is not present, only the concept of generic classes is present. Using this class user can handle any type of element as the requirement of the user. For example :- array is a generic class to handle any types of values using the concept of array. The array class contains a large number of methods to handle array elements. The fill function is used to store values into the array. The sort function is used to arrange data elements in ascending or descending order. The delete function is used to erase data elements from the array. Similarly the search function is used to test existence of a particular element.
This blog written by Jitendra Kumar (Java Trainer at Vtech Academy of Computers)- Java Training Institute in Delhi
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This video tutorial is on how to create a circular queue using array. This tutorial on data structures is for beginners and will make you understand how to implement a circular queue, how to create a circular queue in c using array and circular queue program in c using array. You will understand the complete program and will learn about, circular queue tutorialspoint, circular queue in data structure pdf along with circular queue example. By the end of video you will be knowing, circular queue in C using Array, circular queue in data structures and C program to implement circular queue operations. The tutorial explains with figures at each step and will make you understand the program to implement circular queue using arrays in C, circular queue implementation in C using array and circular queue implementation using array in C along with circular queue algorithm and insertion and deletion in circular queue in data structure too. You can download the program from the following link: http://bmharwani.com/circularqueuearr.c To see the video on linear queue, visit: https://www.youtube.com/watch?v=_OD_BHiDTWk&t=10s To understand pass by value and pass by reference, visit: https://www.youtube.com/watch?v=NIV7M4MSLs4&t=18s To see the video on circular linked list, visit: https://www.youtube.com/watch?v=lg-n_NHAeZk&t=1s For more videos on Data Structures, visit: https://www.youtube.com/watch?v=lg-n_NHAeZk&list=PLuDr_vb2LpAxVWIk-po5nL5Ct2pHpndLR To get notification for latest videos uploaded, subscribe to my channel: https://youtube.com/c/bintuharwani To see more videos on different computer subjects, visit: http://bmharwani.com
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