دانلود کتاب Design and Build Great Web APIs

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Cover
Table of Contents
Preface
Who Is This Book For?
What’s New in the Second Edition
What’s in This Book?
How to Read This Book
Code Examples
Online Resources
Acknowledgments
Connecting
1. Why Data Structures Matter
Data Structures
The Array: The Foundational Data Structure
Measuring Speed
Reading
Searching
Insertion
Deletion
Sets: How a Single Rule Can Affect Efficiency
Wrapping Up
Exercises
2. Why Algorithms Matter
Ordered Arrays
Searching an Ordered Array
Binary Search
Binary Search vs. Linear Search
Wrapping Up
Exercises
3. O Yes! Big O Notation
Big O: How Many Steps Relative to N Elements?
The Soul of Big O
An Algorithm of the Third Kind
Logarithms
O(log N) Explained
Practical Examples
Wrapping Up
Exercises
4. Speeding Up Your Code with Big O
Bubble Sort
Bubble Sort in Action
The Efficiency of Bubble Sort
A Quadratic Problem
A Linear Solution
Wrapping Up
Exercises
5. Optimizing Code with and Without Big O
Selection Sort
Selection Sort in Action
The Efficiency of Selection Sort
Ignoring Constants
Big O Categories
Wrapping Up
Exercises
6. Optimizing for Optimistic Scenarios
Insertion Sort
Insertion Sort in Action
The Efficiency of Insertion Sort
The Average Case
A Practical Example
Wrapping Up
Exercises
7. Big O in Everyday Code
Mean Average of Even Numbers
Word Builder
Array Sample
Average Celsius Reading
Clothing Labels
Count the Ones
Palindrome Checker
Get All the Products
Password Cracker
Wrapping Up
Exercises
8. Blazing Fast Lookup with Hash Tables
Hash Tables
Hashing with Hash Functions
Building a Thesaurus for Fun and Profit, but Mainly Profit
Hash Table Lookups
Dealing with Collisions
Making an Efficient Hash Table
Hash Tables for Organization
Hash Tables for Speed
Wrapping Up
Exercises
9. Crafting Elegant Code with Stacks and Queues
Stacks
Abstract Data Types
Stacks in Action
The Importance of Constrained Data Structures
Queues
Queues in Action
Wrapping Up
Exercises
10. Recursively Recurse with Recursion
Recurse Instead of Loop
The Base Case
Reading Recursive Code
Recursion in the Eyes of the Computer
Filesystem Traversal
Wrapping Up
Exercises
11. Learning to Write in Recursive
Recursive Category: Repeatedly Execute
Recursive Category: Calculations
Top-Down Recursion: A New Way of Thinking
The Staircase Problem
Anagram Generation
Wrapping Up
Exercises
12. Dynamic Programming
Unnecessary Recursive Calls
The Little Fix for Big O
The Efficiency of Recursion
Overlapping Subproblems
Dynamic Programming through Memoization
Dynamic Programming through Going Bottom-Up
Wrapping Up
Exercises
13. Recursive Algorithms for Speed
Partitioning
Quicksort
The Efficiency of Quicksort
Quicksort in the Worst-Case Scenario
Quickselect
Sorting as a Key to Other Algorithms
Wrapping Up
Exercises
14. Node-Based Data Structures
Linked Lists
Implementing a Linked List
Reading
Searching
Insertion
Deletion
Efficiency of Linked List Operations
Linked Lists in Action
Doubly Linked Lists
Queues as Doubly Linked Lists
Wrapping Up
Exercises
15. Speeding Up All the Things with Binary Search Trees
Trees
Binary Search Trees
Searching
Insertion
Deletion
Binary Search Trees in Action
Binary Search Tree Traversal
Wrapping Up
Exercises
16. Keeping Your Priorities Straight with Heaps
Priority Queues
Heaps
Heap Properties
Heap Insertion
Looking for the Last Node
Heap Deletion
Heaps vs. Ordered Arrays
The Problem of the Last Node…Again
Arrays as Heaps
Heaps as Priority Queues
Wrapping Up
Exercises
17. It Doesn\'t Hurt to Trie
Tries
Storing Words
Trie Search
The Efficiency of Trie Search
Trie Insertion
Building Autocomplete
Completing Autocomplete
Tries with Values: A Better Autocomplete
Wrapping Up
Exercises
18. Connecting Everything with Graphs
Graphs
Directed Graphs
Object-Oriented Graph Implementation
Graph Search
Depth-First Search
Breadth-First Search
The Efficiency of Graph Search
Weighted Graphs
Dijkstra’s Algorithm
Wrapping Up
Exercises
19. Dealing with Space Constraints
Big O of Space Complexity
Trade-Offs Between Time and Space
The Hidden Cost of Recursion
Wrapping Up
Exercises
20. Techniques for Code Optimization
Prerequisite: Determine Your Current Big O
Start Here: The Best-Imaginable Big O
Magical Lookups
Recognizing Patterns
Greedy Algorithms
Change the Data Structure
Wrapping Up
Parting Thoughts
Exercises
A1. Exercise Solutions
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Chapter 18
Chapter 19
Chapter 20
Index
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