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Google Interview University

What is it?

This is my multi-month study plan for going from web developer (self-taught, no CS degree) to Google software engineer.

This long list has been extracted and expanded from Google's coaching notes, so these are the things you need to know. There are extra items I added at the bottom that may come up in the interview or be helpful in solving a problem. Many items are from Steve Yegge's "Get that job at Google" and are reflected sometimes word-for-word in Google's coaching notes.

What is it?
Why use it?
How to use it
Get in a Googley Mood
Did I Get the Job?
Follow Along with Me
Don't feel you aren't smart enough
About Google
About Video Resources
Interview Process & General Interview Prep
Pick One Language for the Interview
Before you Get Started
What you Won't See Covered
Prerequisite Knowledge
The Daily Plan
Algorithmic complexity / Big-O / Asymptotic analysis
Data Structures
- Arrays
- Linked Lists
- Stack
- Queue
- Hash table
More Knowledge
- Binary search
- Bitwise operations
Trees
- Trees - Notes & Background
- Binary search trees: BSTs
- Heap / Priority Queue / Binary Heap
- Tries
- Balanced search trees
- N-ary (K-ary, M-ary) trees
Sorting
Graphs
Even More Knowledge
- Recursion
- 动态规划
- Combinatorics (n choose k) & Probability
- NP, NP-Complete and Approximation Algorithms
- Caches
- Processes and Threads
- System Design, Scalability, Data Handling
- Papers
- Testing
- Scheduling
- Implement system routines
- String searching & manipulations
Final Review
Books
Coding exercises/challenges
Once you're closer to the interview
Your Resume
Be thinking of for when the interview comes
Have questions for the interviewer
Once You've Got The Job

---------------- Everything below this point is optional ----------------

Additional Learning
Additional Detail on Some Subjects
Video Series
Computer Science Courses

Why use it?

I'm following this plan to prepare for my Google interview. I've been building the web, building services, and launching startups since 1997. I have an economics degree, not a CS degree. I've been very successful in my career, but I want to work at Google. I want to progress into larger systems and get a real understanding of computer systems, algorithmic efficiency, data structure performance, low-level languages, and how it all works. And if you don't know any of it, Google won't hire you.

When I started this project, I didn't know a stack from a heap, didn't know Big-O anything, anything about trees, or how to traverse a graph. If I had to code a sorting algorithm, I can tell ya it wouldn't have been very good. Every data structure I've ever used was built into the language, and I didn't know how they worked under the hood at all. I've never had to manage memory, unless a process I was running would give an "out of memory" error, and then I'd have to find a workaround. I've used a few multi-dimensional arrays in my life and thousands of associative arrays, but I've never created data structures from scratch.

But after going through this study plan I have high confidence I'll be hired. It's a long plan. It's going to take me months. If you are familiar with a lot of this already it will take you a lot less time.

How to use it

Everything below is an outline, and you should tackle the items in order from top to bottom.

I'm using Github's special markdown flavor, including tasks lists to check progress.

Create a new branch so you can check items like this, just put an x in the brackets: [x]

More about Github-flavored markdown

Get in a Googley Mood

Print out a "future Googler" sign (or two) and keep your eyes on the prize.

Did I Get the Job?

I haven't applied yet.

I still have a few days in the learning phase (finishing up this crazy list), and starting next week all I'll be doing is programming questions all day long. That will continue for a few weeks, and then I'll apply through a referral I've been holding onto since February (yes, February).

Thanks for the referral, JP.

Follow Along with Me

I'm on the journey, too. Follow along on my blog at GoogleyAsHeck.com

Twitter: @googleyasheck
Twitter: @StartupNextDoor
Google+: +Googleyasheck
LinkedIn: johnawasham

Don't feel you aren't smart enough

Google engineers are smart, but many have an insecurity that they aren't smart enough, even though they work at Google.
The myth of the Genius Programmer

About Google

About Video Resources

Some videos are available only by enrolling in a Coursera, EdX, or Lynda.com class. These are called MOOCs. It is free to do so, but sometimes the classes are not in session so you have to wait a couple of months, so you have no access.

I'd appreciate your help converting the MOOC video links to public sources to replace the online course videos over time. I like using university lectures.

Interview Process & General Interview Prep

Videos:
Articles:
- Becoming a Googler in Three Steps
- Get That Job at Google
  - all the things he mentions that you need to know are listed below
- (very dated) How To Get A Job At Google, Interview Questions, Hiring Process
- Phone Screen Questions
Additional (not suggested by Google but I added):
- ABC: Always Be Coding
- Four Steps To Google Without A Degree
- Whiteboarding
- How Google Thinks About Hiring, Management And Culture
- Effective Whiteboarding during Programming Interviews
- Cracking The Coding Interview Set 1:
  - Gayle L McDowell - Cracking The Coding Interview (video)
  - Cracking the Coding Interview with Author Gayle Laakmann McDowell (video)
- How to Get a Job at the Big 4:
  - 'How to Get a Job at the Big 4 - Amazon, Facebook, Google & Microsoft' (video)
- Failing at Google Interviews

Pick One Language for the Interview

I wrote this short article about it: Important: Pick One Language for the Google Interview

You can use a language you are comfortable in to do the coding part of the interview, but for Google, these are solid choices:

C++
Java
Python

You could also use these, but read around first. There may be caveats:

JavaScript
Ruby

You need to be very comfortable in the language, and be knowledgeable.

Before you Get Started

This list grew over many months, and yes, it kind of got out of hand.

Here are some mistakes I made so you'll have a better experience.

1. You Won't Remember it All

I watched hours of videos and took copious notes, and months later there was much I didn't remember. I spent 3 days going through my notes and making flashcards so I could review (see below).

2. Use Flashcards

To solve the problem, I made a little flashcards site where I could add flashcards of 2 types: general and code. Each card has different formatting.

I made a mobile-first website so I could review on my phone and tablet, whereever I am.

Make your own for free:

Flashcards site repo
My flash cards database: Keep in mind I went overboard and have cards covering everything from assembly language and Python trivia to machine learning and statistics. It's way too much for what's required by Google.

Note on flashcards: The first time you recognize you know the answer, don't mark it as known. You have to see the same card and answer it several times correctly before you really know it. Repetition will put that knowledge deeper in your brain.

3. Review, review, review

I keep a set of cheatsheets on ASCII, OSI stack, Big-O notations, and more. I study them when I have some spare time.

Take a break from programming problems for a half hour and go through your flashcards.

4. Focus

There are a lot of distractions that can take up valuable time. Focus and concentration is hard.

What you won't see covered

This big list all started as a personal to-do list made from Google interview coaching notes. These are prevalent technologies but were not mentioned in those notes:

SQL
Javascript
HTML, CSS, and other front-end technologies

The Daily Plan

Some subjects take one day, and some will take multiple days. Some are just learning with nothing to implement.

Each day I take one subject from the list below, watch videos about that subject, and write an implementation in: C - using structs and functions that take a struct * and something else as args. C++ - without using built-in types C++ - using built-in types, like STL's std::list for a linked list Python - using built-in types (to keep practicing Python) and write tests to ensure I'm doing it right, sometimes just using simple assert() statements You may do Java or something else, this is just my thing.

Why code in all of these? Practice, practice, practice, until I'm sick of it, and can do it with no problem (some have many edge cases and bookkeeping details to remember) Work within the raw constraints (allocating/freeing memory without help of garbage collection (except Python)) Make use of built-in types so I have experience using the built-in tools for real-world use (not going to write my own linked list implementation in production)

I may not have time to do all of these for every subject, but I'll try.

You can see my code here:

[C] (https://github.com/jwasham/practice-c)
[C++] (https://github.com/jwasham/practice-cpp)
[Python] (https://github.com/jwasham/practice-python)

You don't need to memorize the guts of every algorithm.

Write code on a whiteboard, not a computer. Test with some sample inputs. Then test it out on a computer.

Prerequisite Knowledge

How computers process a program:
- How does CPU execute program (video)
- Machine Code Instructions (video)
Compilers
How floating point numbers are stored:
- simple 8-bit: Representation of Floating Point Numbers - 1 (video - there is an error in calculations - see video description)
- 32 bit: IEEE754 32-bit floating point binary (video)

Algorithmic complexity / Big-O / Asymptotic analysis

nothing to implement
Harvard CS50 - Asymptotic Notation (video)
Big O Notations (general quick tutorial) (video)
Big O Notation (and Omega and Theta) - best mathematical explanation (video)
Skiena:
- video
- slides
A Gentle Introduction to Algorithm Complexity Analysis
Orders of Growth (video)
Asymptotics (video)
UC Berkeley Big O (video)
UC Berkeley Big Omega (video)
Amortized Analysis (video)
Illustrating "Big O" (video)
TopCoder (includes recurrence relations and master theorem):
- Computational Complexity: Section 1
- Computational Complexity: Section 2
Cheat sheet

If some of the lectures are too mathy, you can jump down to the bottom and watch the discrete mathematics videos to get the background knowledge.

Data Structures

Arrays
- Implement an automatically resizing vector.
- Description:
- Implement a vector (mutable array with automatic resizing):
  - Practice coding using arrays and pointers, and pointer math to jump to an index instead of using indexing.
  - new raw data array with allocated memory
    - can allocate int array under the hood, just not use its features
    - start with 16, or if starting number is greater, use power of 2 - 16, 32, 64, 128
  - size() - number of items
  - capacity() - number of items it can hold
  - is_empty()
  - at(index) - returns item at given index, blows up if index out of bounds
  - push(item)
  - insert(index, item) - inserts item at index, shifts that index's value and trailing elements to the right
  - prepend(item) - can use insert above at index 0
  - pop() - remove from end, return value
  - delete(index) - delete item at index, shifting all trailing elements left
  - remove(item) - looks for value and removes index holding it (even if in multiple places)
  - find(item) - looks for value and returns first index with that value, -1 if not found
  - resize(new_capacity) // private function
    - when you reach capacity, resize to double the size
    - when popping an item, if size is 1/4 of capacity, resize to half
- Time
  - O(1) to add/remove at end (amortized for allocations for more space), index, or update
  - O(n) to insert/remove elsewhere
- Space
  - contiguous in memory, so proximity helps performance
  - space needed = (array capacity, which is >= n) * size of item, but even if 2n, still O(n)
Linked Lists
- Description:
  - Singly Linked Lists (video)
  - CS 61B - Linked Lists (video)
- C Code (video) - not the whole video, just portions about Node struct and memory allocation.
- Linked List vs Arrays:
  - Core Linked Lists Vs Arrays (video)
  - In The Real World Linked Lists Vs Arrays (video)
- why you should avoid linked lists (video)
- Gotcha: you need pointer to pointer knowledge: (for when you pass a pointer to a function that may change the address where that pointer points) This page is just to get a grasp on ptr to ptr. I don't recommend this list traversal style. Readability and maintainability suffer due to cleverness.
  - Pointers to Pointers
- implement (I did with tail pointer & without):
  - size() - returns number of data elements in list
  - empty() - bool returns true if empty
  - value_at(index) - returns the value of the nth item (starting at 0 for first)
  - push_front(value) - adds an item to the front of the list
  - pop_front() - remove front item and return its value
  - push_back(value) - adds an item at the end
  - pop_back() - removes end item and returns its value
  - front() - get value of front item
  - back() - get value of end item
  - insert(index, value) - insert value at index, so current item at that index is pointed to by new item at index
  - erase(index) - removes node at given index
  - value_n_from_end(n) - returns the value of the node at nth position from the end of the list
  - reverse() - reverses the list
  - remove_value(value) - removes the first item in the list with this value
- Doubly-linked List
  - Description (video)
  - No need to implement
Stack
- Stacks (video)
- Using Stacks Last-In First-Out (video)
- Will not implement. Implementing with array is trivial.
Queue
- Using Queues First-In First-Out(video)
- Queue (video)
- Circular buffer/FIFO
- Priority Queues (video)
- Implement using linked-list, with tail pointer:
  - enqueue(value) - adds value at position at tail
  - dequeue() - returns value and removes least recently added element (front)
  - empty()
- Implement using fixed-sized array:
  - enqueue(value) - adds item at end of available storage
  - dequeue() - returns value and removes least recently added element
  - empty()
  - full()
- Cost:
  - a bad implementation using linked list where you enqueue at head and dequeue at tail would be O(n) because you'd need the next to last element, causing a full traversal each dequeue
  - enqueue: O(1) (amortized, linked list and array [probing])
  - dequeue: O(1) (linked list and array)
  - empty: O(1) (linked list and array)
Hash table
- Videos:
- Online Courses:
- implement with array using linear probing
  - hash(k, m) - m is size of hash table
  - add(key, value) - if key already exists, update value
  - exists(key)
  - get(key)
  - remove(key)

More Knowledge

Binary search
- Binary Search (video)
- Binary Search (video)
- detail
- Implement:
  - binary search (on sorted array of integers)
  - binary search using recursion
Bitwise operations
- Bits cheat sheet - you should know many of the powers of 2 from (2^1 to 2^16 and 2^32)
- Get a really good understanding of manipulating bits with: &, |, ^, ~, >>, <<
- 2s and 1s complement
- count set bits
- round to next power of 2:
  - Round Up To Next Power Of Two
- swap values:
  - Swap
- absolute value:
  - Absolute Integer

Trees

Trees - Notes & Background
- Series: Core Trees (video)
- Series: Trees (video)
- basic tree construction
- traversal
- manipulation algorithms
- BFS (breadth-first search)
  - MIT (video)
  - level order (BFS, using queue) time complexity: O(n) space complexity: best: O(1), worst: O(n/2)=O(n)
- DFS (depth-first search)
  - MIT (video)
  - notes: time complexity: O(n) space complexity: best: O(log n) - avg. height of tree worst: O(n)
  - inorder (DFS: left, self, right)
  - postorder (DFS: left, right, self)
  - preorder (DFS: self, left, right)
Binary search trees: BSTs
- Binary Search Tree Review (video)
- Series (video)
  - starts with symbol table and goes through BST applications
- Introduction (video)
- MIT (video)
- C/C++:
- Implement:
  - insert // insert value into tree
  - get_node_count // get count of values stored
  - print_values // prints the values in the tree, from min to max
  - delete_tree
  - is_in_tree // returns true if given value exists in the tree
  - get_height // returns the height in nodes (single node's height is 1)
  - get_min // returns the minimum value stored in the tree
  - get_max // returns the maximum value stored in the tree
  - is_binary_search_tree
  - delete_value
  - get_successor // returns next-highest value in tree after given value, -1 if none
Heap / Priority Queue / Binary Heap
- visualized as a tree, but is usually linear in storage (array, linked list)
- Heap
- Introduction (video)
- Naive Implementations (video)
- Binary Trees (video)
- Tree Height Remark (video)
- Basic Operations (video)
- Complete Binary Trees (video)
- Pseudocode (video)
- Heap Sort - jumps to start (video)
- Heap Sort (video)
- Building a heap (video)
- MIT: Heaps and Heap Sort (video)
- CS 61B Lecture 24: Priority Queues (video)
- Linear Time BuildHeap (max-heap)
- Implement a max-heap:
  - insert
  - sift_up - needed for insert
  - get_max - returns the max item, without removing it
  - get_size() - return number of elements stored
  - is_empty() - returns true if heap contains no elements
  - extract_max - returns the max item, removing it
  - sift_down - needed for extract_max
  - remove(i) - removes item at index x
  - heapify - create a heap from an array of elements, needed for heap_sort
  - heap_sort() - take an unsorted array and turn it into a sorted array in-place using a max heap
    - note: using a min heap instead would save operations, but double the space needed (cannot do in-place).
Tries
- Note there are different kinds of tries. Some have prefixes, some don't, and some use string instead of bits to track the path.
- I read through code, but will not implement.
- Notes on Data Structures and Programming Techniques
- Short course videos:
- The Trie: A Neglected Data Structure
- TopCoder - Using Tries
- Stanford Lecture (real world use case) (video)
- MIT, Advanced Data Structures, Strings (can get pretty obscure about halfway through)
Balanced search trees
- Know least one type of balanced binary tree (and know how it's implemented):
- "Among balanced search trees, AVL and 2/3 trees are now passé, and red-black trees seem to be more popular. A particularly interesting self-organizing data structure is the splay tree, which uses rotations to move any accessed key to the root." - Skiena
- Of these, I chose to implement a splay tree. From what I've read, you won't implement a balanced search tree in your interview. But I wanted exposure to coding one up and let's face it, splay trees are the bee's knees. I did read a lot of red-black tree code.
  - splay tree: insert, search, delete functions If you end up implementing red/black tree try just these:
  - search and insertion functions, skipping delete
- I want to learn more about B-Tree since it's used so widely with very large data sets.
- Self-balancing binary search tree
- AVL trees
  - In practice: From what I can tell, these aren't used much in practice, but I could see where they would be: The AVL tree is another structure supporting O(log n) search, insertion, and removal. It is more rigidly balanced than red–black trees, leading to slower insertion and removal but faster retrieval. This makes it attractive for data structures that may be built once and loaded without reconstruction, such as language dictionaries (or program dictionaries, such as the opcodes of an assembler or interpreter).
  - MIT AVL Trees / AVL Sort (video)
  - AVL Trees (video)
  - AVL Tree Implementation (video)
  - Split And Merge
- Splay trees
  - In practice: Splay trees are typically used in the implementation of caches, memory allocators, routers, garbage collectors, data compression, ropes (replacement of string used for long text strings), in Windows NT (in the virtual memory, networking, and file system code) etc.
  - CS 61B: Splay Trees (video)
  - MIT Lecture: Splay Trees:
    - Gets very mathy, but watch the last 10 minutes for sure.
    - Video
- 2-3 search trees
  - In practice: 2-3 trees have faster inserts at the expense of slower searches (since height is more compared to AVL trees).
  - You would use 2-3 tree very rarely because its implementation involves different types of nodes. Instead, people use Red Black trees.
  - 23-Tree Intuition and Definition (video)
  - Binary View of 23-Tree
  - 2-3 Trees (student recitation) (video)
- 2-3-4 Trees (aka 2-4 trees)
  - In practice: For every 2-4 tree, there are corresponding red–black trees with data elements in the same order. The insertion and deletion operations on 2-4 trees are also equivalent to color-flipping and rotations in red–black trees. This makes 2-4 trees an important tool for understanding the logic behind red–black trees, and this is why many introductory algorithm texts introduce 2-4 trees just before red–black trees, even though 2-4 trees are not often used in practice.
  - CS 61B Lecture 26: Balanced Search Trees (video)
  - Bottom Up 234-Trees (video)
  - Top Down 234-Trees (video)
- B-Trees
  - fun fact: it's a mystery, but the B could stand for Boeing, Balanced, or Bayer (co-inventor)
  - In Practice: B-Trees are widely used in databases. Most modern filesystems use B-trees (or Variants). In addition to its use in databases, the B-tree is also used in filesystems to allow quick random access to an arbitrary block in a particular file. The basic problem is turning the file block i address into a disk block (or perhaps to a cylinder-head-sector) address.
  - B-Tree
  - Introduction to B-Trees (video)
  - B-Tree Definition and Insertion (video)
  - B-Tree Deletion (video)
  - MIT 6.851 - Memory Hierarchy Models (video) - covers cache-oblivious B-Trees, very interesting data structures - the first 37 minutes are very technical, may be skipped (B is block size, cache line size)
- Red/black trees
  - In practice: Red–black trees offer worst-case guarantees for insertion time, deletion time, and search time. Not only does this make them valuable in time-sensitive applications such as real-time applications, but it makes them valuable building blocks in other data structures which provide worst-case guarantees; for example, many data structures used in computational geometry can be based on red–black trees, and the Completely Fair Scheduler used in current Linux kernels uses red–black trees. In the version 8 of Java, the Collection HashMap has been modified such that instead of using a LinkedList to store identical elements with poor hashcodes, a Red-Black tree is used.
  - Aduni - Algorithms - Lecture 4 (link jumps to starting point) (video)
  - Aduni - Algorithms - Lecture 5 (video)
  - Black Tree
  - An Introduction To Binary Search And Red Black Tree
N-ary (K-ary, M-ary) trees
- note: the N or K is the branching factor (max branches)
  - binary trees are a 2-ary tree, with branching factor = 2
  - 2-3 trees are 3-ary
- K-Ary Tree

Sorting

Notes:
- 实现各种排序 & 知道每种排序的最坏、最好和平均的复杂度分别是什么场景:
  - 不要用冒泡排序 - 大多数情况下效率感人 - 时间复杂度 O(n^2), 除非 n <= 16
- 排序算法的稳定性 ("快排是稳定的么?")
- 哪种排序算法可以用链表？哪种用数组？哪种两者都可？
  - 并不推荐对一个链表排序，但归并排序是可行的.
  - 链表的归并排序
关于堆排序，请查看前文堆的数据结构部分。堆排序很强大，不过是非稳定排序。
冒泡排序 (video)
冒泡排序分析 (video)
插入排序 & 归并排序 (video)
插入排序 (video)
归并排序 (video)
快排 (video)
选择排序 (video)
斯坦福大学关于排序算法的视频:
- Lecture 15 | Programming Abstractions (video)
- Lecture 16 | Programming Abstractions (video)
Shai Simonson 视频, Aduni.org:
- 算法 - 排序 - 第二讲 (video)
- 算法 - 排序2 - 第三讲 (video)
Steven Skiena 关于排序的视频:
加州大学伯克利分校（UC Berkeley）大学课程:
- 归并排序:
- 使用外部数组
- 对原数组直接排序
- 快速排序:
- 实现
- 实现
实现:
- 归并：平均和最差情况的时间复杂度为 O(n log n)。
- 快排：平均时间复杂度为 O(n log n)。
- 选择排序和插入排序的最坏、平均时间复杂度都是 O(n^2)。
- 关于堆排序，请查看前文堆的数据结构部分。
有兴趣的话，还有一些补充 - 但并不是必须的:

图

图论能解决计算机科学里的很多问题，所以这一节会比较长，像树和排序的部分一样。

Yegge 的笔记:
- 有 3 种基本方式在内存里表示一个图:
  - 对象和指针
  - 矩阵
  - 邻接表
- 熟悉以上每一种图的表示法，并了解各自的优缺点
- 宽度优先搜索和深度优先搜索 - 知道它们的计算复杂度和设计上的权衡以及如何用代码实现它们
- 遇到一个问题时，首先尝试基于图的解决方案，如果没有再去尝试其他的。
Skiena 教授的课程 - 很不错的介绍:
图 (复习和其他):
完整的 Coursera 课程:
- 图的算法 (video)
Yegge: 如果有机会，可以试试研究更酷炫的算法:
- Dijkstra 算法 - 上文 - 6.006
- A* 算法
我会实现:
- DFS 邻接表 (递归)
- DFS 邻接表 (栈迭代)
- DFS 邻接矩阵 (递归)
- DFS 邻接矩阵 (栈迭代)
- BFS 邻接表
- BFS 邻接矩阵
- 单源最短路径问题 (Dijkstra)
- 最小生成树
- 基于 DFS 的算法 (根据上文 Aduni 的视频):
  - 检查环 (我们会先检查是否有环存在以便做拓扑排序)
  - 拓扑排序
  - 计算图中的连通分支
  - 列出强连通分量
  - 检查双向图

可以从 Skiena 的书（参考下面的书推荐小节）和面试书籍中学习更多关于图的实践。

终面

这一部分有一些短视频，你可以快速的观看和复习大多数重要概念。
这对经常性的巩固很有帮助。

综述:

2-3 分钟的短视频系列 (23 个)
- Videos
2-5 分钟的短视频系列 - Michael Sambol (18 个):
- Videos

排序:

归并排序: https://www.youtube.com/watch?v=GCae1WNvnZM

书籍

Google Coaching 里提到的

阅读并做练习:

算法设计手册 (Skiena)
- 书 (Kindle 上可以租到):
  - Algorithm Design Manual
- Half.com 是一个资源丰富且性价比很高的在线书店.
- 答案:
  - 解答
  - 解答
- 勘误表
read and do exercises from the books below. Then move to coding challenges (further down below) 一旦你理解了每日计划里的所有内容，就去读上面所列的书并完成练习，然后开始读下面所列的书并做练习，之后就可以开始实战写代码了（本文再往后的部分）

首先阅读:

Programming Interviews Exposed: Secrets to Landing Your Next Job, 2nd Edition

然后阅读 (这本获得了很多推荐，但是不在 Google coaching 的文档里):

Cracking the Coding Interview, 6th Edition
- 如果你看到有人在看 "The Google Resume", 实际上它和 "Cracking the Coding Interview" 是同一个作者写的，而且后者是升级版。

附加书单

这些没有被 Google 推荐阅读，不过我因为需要这些背景知识所以也把它们列在了这里。

C Programming Language, Vol 2
- 练习的答案
C++ Primer Plus, 6th Edition
《Unxi 环境高级编程》 The Unix Programming Environment
《编程珠玑》 Programming Pearls
Algorithms and Programming: Problems and Solutions

如果你有时间

Introduction to Algorithms
Elements of Programming Interviews
- 如果你希望在面试里用 C++ 写代码，这本书的代码全都是 C++ 写的
- 通常情况下能找到解决方案的好书.

编码练习和挑战

一旦你学会了理论基础，就应该把它们拿出来练练。尽量坚持每天做编码练习，越多越好。

编程问题预备:

编码练习平台:

当你临近面试时

搞定代码面试 (videos):

你的简历

10 条小贴士让你写出一份还算不错的简历
这是搞定面试的第一个关键步骤

当面试来临的时候

随着下面列举的问题思考下你可能会遇到的 20 个面试问题
每个问题准备 2-3 种回答
准备点故事，不要只是摆一些你完成的事情的数据，相信我，人人都喜欢听故事

你为什么想得到这份工作？
你解决过的最有难度的问题是什么？
面对过的最大挑战是什么?
见过的最好或者最坏的设计是怎么样的?
对某项 Google 产品提出改进建议。
你作为一个个体同时也是团队的一员，如何达到最好的工作状态?
你的什么技能或者经验是你的角色中不可或缺的?为什么？
你在某份工作或某个项目中最享受的是什么?
你在某份工作或某个项目中面临过的最大挑战是什么?
你在某份工作或某个项目中遇到过的最蛋疼的 Bug 是什么样的？
你在某份工作或某个项目中学到了什么？
你在某份工作或某个项目中哪些地方还可以做的更好？

问面试官的问题

我会问的一些：(可能我已经知道了答案但我想听听面试官的看法或者了解团队的前景):

团队多大规模?
开发周期是怎样的? 会使用瀑布流/极限编程/敏捷开发么?
经常会为 deadline 加班么? 或者是有弹性的?
团队里怎么做技术选型?
每周平均开多少次会?
你觉得工作环境有助于员工集中精力吗?
目前正在做什么工作?
喜欢这些事情吗?
工作期限是怎么样的?

当你获得了梦想的职位

我还能说些什么呢，恭喜你！

我希望在 Google 的第一天就知道的 10 件事

坚持继续学习。

得到这份工作只是一个开始。

*****************************************************************************************************
*****************************************************************************************************

Everything below this point is optional. These are my recommendations, not Google's. 
By studying these, you'll get greater exposure to more CS concepts, and will be better prepared for 
any software engineering job.

*****************************************************************************************************
*****************************************************************************************************

Additional Learning

Unicode
- The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets
- What Every Programmer Absolutely, Positively Needs To Know About Encodings And Character Sets To Work With Text
Endianness
- Big And Little Endian
- Big Endian Vs Little Endian (video)
- Big And Little Endian Inside/Out (video)
  - Very technical talk for kernel devs. Don't worry if most is over your head.
  - The first half is enough.
Emacs and vi(m)
- suggested by Yegge, from an old Amazon recruiting post: Familiarize yourself with a unix-based code editor
- vi(m):
- emacs:
Unix command line tools
- suggested by Yegge, from an old Amazon recruiting post. I filled in the list below from good tools.
- bash
- cat
- grep
- sed
- awk
- curl or wget
- sort
- tr
- uniq
- strace
- tcpdump
Information theory (videos)
- Khan Academy
- more about Markov processes:
- See more in MIT 6.050J Information and Entropy series below.
Parity & Hamming Code (videos)
- Intro
- Parity
- Hamming Code:
  - Error detection
  - Error correction
- Error Checking
Entropy
- also see videos below
- make sure to watch information theory videos first
- Information Theory, Claude Shannon, Entropy, Redundancy, Data Compression & Bits (video)
Cryptography
- also see videos below
- make sure to watch information theory videos first
- Khan Academy Series
- Cryptography: Hash Functions
- Cryptography: Encryption
Compression
- make sure to watch information theory videos first
- Computerphile (videos):
- Compressor Head videos
- (optional) Google Developers Live: GZIP is not enough!
Networking (videos)
- Khan Academy
- UDP and TCP: Comparison of Transport Protocols
- TCP/IP and the OSI Model Explained!
- Packet Transmission across the Internet. Networking & TCP/IP tutorial.
- HTTP
- SSL and HTTPS
- SSL/TLS
- HTTP 2.0
- Video Series (21 videos)
- Subnetting Demystified - Part 5 CIDR Notation
Computer Security
- MIT (23 videos)
Garbage collection
- Garbage collection (Java); Augmenting data str (video)
- Compilers (video)
- GC in Python (video)
- Deep Dive Java: Garbage Collection is Good!
- Deep Dive Python: Garbage Collection in CPython (video)
Parallel Programming
- Coursera (Scala)
- Efficient Python for High Performance Parallel Computing (video)
Design patterns
- Quick UML review (video)
- Learn these patterns:
  - strategy
  - singleton
  - adapter
  - prototype
  - decorator
  - visitor
  - factory, abstract factory
  - facade
  - observer
  - proxy
  - delegate
  - command
  - state
  - memento
  - iterator
  - composite
  - flyweight
- Chapter 6 (Part 1) - Patterns (video)
- Chapter 6 (Part 2) - Abstraction-Occurrence, General Hierarchy, Player-Role, Singleton, Observer, Delegation (video)
- Chapter 6 (Part 3) - Adapter, Facade, Immutable, Read-Only Interface, Proxy (video)
- Series of videos (27 videos)
- Head First Design Patterns
  - I know the canonical book is "Design Patterns: Elements of Reusable Object-Oriented Software", but Head First is great for beginners to OO.
- Handy reference: 101 Design Patterns & Tips for Developers
Messaging, Serialization, and Queueing Systems
- Thrift
  - Tutorial
- Protocol Buffers
  - Tutorials
- gRPC
  - gRPC 101 for Java Developers (video)
- Redis
  - Tutorial
- Amazon SQS (queue)
- Amazon SNS (pub-sub)
- RabbitMQ
  - Get Startet
- Celery
  - First Steps With Celery
- ZeroMQ
  - Intro - Read The Manual
- ActiveMQ
- Kafka
- MessagePack
- Avro
Fast Fourier Transform
- What is a Fourier transform? What is it used for?
- What is the Fourier Transform? (video)
- Divide & Conquer: FFT (video)
- Understanding The FFT
Bloom Filter
- Given a Bloom filter with m bits and k hashing functions, both insertion and membership testing are O(k)
- Bloom Filters
- Bloom Filters | Mining of Massive Datasets | Stanford University
- Tutorial
- How To Write A Bloom Filter App
van Emde Boas Trees
- Divide & Conquer: van Emde Boas Trees (video)
- MIT Lecture Notes
Augmented Data Structures
- CS 61B Lecture 39: Augmenting Data Structures
Skip lists
- "These are somewhat of a cult data structure" - Skiena
- Randomization: Skip Lists (video)
- For animations and a little more detail
Network Flows
- Ford-Fulkerson in 5 minutes (video)
- Ford-Fulkerson Algorithm (video)
- Network Flows (video)
Disjoint Sets & Union Find
- Disjoint Set
- UCB 61B - Disjoint Sets; Sorting & selection (video)
- Coursera (not needed since the above video explains it great):
Math for Fast Processing
- Integer Arithmetic, Karatsuba Multiplication (video)
- The Chinese Remainder Theorem (used in cryptography) (video)
Treap
- Combination of a binary search tree and a heap
- Treap
- Data Structures: Treaps explained (video)
- Applications in set operations
Linear Programming (videos)
- Linear Programming
- Finding minimum cost
- Finding maximum value
Geometry, Convex hull (videos)
- Graph Alg. IV: Intro to geometric algorithms - Lecture 9
- Geometric Algorithms: Graham & Jarvis - Lecture 10
- Divide & Conquer: Convex Hull, Median Finding
Discrete math
- see videos below
Machine Learning
- Why ML?
- Google's Cloud Machine learning tools (video)
- Google Developers' Machine Learning Recipes (Scikit Learn & Tensorflow) (video)
- Tensorflow (video)
- Tensorflow Tutorials
- [Practical Guide to implementing Neural Networks in Python](using Theano)])http://www.analyticsvidhya.com/blog/2016/04/neural-networks-python-theano/)
- Courses:
  - Great starter course: Machine Learning - videos only - see videos 12-18 for a review of linear algebra (14 and 15 are duplicates)
  - Neural Networks for Machine Learning
  - Google's Deep Learning Nanodegree
  - Google/Kaggle Machine Learning Engineer Nanodegree
  - Self-Driving Car Engineer Nanodegree
  - Metis Online Course ($99 for 2 months)
- Resources:
  - Great book: Data Science from Scratch: First Principles with Python: https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X
  - Data School: http://www.dataschool.io/
Go
- Videos:
- Books:
  - An Introduction to Programming in Go (read free online)
  - The Go Programming Language (Donovan & Kernighan)
- Bootcamp

Additional Detail on Some Subjects

I added these to reinforce some ideas already presented above, but didn't want to include them
above because it's just too much. It's easy to overdo it on a subject.
You want to get hired in this century, right?

More 动态规划 (videos)
Advanced Graph Processing (videos)
- Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees
- Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees
MIT Probability (mathy, and go slowly, which is good for mathy things) (videos):
Simonson: Approximation Algorithms (video)

Video Series

Sit back and enjoy. "netflix and skill" :P

119 KiB Raw Blame History Unescape Escape

Google Interview University

What is it?

Table of Contents

Why use it?

How to use it

Get in a Googley Mood

Did I Get the Job?

Follow Along with Me

Don't feel you aren't smart enough

About Google

About Video Resources

Interview Process & General Interview Prep

Pick One Language for the Interview

Before you Get Started

1. You Won't Remember it All

2. Use Flashcards

3. Review, review, review

4. Focus

What you won't see covered

The Daily Plan

Prerequisite Knowledge

Algorithmic complexity / Big-O / Asymptotic analysis

Data Structures

Arrays

Linked Lists

Stack

Queue

Hash table

More Knowledge

Binary search

Bitwise operations

Trees

Trees - Notes & Background

Binary search trees: BSTs

Heap / Priority Queue / Binary Heap

Tries

Balanced search trees

N-ary (K-ary, M-ary) trees

Sorting

图

更多知识

递归

动态规划

组合 (n 中选 k 个) & 概率

NP, NP-完全和近似算法

缓存

进程和线程

系统设计、可伸缩性、数据处理

论文

测试

调度

实现系统例程

字符串搜索和操作

终面

综述:

排序:

书籍

Google Coaching 里提到的

附加书单

如果你有时间

编码练习和挑战

当你临近面试时

你的简历

当面试来临的时候

问面试官的问题

当你获得了梦想的职位

Additional Learning

Unicode

Endianness

Emacs and vi(m)

Unix command line tools

Information theory (videos)

Parity & Hamming Code (videos)

Entropy

Cryptography

Compression

Networking (videos)

Computer Security

Garbage collection

119 KiB

Raw Blame History