Google Interview University

Translations:

中文版本

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
- Dynamic Programming
- 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
Coding Question Practice
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 Books
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

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 or paper, not a computer. Test with some sample inputs. Then test it out on a computer.

Prerequisite Knowledge

Algorithmic complexity / Big-O / Asymptotic analysis

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

Graphs

Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were.

You'll get more graph practice in Skiena's book (see Books section below) and the interview books

Even More Knowledge

Recursion
- Stanford lectures on recursion & backtracking:
- when it is appropriate to use it
- how is tail recursion better than not?
  - What Is Tail Recursion Why Is It So Bad?
  - Tail Recursion (video)
Dynamic Programming
- This subject can be pretty difficult, as each DP soluble problem must be defined as a recursion relation, and coming up with it can be tricky.
- I suggest looking at many examples of DP problems until you have a solid understanding of the pattern involved.
- Videos:
  - the Skiena videos can be hard to follow since he sometimes uses the whiteboard, which is too small to see
  - Skiena: CSE373 2012 - Lecture 19 - Introduction to Dynamic Programming (video)
  - Skiena: CSE373 2012 - Lecture 20 - Edit Distance (video)
  - Skiena: CSE373 2012 - Lecture 21 - Dynamic Programming Examples (video)
  - Skiena: CSE373 2012 - Lecture 22 - Applications of Dynamic Programming (video)
  - Simonson: Dynamic Programming 0 (starts at 59:18) (video)
  - Simonson: Dynamic Programming I - Lecture 11 (video)
  - Simonson: Dynamic programming II - Lecture 12 (video)
  - List of individual DP problems (each is short): Dynamic Programming (video)
- Yale Lecture notes:
  - Dynamic Programming
- Coursera:
Combinatorics (n choose k) & Probability
- Math Skills: How to find Factorial, Permutation and Combination (Choose) (video)
- Make School: Probability (video)
- Make School: More Probability and Markov Chains (video)
- Khan Academy:
  - Course layout:
    - Basic Theoretical Probability
  - Just the videos - 41 (each are simple and each are short):
    - Probability Explained (video)
NP, NP-Complete and Approximation Algorithms
- Know about the most famous classes of NP-complete problems, such as traveling salesman and the knapsack problem, and be able to recognize them when an interviewer asks you them in disguise.
- Know what NP-complete means.
- Computational Complexity (video)
- Simonson:
- Skiena:
- Complexity: P, NP, NP-completeness, Reductions (video)
- Complexity: Approximation Algorithms (video)
- Complexity: Fixed-Parameter Algorithms (video)
- Peter Norvik discusses near-optimal solutions to traveling salesman problem:
  - Jupyter Notebook
- Pages 1048 - 1140 in CLRS if you have it.
Caches
- LRU cache:
- CPU cache:
  - MIT 6.004 L15: The Memory Hierarchy (video)
  - MIT 6.004 L16: Cache Issues (video)
Processes and Threads
- Computer Science 162 - Operating Systems (25 videos):
  - for precesses and threads see videos 1-11
  - Operating Systems and System Programming (video)
- What Is The Difference Between A Process And A Thread?
- Covers:
  - Processes, Threads, Concurrency issues
    - difference between processes and threads
    - processes
    - threads
    - locks
    - mutexes
    - semaphores
    - monitors
    - how they work
    - deadlock
    - livelock
  - CPU activity, interrupts, context switching
  - Modern concurrency constructs with multicore processors
  - Process resource needs (memory: code, static storage, stack, heap, and also file descriptors, i/o)
  - Thread resource needs (shares above (minus stack) with other threads in same process but each has its own pc, stack counter, registers and stack)
  - Forking is really copy on write (read-only) until the new process writes to memory, then it does a full copy.
  - Context switching
    - How context switching is initiated by the operating system and underlying hardware
- threads in C++ (series - 10 videos)
- concurrency in Python (videos):
Scalability and System Design are very large topics with many topics and resources, since there is a lot to consider when designing a software/hardware system that can scale. Expect to spend quite a bit of time on this.
System Design, Scalability, Data Handling
- Considerations from Yegge:
  - scalability
    - Distill large data sets to single values
    - Transform one data set to another
    - Handling obscenely large amounts of data
  - system design
    - features sets
    - interfaces
    - class hierarchies
    - designing a system under certain constraints
    - simplicity and robustness
    - tradeoffs
    - performance analysis and optimization
- START HERE: System Design from HiredInTech
- How Do I Prepare To Answer Design Questions In A Technical Inverview?
- 8 Things You Need to Know Before a System Design Interview
- Algorithm design
- Database Normalization - 1NF, 2NF, 3NF and 4NF (video)
- System Design Interview - There are a lot of resources in this one. Look through the articles and examples. I put some of them below.
- How to ace a systems design interview
- Numbers Everyone Should Know
- How long does it take to make a context switch?
- Transactions Across Datacenters (video)
- A plain english introduction to CAP Theorem
- Paxos Consensus algorithm:
  - short video
  - extended video with use case and multi-paxos
  - paper
- Consistent Hashing
- NoSQL Patterns
- Optional: UML 2.0 Series (vido)
- OOSE: Software Dev Using UML and Java (21 videos):
  - Can skip this if you have a great grasp of OO and OO design practices.
  - OOSE: Software Dev Using UML and Java
- SOLID OOP Principles:
  - Bob Martin SOLID Principles of Object Oriented and Agile Design (video)
  - SOLID Design Patterns in C# (video)
  - SOLID Principles (video)
  - S - Single Responsibility Principle | Single responsibility to each Object
    - more flavor
  - O - Open/Closed Principal | On production level Objects are ready for extension for not for modification
    - more flavor
  - L - Liskov Substitution Principal | Base Class and Derived class follow ‘IS A’ principal
    - more flavor
  - I - Interface segregation principle | clients should not be forced to implement interfaces they don't use
    - Interface Segregation Principle in 5 minutes (video)
    - more flavor
  - D -Dependency Inversion principle | Reduce the dependency In composition of objects.
    - Why Is The Dependency Inversion Principle And Why Is It Important
    - more flavor
- Scalability:
  - Great overview (video)
  - Short series:
    - Clones
    - Database
    - Cache
    - Asynchronism
  - Scalable Web Architecture and Distributed Systems
  - Fallacies of Distributed Computing Explained
  - Pragmatic Programming Techniques
    - extra: Google Pregel Graph Processing
  - Jeff Dean - Building Software Systems At Google and Lessons Learned (video)
  - Introduction to Architecting Systems for Scale
  - Scaling mobile games to a global audience using App Engine and Cloud Datastore (video)
  - How Google Does Planet-Scale Engineering for Planet-Scale Infra (video)
  - The Importance of Algorithms
  - Sharding
  - Scale at Facebook (2009)
  - Scale at Facebook (2012), "Building for a Billion Users" (video)
  - Engineering for the Long Game - Astrid Atkinson Keynote(video)
  - 7 Years Of YouTube Scalability Lessons In 30 Minutes
    - video
  - How PayPal Scaled To Billions Of Transactions Daily Using Just 8VMs
  - How to Remove Duplicates in Large Datasets
  - A look inside Etsy's scale and engineering culture with Jon Cowie (video)
  - What Led Amazon to its Own Microservices Architecture
  - To Compress Or Not To Compress, That Was Uber's Question
  - Asyncio Tarantool Queue, Get In The Queue
  - When Should Approximate Query Processing Be Used?
  - Google's Transition From Single Datacenter, To Failover, To A Native Multihomed Architecture
  - Spanner
  - Egnyte Architecture: Lessons Learned In Building And Scaling A Multi Petabyte Distributed System
  - Machine Learning Driven Programming: A New Programming For A New World
  - The Image Optimization Technology That Serves Millions Of Requests Per Day
  - A Patreon Architecture Short
  - Tinder: How Does One Of The Largest Recommendation Engines Decide Who You'll See Next?
  - Design Of A Modern Cache
  - Live Video Streaming At Facebook Scale
  - A Beginner's Guide To Scaling To 11 Million+ Users On Amazon's AWS
  - How Does The Use Of Docker Effect Latency?
  - Does AMP Counter An Existential Threat To Google?
  - A 360 Degree View Of The Entire Netflix Stack
  - Latency Is Everywhere And It Costs You Sales - How To Crush It
  - Serverless (very long, just need the gist)
  - What Powers Instagram: Hundreds of Instances, Dozens of Technologies
  - Cinchcast Architecture - Producing 1,500 Hours Of Audio Every Day
  - Justin.Tv's Live Video Broadcasting Architecture
  - Playfish's Social Gaming Architecture - 50 Million Monthly Users And Growing
  - TripAdvisor Architecture - 40M Visitors, 200M Dynamic Page Views, 30TB Data
  - PlentyOfFish Architecture
  - Salesforce Architecture - How They Handle 1.3 Billion Transactions A Day
  - ESPN's Architecture At Scale - Operating At 100,000 Duh Nuh Nuhs Per Second
  - See "Messaging, Serialization, and Queueing Systems" way below for info on some of the technologies that can glue services together
  - Twitter:
    - O'Reilly MySQL CE 2011: Jeremy Cole, "Big and Small Data at @Twitter" (video)
    - Timelines at Scale
  - For even more, see "Mining Massive Datasets" video series in the Video Series section.
- Practicing the system design process: Here are some ideas to try working through on paper, each with some documentation on how it was handled in the real world:
  - review: System Design from HiredInTech
  - cheat sheet
  - flow:
    1. Understand the problem and scope:
      - define the use cases, with interviewer's help
      - suggest additional features
      - remove items that interviewer deems out of scope
      - assume high availability is required, add as a use case
    2. Think about constraints:
      - ask how many requests per month
      - ask how many requests per second (they may volunteer it or make you do the math)
      - estimate reads vs. writes percentage
      - keep 80/20 rule in mind when estimating
      - how much data written per second
      - total storage required over 5 years
      - how much data read per second
    3. Abstract design:
      - layers (service, data, caching)
      - infrastructure: load balancing, messaging
      - rough overview of any key algorithm that drives the service
      - consider bottlenecks and determine solutions
  - Exercises:
Papers
- These are Google papers and well-known papers.
- Reading all from end to end with full comprehension will likely take more time than you have. I recommend being selective on papers and their sections.
- 1978: Communicating Sequential Processes
  - implemented in Go
  - Love classic papers?
- 2003: The Google File System
  - replaced by Colossus in 2012
- 2004: MapReduce: Simplified Data Processing on Large Clusters
  - mostly replaced by Cloud Dataflow?
- 2007: What Every Programmer Should Know About Memory (very long, and the author encourages skipping of some sections)
- 2012: Google's Colossus
  - paper not available
- 2012: AddressSanitizer: A Fast Address Sanity Checker:
  - paper
  - video
- 2013: Spanner: Google’s Globally-Distributed Database:
  - paper
  - video
- 2014: Machine Learning: The High-Interest Credit Card of Technical Debt
- 2015: Continuous Pipelines at Google
- 2015: High-Availability at Massive Scale: Building Google’s Data Infrastructure for Ads
- 2015: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- 2015: How Developers Search for Code: A Case Study
- 2016: Borg, Omega, and Kubernetes
Testing
- To cover:
  - how unit testing works
  - what are mock objects
  - what is integration testing
  - what is dependency injection
- Agile Software Testing with James Bach (video)
- Open Lecture by James Bach on Software Testing (video)
- Steve Freeman - Test-Driven Development (that’s not what we meant) (video)
  - slides
- TDD is dead. Long live testing.
- Is TDD dead? (video)
- Video series (152 videos) - not all are needed (video)
- Test-Driven Web Development with Python
- Dependency injection:
  - video
  - Tao Of Testing
- How to write tests
Scheduling
- in an OS, how it works
- can be gleaned from Operating System videos
Implement system routines
- understand what lies beneath the programming APIs you use
- can you implement them?
String searching & manipulations
- Search pattern in text (video)
- Rabin-Karp (videos):
- Knuth-Morris-Pratt (KMP):
  - Pratt Algorithm
  - Tutorial: The Knuth-Morris-Pratt (KMP) String Matching Algorithm
- Boyer–Moore string search algorithm
  - Boyer-Moore String Search Algorithm
  - Advanced String Searching Boyer-Moore-Horspool Algorithms (video)
- Coursera: Algorithms on Strings

Final Review

This section will have shorter videos that can you watch pretty quickly to review most of the important concepts.
It's nice if you want a refresher often.
(More items will be added here)

General:

Series of 2-3 minutes short subject videos (23 videos)
- Videos
Series of 2-5 minutes short subject videos - Michael Sambol (18 videos):
- Videos

Sorts:

Merge Sort: https://www.youtube.com/watch?v=GCae1WNvnZM

Coding Question Practice

Now that you know all the computer science topics above, it's time to practice answering coding problems.

Coding question practice is not about memorizing answers to programming problems.

Why you need to practice doing programming problems:

problem recognition, and where the right data structures and algorithms fit in
gathering requirements for the problem
talking your way through the problem like you will in the interview
coding on a whiteboard or paper, not a computer
coming up with time and space complexity for your solutions
testing your solutions

There is a great intro for methodical, communicative problem solving in an interview. You'll get this from the programming interview books, too, but I found this outstanding:

Algorithm design canvas

No whiteboard at home? That makes sense. I'm a weirdo and have a big whiteboard. Instead of a whiteboard, pick up a large drawing pad from an art store. You can sit on the couch and practice. This is my "sofa whiteboard". I added the pen in the photo for scale.

Supplemental:

Read as a review and problem recognition

Algorithm Design Manual (Skiena)
- To quote Yegge: "More than any other book it helped me understand just how astonishingly commonplace (and important) graph problems are – they should be part of every working programmer's toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. But the gold mine is the second half of the book, which is a sort of encyclopedia of 1-pagers on zillions of useful problems and various ways to solve them, without too much detail. Almost every 1-pager has a simple picture, making it easy to remember. This is a great way to learn how to identify hundreds of problem types."
- Can rent it on kindle
- Half.com is a great resource for textbooks at good prices.
- Answers:
  - Solutions
  - Solutions
- Errata

Read and Do Programming Problems (in this order):

Programming Interviews Exposed: Secrets to Landing Your Next Job, 2nd Edition
- recommended in Google candidate coaching
Cracking the Coding Interview, 6th Edition
- recommended on the Google Careers site
- If you see people reference "The Google Resume", it was a book replaced by "Cracking the Coding Interview".

If you have time

Programming Pearls
Algorithms and Programming: Problems and Solutions
Introduction to Algorithms
- To quote Yegge: "But if you want to come into your interviews prepped, then consider deferring your application until you've made your way through that book."
- Half.com is a great resource for textbooks at good prices.
- aka CLR, sometimes CLRS, because Stein was late to the game
Elements of Programming Interviews
- all code is in C++, if you're looking to use C++ in your interview
- good book on problem solving in general.

Coding exercises/challenges

Once you've learned your brains out, put those brains to work. Take coding challenges every day, as many as you can.

Challenge sites:

Maybe:

Mock interviewers from big companies

Once you're closer to the interview

Cracking The Coding Interview Set 2 (videos):

Your Resume

Ten Tips for a (Slightly) Less Awful Resume
Great stuff at the back of Cracking The Coding Interview

Be thinking of for when the interview comes

Think of about 20 interview questions you'll get, along the lines of the items below. Have 2-3 answers for each. Have a story, not just data, about something you accomplished.

Why do you want this job?
What's a tough problem you've solved?
Biggest challenges faced?
Best/worst designs seen?
Ideas for improving an existing Google product.
How do you work best, as an individual and as part of a team?
Which of your skills or experiences would be assets in the role and why?
What did you most enjoy at [job x / project y]?
What was the biggest challenge you faced at [job x / project y]?
What was the hardest bug you faced at [job x / project y]?
What did you learn at [job x / project y]?
What would you have done better at [job x / project y]?

Have questions for the interviewer

Some of mine (I already may know answer to but want their opinion or team perspective):

How large is your team?
What is your dev cycle look like? Do you do waterfall/sprints/agile?
Are rushes to deadlines common? Or is there flexibility?
How are decisions made in your team?
How many meetings do you have per week?
Do you feel your work environment helps you concentrate?
What are you working on?
What do you like about it?
What is the work life like?

Once You've Got The Job

Congratulations!

10 things I wish I knew on my first day at Google

Keep learning.

You're never really done.

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

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. You'll be a much more well-rounded software engineer.

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

Additional Books

C Programming Language, Vol 2
- answers to questions
The Unix Programming Environment
- and oldie but a goodie
The Linux Command Line: A Complete Introduction
- a modern option
TCP/IP Illustrated Series
Head First Design Patterns
- a gentle introduction to design patterns
Design Patterns: Elements of Reusable Object-Oriented Software
- aka the "Gang Of Four" book, or GOF
- the canonical design patterns book
Site Reliability Engineering
- Site Reliability Engineering: How Google Runs Production Systems
UNIX and Linux System Administration Handbook, 4th Edition

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:
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?

Video Series

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

Name		Name	Last commit message	Last commit date
Latest commit History 436 Commits
extras		extras
.gitignore		.gitignore
LICENSE.txt		LICENSE.txt
README-cn.md		README-cn.md
README.md		README.md
programming-language-resources.md		programming-language-resources.md

License

XUZHOUWANG/google-interview-university

Folders and files

Latest commit

History

Repository files navigation

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

Graphs

Even More Knowledge

Recursion

Dynamic Programming

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

General:

Sorts:

Coding Question Practice

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

Additional Books

Additional Learning

Unicode

Endianness

Emacs and vi(m)

Unix command line tools

Information theory (videos)

Parity & Hamming Code (videos)

Entropy

Cryptography

Compression

Packages