Google Interview University
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 wordforword in Google’s coaching notes.
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, lowlevel 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 BigO 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 multidimensional 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]
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:
 How to Get a Job at the Big 4:
 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.
Read more about choices:
 http://www.bytebybyte.com/choosetherightlanguageforyourcodinginterview/
 http://blog.codingforinterviews.com/bestprogramminglanguagejobs/
 https://www.quora.com/WhatisthebestlanguagetoprograminforaninpersonGoogleinterview
You’ll see some C, C++, and Python learning included below, because I’m learning. There are a few books involved, see the bottom.
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 mobilefirst website so I could review on my phone and tablet, whereever I am.
Make your own for free:
I’ll add my set of flashcards to that repo soon so you have access to a lot of cards.
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, BigO 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 todo list made from Google interview coaching notes. These are prevalent technologies but were not mentioned in those notes:
 SQL
 Javascript
 HTML, CSS, and other frontend 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 builtin types C++ – using builtin types, like STL’s std::list for a linked list Python – using builtin 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 builtin types so I have experience using the builtin tools for realworld 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:
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 floating point numbers are stored:
 simple 8bit: Fractions in binary? (video)
 32 bit: Representation of Floating Point Numbers – 1 (video)
 64 bit: IEEE754 32bit floating point binary (video)
 Computer Arch Intro: (first video only – interesting but not required) Introduction and Basics – Carnegie Mellon – Computer Architecture
 Compilers
Algorithmic complexity / BigO / 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:
 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):
 Cheat sheetIf 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:
 C Code (video) – not the whole video, just portions about Node struct and memory allocation.
 Linked List vs Arrays:
 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.
 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
 Doublylinked List
 Description (video)
 No need to implement

Stack
 Stacks (video)
 Using Stacks LastIn FirstOut (video)
 Will not implement. Implementing with array is trivial.

Queue
 Using Queues FirstIn FirstOut(video)
 Queue (video)
 Circular buffer/FIFO
 Priority Queues (video)
 Implement using linkedlist, with tail pointer:
 enqueue(value) – adds value at position at tail
 dequeue() – returns value and removes least recently added element (front)
 empty()
 Implement using fixedsized 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

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.

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:
 swap values:
 absolute value:
Trees

Trees – Notes & Background
 Series: Core Trees (video)
 Series: Trees (video)
 basic tree construction
 traversal
 manipulation algorithms
 BFS (breadthfirst search)
 MIT (video)
 level order (BFS, using queue) time complexity: O(n) space complexity: best: O(1), worst: O(n/2)=O(n)
 DFS (depthfirst 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++:
 Binary search tree – Implementation in C/C++ (video)
 BST implementation – memory allocation in stack and heap (video)
 Find min and max element in a binary search tree (video)
 Find height of a binary tree (video)
 Binary tree traversal – breadthfirst and depthfirst strategies (video)
 Binary tree: Level Order Traversal (video)
 Binary tree traversal: Preorder, Inorder, Postorder (video)
 Check if a binary tree is binary search tree or not (video)
 Delete a node from Binary Search Tree (video)
 Inorder Successor in a binary search tree (video)
 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 nexthighest 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 (maxheap)
 Implement a maxheap:
 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 inplace using a max heap
 note: using a min heap instead would save operations, but double the space needed (cannot do inplace).

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 redblack trees seem to be more popular. A particularly interesting selforganizing 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 redblack 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 BTree since it’s used so widely with very large data sets.
 Selfbalancing 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
 23 search trees
 In practice: 23 trees have faster inserts at the expense of slower searches (since height is more compared to AVL trees).
 You would use 23 tree very rarely because its implementation involves different types of nodes. Instead, people use Red Black trees.
 23Tree Intuition and Definition (video)
 Binary View of 23Tree
 23 Trees (student recitation) (video)
 234 Trees (aka 24 trees)
 In practice: For every 24 tree, there are corresponding red–black trees with data elements in the same order. The insertion and deletion operations on 24 trees are also equivalent to colorflipping and rotations in red–black trees. This makes 24 trees an important tool for understanding the logic behind red–black trees, and this is why many introductory algorithm texts introduce 24 trees just before red–black trees, even though 24 trees are not often used in practice.
 CS 61B Lecture 26: Balanced Search Trees (video)
 Bottom Up 234Trees (video)
 Top Down 234Trees (video)
 BTrees
 fun fact: it’s a mystery, but the B could stand for Boeing, Balanced, or Bayer (coinventor)
 In Practice: BTrees are widely used in databases. Most modern filesystems use Btrees (or Variants). In addition to its use in databases, the Btree 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 cylinderheadsector) address.
 BTree
 Introduction to BTrees (video)
 BTree Definition and Insertion (video)
 BTree Deletion (video)
 MIT 6.851 – Memory Hierarchy Models (video) – covers cacheoblivious BTrees, 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 worstcase guarantees for insertion time, deletion time, and search time. Not only does this make them valuable in timesensitive applications such as realtime applications, but it makes them valuable building blocks in other data structures which provide worstcase 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 RedBlack 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

Nary (Kary, Mary) trees
 note: the N or K is the branching factor (max branches)
 binary trees are a 2ary tree, with branching factor = 2
 23 trees are 3ary
 KAry Tree
 note: the N or K is the branching factor (max branches)
Sorting
 Notes:
 Implement sorts & know best case/worst case, average complexity of each:
 no bubble sort – it’s terrible – O(n^2), except when n <= 16
 stability in sorting algorithms (“Is Quicksort stable?”)
 Which algorithms can be used on linked lists? Which on arrays? Which on both?
 I wouldn’t recommend sorting a linked list, but merge sort is doable.
 Merge Sort For Linked List
 Implement sorts & know best case/worst case, average complexity of each:
 For heapsort, see Heap data structure above. Heap sort is great, but not stable.
 Bubble Sort (video)
 Analyzing Bubble Sort (video)
 Insertion Sort, Merge Sort (video)
 Insertion Sort (video)
 Merge Sort (video)
 Quicksort (video)
 Selection Sort (video)
 Stanford lectures on sorting:
 Shai Simonson, Aduni.org:
 Steven Skiena lectures on sorting:
 UC Berkeley:
 – Merge sort code:
 – Quick sort code:
 Implement:
 Mergesort: O(n log n) average and worst case
 Quicksort O(n log n) average case
 Selection sort and insertion sort are both O(n^2) average and worst case
 For heapsort, see Heap data structure above.
 For curiosity – not required:
Graphs
Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were.
 Notes from Yegge:
 There are three basic ways to represent a graph in memory:
 objects and pointers
 matrix
 adjacency list
 Familiarize yourself with each representation and its pros & cons
 BFS and DFS – know their computational complexity, their tradeoffs, and how to implement them in real code
 When asked a question, look for a graphbased solution first, then move on if none.
 There are three basic ways to represent a graph in memory:
 Skiena Lectures – great intro:
 CSE373 2012 – Lecture 11 – Graph Data Structures (video)
 CSE373 2012 – Lecture 12 – BreadthFirst Search (video)
 CSE373 2012 – Lecture 13 – Graph Algorithms (video)
 CSE373 2012 – Lecture 14 – Graph Algorithms (con’t) (video)
 CSE373 2012 – Lecture 15 – Graph Algorithms (con’t 2) (video)
 CSE373 2012 – Lecture 16 – Graph Algorithms (con’t 3) (video)
 Graphs (review and more):
 6.006 SingleSource Shortest Paths Problem (video)
 6.006 Dijkstra (video)
 6.006 BellmanFord (video)
 6.006 Speeding Up Dijkstra (video)
 Aduni: Graph Algorithms I – Topological Sorting, Minimum Spanning Trees, Prim’s Algorithm – Lecture 6 (video)
 Aduni: Graph Algorithms II – DFS, BFS, Kruskal’s Algorithm, Union Find Data Structure – Lecture 7 (video)
 Aduni: Graph Algorithms III: Shortest Path – Lecture 8 (video)
 Aduni: Graph Alg. IV: Intro to geometric algorithms – Lecture 9 (video)
 CS 61B 2014 (starting at 58:09) (video)
 CS 61B 2014: Weighted graphs (video)
 Greedy Algorithms: Minimum Spanning Tree (video)
 Strongly Connected Components Kosaraju’s Algorithm Graph Algorithm (video)
 Full Coursera Course:
 Yegge: If you get a chance, try to study up on fancier algorithms:
 Dijkstra’s algorithm – see above – 6.006
 A*
 I’ll implement:
 DFS with adjacency list (recursive)
 DFS with adjacency list (iterative with stack)
 DFS with adjacency matrix (recursive)
 DFS with adjacency matrix (iterative with stack)
 BFS with adjacency list
 BFS with adjacency matrix
 singlesource shortest path (Dijkstra)
 minimum spanning tree
 DFSbased algorithms (see Aduni videos above):
 check for cycle (needed for topological sort, since we’ll check for cycle before starting)
 topological sort
 count connected components in a graph
 list strongly connected components
 check for bipartite graph
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?

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:
 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:
 Just the videos – 41 (each are simple and each are short):

NP, NPComplete and Approximation Algorithms
 Know about the most famous classes of NPcomplete 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 NPcomplete means.
 Computational Complexity (video)
 Simonson:
 Skiena:
 Complexity: P, NP, NPcompleteness, Reductions (video)
 Complexity: Approximation Algorithms (video)
 Complexity: FixedParameter Algorithms (video)
 Peter Norvik discusses nearoptimal solutions to traveling salesman problem:
 Pages 1048 – 1140 in CLRS if you have it.

Garbage collection

Caches

Processes and Threads
 Computer Science 162 – Operating Systems (25 videos):
 for precesses and threads see videos 111
 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 (readonly) 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
 Processes, Threads, Concurrency issues
 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.
 Computer Science 162 – Operating Systems (25 videos):

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
 scalability
 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:
 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
 O – Open/Closed Principal  On production level Objects are ready for extension for not for modification
 L – Liskov Substitution Principal  Base Class and Derived class follow ‘IS A’ principal
 I – Interface segregation principle  clients should not be forced to implement interfaces they don’t use
 D –Dependency Inversion principle  Reduce the dependency In composition of objects.
 Scalability:
 Great overview (video)
 Short series:
 Scalable Web Architecture and Distributed Systems
 Fallacies of Distributed Computing Explained
 Pragmatic Programming Techniques
 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 PlanetScale Engineering for PlanetScale 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
 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:
 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:
 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
 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
 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
 Understand the problem and scope:
 Exercises:
 Design a CDN network: old article
 Design a random unique ID generation system
 Design an online multiplayer card game
 Design a keyvalue database
 Design a function to return the top k requests during past time interval
 Design a picture sharing system
 Design a recommendation system
 Design a URLshortener system: copied from above
 Design a cache system
 Considerations from Yegge:

Papers
 These are Google papers and wellknown 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
 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:
 2013: Spanner: Google’s GloballyDistributed Database:
 2014: Machine Learning: The HighInterest Credit Card of Technical Debt
 2015: Continuous Pipelines at Google
 2015: HighAvailability at Massive Scale: Building Google’s Data Infrastructure for Ads
 2015: TensorFlow: LargeScale Machine Learning on Heterogeneous Distributed Systems
 2015: How Developers Search for Code: A Case Study
 2016: Borg, Omega, and Kubernetes

Unicode

Emacs and vi(m)
 suggested by Yegge, from an old Amazon recruiting post: Familiarize yourself with a unixbased code editor
 vi(m):
 emacs:

Unix command line tools

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 – TestDriven Development (that’s not what we meant) (video)
 TDD is dead. Long live testing.
 Is TDD dead? (video)
 Video series (152 videos) – not all are needed (video)
 TestDriven Web Development with Python
 Dependency injection:
 How to write tests
 To cover:

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) – AbstractionOccurrence, General Hierarchy, PlayerRole, Singleton, Observer, Delegation (video)
 Chapter 6 (Part 3) – Adapter, Facade, Immutable, ReadOnly Interface, Proxy (video)
 Series of videos (27 videos)
 Head First Design Patterns
 I know the canonical book is “Design Patterns: Elements of Reusable ObjectOriented Software”, but Head First is great for beginners to OO.
 Handy reference: 101 Design Patterns & Tips for Developers

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)
 RabinKarp (videos):
 Precomputing
 Optimization: Implementation and Analysis
 KnuthMorrisPratt (KMP):
 Boyer–Moore string search algorithm
 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 23 minutes short subject videos (23 videos)
 Series of 25 minutes short subject videos – Michael Sambol (18 videos):
Sorts:
 Merge Sort: https://www.youtube.com/watch?v=GCae1WNvnZM
Books
Mentioned in Google Coaching
Read and do exercises:
 The Algorithm Design Manual (Skiena)
 Book (can rent on kindle):
 Half.com is a great resource for textbooks at good prices.
 Answers:
 Errata
Once you’ve understood everything in the daily plan, and read and done exercises from the the books above, read and do exercises from the books below. Then move to coding challenges (further down below)
Read first:
Read second (recommended by many, but not in Google coaching docs):
 Cracking the Coding Interview, 6th Edition
 If you see people reference “The Google Resume”, it was a book replaced by “Cracking the Coding Interview”.
Additional books
These were not suggested by Google but I added because I needed the background knowledge
 C Programming Language, Vol 2
 C++ Primer Plus, 6th Edition
 The Unix Programming Environment
 Programming Pearls
 Algorithms and Programming: Problems and Solutions
If you have time
 Introduction to Algorithms
 Half.com is a great resource for textbooks at good prices.
 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.
 Great intro (copied from System Design section): Algorithm design:
 How to Find a Solution
 How to Dissect a Topcoder Problem Statement
 Mathematics for Topcoders
 Dynamic Programming – From Novice to Advanced
 MIT Interview Materials
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 23 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?
Additional Learnings (not required)
Everything below is my recommendation, not Google's, and you may not have enough time to
learn, watch or read them all. That's ok. I may not either.

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 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)

Computer Security

Parallel Programming

Messaging, Serialization, and Queueing Systems

Fast Fourier Transform

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

Augmented 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

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

Treap
 Combination of a binary search tree and a heap
 Treap
 Data Structures: Treaps explained (video)
 Applications in set operations

Linear Programming (videos)

Geometry, Convex hull (videos)

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])http://www.analyticsvidhya.com/blog/2016/04/neuralnetworkspythontheano/)
 Courses: (videos)
 Great starter course: Machine Learning
 videos only
 see videos 1218 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
 SelfDriving Car Engineer Nanodegree
 Metis Online Course ($99 for 2 months)
 Great starter course: Machine Learning
 Resources:
 Great book: Data Science from Scratch: First Principles with Python: https://www.amazon.com/DataScienceScratchPrinciplesPython/dp/149190142X
 Data School: http://www.dataschool.io/

Go
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 Dynamic Programming (videos)
 6.006: Dynamic Programming I: Fibonacci, Shortest Paths
 [ ] 6.006: Dynamic Programming II: Text Justification, Blackjack
 6.006: DP III: Parenthesization, Edit Distance, Knapsack
 6.006: DP IV: Guitar Fingering, Tetris, Super Mario Bros.
 6.046: Dynamic Programming & Advanced DP
 6.046: Dynamic Programming: AllPairs Shortest Paths
 6.046: Dynamic Programming (student recitation)
 Advanced Graph Processing (videos)
 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” 😛
 List of individual Dynamic Programming problems (each is short)
 x86 Architecture, Assembly, Applications (11 videos)
 MIT 18.06 Linear Algebra, Spring 2005 (35 videos)
 Excellent – MIT Calculus Revisited: Single Variable Calculus
 Computer Science 70, 001 – Spring 2015 – Discrete Mathematics and Probability Theory
 Discrete Mathematics (19 videos)
 CSE373 – Analysis of Algorithms (25 videos)
 UC Berkeley 61B (Spring 2014): Data Structures (25 videos)
 UC Berkeley 61B (Fall 2006): Data Structures (39 videos)
 UC Berkeley 61C: Machine Structures (26 videos)
 OOSE: Software Dev Using UML and Java (21 videos)
 UC Berkeley CS 152: Computer Architecture and Engineering (20 videos)
 MIT 6.004: Computation Structures (49 videos)
 MIT 6.006: Intro to Algorithms (47 videos)
 MIT 6.033: Computer System Engineering (22 videos)
 MIT 6.034 Artificial Intelligence, Fall 2010 (30 videos)
 MIT 6.042J: Mathematics for Computer Science, Fall 2010 (25 videos)
 MIT 6.046: Design and Analysis of Algorithms (34 videos)
 MIT 6.050J: Information and Entropy, Spring 2008 (19 videos)
 MIT 6.851: Advanced Data Structures (22 videos)
 MIT 6.854: Advanced Algorithms, Spring 2016 (24 videos)
 MIT 6.858 Computer Systems Security, Fall 2014
 Stanford: Programming Paradigms (17 videos)
 Introduction to Cryptography
 Mining Massive Datasets – Stanford University (94 videos)
https://github.com/jwasham/googleinterviewuniversity