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Google Interview University

(formerly known as Project 9894)

What is it?

This is my multi-month study plan for going from web developer (self-taught, no CS degree) to Google software engineer. Don't let that offend you if you are a web developer. I'm speaking from my experience, not yours.

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.

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 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 in to 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 my progress.

I check each task box at the beginning of a line when I'm done with it. When all sub-items in a block are done, I put [x] at the top level, meaning the entire block is done. Sorry you have to remove all my [x] markings to use this the same way. If you search/replace, just replace [x] with [ ]. Sometimes I just put a [x] at top level if I know I've done all the subtasks, to cut down on clutter.

More about Github flavored markdown: https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown

I have a friendly referral already to get my resume in at Google. Thanks JP.

Get in a Googley Mood

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

Follow me

John Washam

I'm on the journey. Follow along at GoogleyAsHeck.com

Interview Process & General Interview Prep

Prerequisite Knowledge

This short section were prerequisites/interesting info I wanted to learn before getting started on the daily plan.

You need to know C, C++, or Java to do the coding part of the interview. They will sometimes make an exception and let you use Python or some other language, but the language must be mainstream and allow you write your code low-level enough to solve the problems. You'll see some C, C++ learning included below.

There are a few books involved, see the bottom.

Some videos are available only by enrolling in a Coursera or EdX class. It is free to do so.

The Daily Plan:

Each subject does not require a whole day to be able to understand it fully, and you can do multiple of these in a day.

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 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 to make sure it's not buggy from syntax.

Data Structures

More Knowledge

Trees

Graphs

This area is sparse (no pun intended), and I'll be filling it in once I get here.

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

Sorting

This area is sparse, and I'll be filling it in once I get here.

Even More Knowledge

This area is sparse, and I'll be filling it in once I get here.

Books

Mentioned in Google Coaching:

Read first:

Read second:

Additional books (not suggested by Google but I added):

If you see people reference "The Google Resume", it was a book replaced by "Cracking the Coding Interview".

About Google

Articles

Papers:

Computing Weak Consistency in Polynomial Time - http://dl.acm.org/ft_gateway.cfm?id=2767407&ftid=1607485&dwn=1&CFID=627637486&CFTOKEN=49290244

How Developers Search for Code: A Case Study - http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43835.pdf

Borg, Omega, and Kubernetes - http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/44843.pdf

Continuous Pipelines at Google - http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43790.pdf

AddressSanitizer: A Fast Address Sanity Checker - http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37752.pdf

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.

The Best:

More:

Once you're closer to the interview:

Your Resume

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?

Additional Resources

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.

Videos

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

Maybe

http://www.gainlo.co/ - Mock interviewers from big companies

Code References

For review questions in C book: https://github.com/lekkas/c-algorithms

Once You've Got The Job

This is mainly for me.

Done

You're never really done. Keep learning.