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Algorithms, Data Structures, System Design and Interview Questions

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Algorithms, Data Structures, System Design and Interview Questions

Owned by Nathan Wailes
Last updated: Sept 29, 2024
33 min read

Related pages

My overall strategy on this page is to 1) summarize information I find around the web, and then 2) try to weave the information I have in those summaries into unified processes for handling DSA questions, system design questions, API design questions, how to format a resume, etc.

Non-algorithm-specific interview advice

Algorithms

A step-by-step process for solving algorithm questions

  1. Understand that it is critical that you think out loud for the steps that follow.

    1. Sources:

      1. Why I Fail Candidates During Google Interviews.

        1. The next issue that most people have is that they're a little too quiet, and I get it. A lot of us try to code on our own late at night without talking to anyone, but when you're in the workforce, you have to show how well you can communicate with others. The best way to do that is by thinking out loud. So when you're writing your code, definitely express what you're thinking line by line as you're coding it out.

  2. Clarify the question with the interviewer.

    1. Be sure you understand the limits of the input.

    2. Define some of your own test cases.

    3. Be sure you understand what the output should be for the test cases you define.

    4. Sources:

      1. Why I Fail Candidates During Google Interviews.

        1. The biggest mistake that most candidates make is not clarifying the question. I think people are a little too eager to start coding, and they end up solving the wrong problem or forgetting a few test cases. The simple thing that most people can do to start clarifying the problem is just to ask questions about it, understand what the limits of the problem are, define their own test cases, and understand what the input should be for the expected output.

          1. “What the input should be for the expected output”? Huh? Did he mean “what the output should be for a given input”?

  3. Draw out a simple visual example.

    1. Do it in something like Balsamiq.

    2. Copy-paste it to visually show what the step-by-step process should look like.

    3. Think about what variables you want at each step to answer the relevant question you have to be able to calculate the desired answer.

  4. Ask yourself: What features of the state of each ‘step’ through the input can you think of to assign names to? Write the code needed to calculate/update these values with each iteration (or just have them as stub functions to be filled in later) and it could give you enough of a step in the right direction for the rest of the solution to make itself clear.

  5. Step through the code with a simple example.

    1. 2024.09.14 - I’m thinking it may make sense to keep track of variables in a separate comment block, keeping track of the call stack / execution contexts as well. So basically doing what Jetbrains does, but manually.

    2. One thing I’m noticing is that if I have the comment block with the variables above the code, then I can’t easily reference line numbers in the code because adding new lines to my comment block changes the line numbers of the code. So it seems better to keep the comment block below the code.

  6. After you write out your code, step through it line-by-line with at least one of the test cases.

    1. Sources:

      1. Why I Fail Candidates During Google Interviews.

        1. The other most common mistake that I've seen other people make is assuming that their code works, and what I mean by this is that after they go through the entire interview, they're just so glad to write down all the code that they forget to test if it actually works or does what it's supposed to do. So what candidates should be doing here is to actually go through their code line by line against one of the test cases that they outlined at the beginning of the interview. If you do that, then it's not just assuming that it works, it actually does work, and it shows the interviewer that you can really communicate your code and what's going on.

  7. Explain the running-time of your code by walking through the code and explaining the running-time of each area.

    1. Sources:

      1. Why I Fail Candidates During Google Interviews.

        1. The next topic I want to talk about is that people tend to struggle a little bit with the running time. What I mean by this is that most people feel like they have to just blurt out the running time or answer it like it's a multiple-choice exam or some other homework question. When someone asks you what the running time is, people don't want you to just say it's linear or n squared or whatever. They really want you to explain different areas of your code and what the running time is for those areas. If you can do that, you'll stand out a lot more than people who are just treating it like another exam question.

How to study

  1. Memorize how to work with basic data structures (CRUD and search operations).

    1. lists, dicts, sets, deques

  2. Memorize some basic algorithms that are used as building blocks in many LeetCode questions.

    1. Bookmark the two links below in a Chrome bookmark folder so you can quickly open all three links every day (right-click the folder and select “Open all”):

      1. Random 2 basic algorithms

      2. https://www.online-python.com/

    2. Every day, learn one of the basic algorithms listed in this pastebin if you haven’t already learned all of them.

      1. Start from the top and work your way down.

      2. To “learn” an algorithm:

        1. Read the pastebin until you feel like you understand how the algorithm works.

        2. Write out the algorithm on online-python.com with the pastebin open in a separate window (have the two windows open side-by-side).

          1. I recommend turning off their autocompletion feature as I find it more of a hindrance than a help.

        3. Set a timer for 60 minutes and then see if you can write out the algorithm on online-python.com from memory.

          1. If you can’t, just keep trying that day until you can. Adjust the timer duration as necessary.

    3. Every day, review two of the basic algorithms you’ve already learned.

      1. Write out the algorithm, including a test case, from memory on online-python.com

      2. You should be able to do this review in ~5 minutes.

  3. Memorize the NeetCode 150.

    1. I put all of the Neetcode 150 in Anki, separated by question type.

    2. Creating Replit versions:

      1. If I can’t access the question on Leetcode (it’s a premium question) or the Lintcode version (or if the Lintcode version is too different from the Leetcode question), I can create a Replit version in here.

      2. Make the ‘template’ version of the question, then when you actually want to do the question, use that same Replit and you can just use the git feature to discard all of the changes you made to the code when you’re done.

    3. Thought: I want to have a way to separately review a high-level description of how to solve a problem vs. reviewing the writing out of the actual solution. Like, I want to be able to do 10 high-level solutions per day (where I just have to remember a plain-English sentence or two about how to solve a question) and 1 written-out solution (where I actually have to write out the solution and run it to make sure it works). I think this is a good way to more-frequently refresh my memory of solutions without spending too much time per day. I want the whole thing to take ~15 minutes.

    4. Thought: It’s sometimes not clear when I “know” a solution. Like, I solved a linked list question, but then I saw in the LeetCode explanation that there was a more-efficient solution. So when do I review the card? In a day, to see if I remember the efficient solution? Or in 6 months, when I might have forgotten my less-efficient solution?

    5. 2024.08.27 - What I’m doing now is aiming to spend an hour per day on the NeetCode 150, where my goal is to memorize the approach used to solve the question rather than try to “figure out” the question on my own. So I will spend a few minutes trying to remember the approach to solve the question, if I can’t then I just look at the LeetCode Editorial (both explanation and code) and set the due date of the card for 7-14 days from now to see if I can remember the approach at that time. Once I can remember the approach, I then see if I can write out the code. So I’m using the same Anki card to test my memory of two different things: 1) if I can remember a plain-English description of the approach for solving the question, and 2) if I can remember both the plain-English description and also how to write out the code.

      1. If I end up with extra time from solving a question quickly, I’ll keep going with more NeetCode 150 Anki cards until the hour is up.

    6. 2024.08.29 - I think the way to do this is:

      1. For each card, just read the editorials of the different approaches and set the card for a day or two out and just make sure you can remember the various different approaches. Interviewers apparently want to see that you can identify the different possible approaches to a problem.

      2. Once you can identify the different approaches, set the card for maybe 7-14 days out to see if you can actually write out the ideal approach (and maybe one of the unideal approaches?).

    7. 2024.08.31 - If you’re limited on time on a certain day, try to make at least 20 minutes of progress so you don’t lose momentum. Just watch the LC Editorial video for a question, or just watch the Neetcode video and try to summarize what you see, or just skim the explanations and try to fill out the Anki card with a summary of what you read. Or just read the explanation of one of the possible approaches in-depth and try to summarize it.

      1. Example: I’m taking several days to work on the LRU Cache LC question.

        1. The first day I just opened the editorial, skimmed it, and wrote out on the Anki card “1. doubly-linked list. TODO: fill this out” and “2. use built-in data structures like OrderedDict(). You need to know how to use the .move_to_end() and .popitem() methods”.

        2. The second day I skimmed the explanation of the first approach in greater detail so that I could fill out a summary on the Anki card.

        3. My idea is that on a third day I’ll skim the code of one or both explanations and then try to write it out from memory afterwards (immediately afterwards? after 30 minutes? I’m not sure yet).

    8. 2024.09.06 - I had a good idea today: sometimes I’ll solve a question using the ‘normal’ approach but still feel like the card is worth reviewing to test myself on an alternative way of solving the question. So today I just made a note of what solution I wanted to try next time in my note in the answer section of Anki, and made a similar note in the ‘Question’ section so that the next time I see the card I’ll remember what my goal is:

    9. 2024.09.07

      1. When you’re first learning a question, you can use Anki to just test your ability to read the prompt and identify the major issues / tricky things about the question, and the possible approaches for dealing with them. Don’t even bother trying to code the solution until you’ve internalized that much.

      2. I’m also thinking it might make sense to skip the Hards until you’ve first gone through and become comfortable with the Mediums in every topic. I think I’m going to skip the Linked List Hards until I get through the rest of the questions first.

      3. I’m also thinking I should maybe add all the Easy questions to the list of daily practice problems that I randomly choose two from to write out.

    10. 2024.09.10 - I’m working on a binary tree LC question (LC 102) and it’s occurring to me while I try to step through an example that I don’t have a clear system for writing out the state of the variables as I step through the code while keeping comments to the side. For example, I’m just now deciding that I will try to keep track of the state of variables where they’re defined, and then if there’s a .pop from it, I just add a comment pointing up to indicate that I should look above for the state. But it’s making me realize that there should really be a standardized / best-practice way of doing this, but it’s not something I see even being discussed online.

    11. 2024.09.10

      1. When I first see a new problem, I don’t have it as a goal to write out the code. I just want to spend literally a few minutes thinking about it to see if I can guess what the correct approach is, and then I look at the LC Editorial / NC video to see what the correct approach is. I think this is a much nicer way to do things, much less likely to waste your time. Previously I would spend hours trying to figure it out on my own. You don’t want to waste time going in the wrong direction.

      2. I’m looking at LC 572, supposedly an “Easy”, and the latter two solutions in LC are both closer to being Hard questions. So I guess I shouldn’t worry about memorizing every possible solution to a question as I move through questions. Maybe set it as a later goal.

  4. Try solving questions on LeetCode that are labeled as being asked by the company you’re trying to apply to.

Data structures

Questions

  • What is the threshold at which point it's a good idea to create a class for a data structure?

Learning Resources

Algorithms Unlocked

  • Summary

    • 1. What Are Algorithms and Why Should You Care?

    • 2. How to Describe and Evaluate Computer Algorithms

    • 3. Algorithms for Sorting and Searching

    • 4. A Lower Bound for Sorting and How to Beat It

    • 5. Directed Acyclic Graphs

    • 6. Shortest Paths

    • 7. Algorithms on Strings

    • 8. Foundations of Cryptography

    • 9. Data Compression

    • 10. Hard? Problems

Topics

Linked Lists

Databases

Dynamic Programming (DP)

Heaps

Recursion

Trees

Binary Trees

The interview process

Flashcards



System Design

Step-by-step process

  1. Clarify the task.

    1. Share what you know about the system with the interviewer.

    2. If you’re not familiar with the system, tell the interviewer so they can explain how it should work.

    3. Constrain the problem to make it solvable within the hour.

    4. List the different metrics you could consider tracking / focusing on and explain the one you think we should focus on and why.

      1. Total users, DAU, WAU, MAU. Maybe daily minutes per active user.

      2. Uber: driver ratings, trip completion rates, and acceptance rates, number of rides completed per day, number of cancelled rides, number of rider complaints raised, average loading time, ratio of daily active users to monthly active users, number of daily rides booked per daily active user, average revenue per user, average wait time

  2. Clarify the scale of usage, the amount of data involved, the rate of requests.

    1. Specify some key metrics to help your high-level decision-making.

      1. Example: the number of users, the amount of data.

      2. Try to determine the number of requests per day, divide by 100,000 (seconds per day) to get the average requests per second, and take a guess at the peak factor (how much times the number of requests per second you might see in peak traffic), take a guess at how many requests per second a typical server might be able to handle (say, 1000), and use that to determine how many servers you’ll need.

        1. It doesn’t have to be perfect, the idea is to see that you’re aware of these ideas.

        2. If you’re going to be using auto-scaling servers, that’s fine.

        3. (Source)

    2. You don’t necessarily have to go into the details of scaling at this point.

  3. Design the system at a high level.

    1. At an early stage, it’s OK to have a few simple components; later on you can get more complex.

    2. Example:

      1. If asked to design Spotify, you could draw a mobile app connected to a load balancer which connects to a group of webservers, which connect to the database.

  4. Outline your database design.

    1. After laying out your components, design your database.

    2. Split out databases appropriate for different types of data.

    3. In general, try to explain why you’re doing something upfront rather than waiting for your interviewer to ask you.

  5. Go through a normal use-cases and explain/flesh out how it will work in greater detail.

    1. Consider how the webservers will retrieve data from the db and how they’ll send it to the end-user; will they use websockets?

  6. If you have more time, think about how things could “go wrong” in terms of scaling.

    1. Examples:

      1. When designing Spotify, some songs might be requested by users for listening far more than others.

    2. Solutions:

      1. A CDN

Learning resources

API Design Patterns

  • Summary

    • PART 1: INTRODUCTION

      • 1 Introduction to APIs

      • 2 Introduction to API design patterns

    • PART 2: DESIGN PRINCIPLES

      • 3 Naming

      • 4 Resource scope and hierarchy

      • 5 Data types and defaults

    • PART 3: FUNDAMENTALS

      • 6 Resource identification

      • 7 Standard methods

      • 8 Partial updates and retrievals

      • 9 Custom methods

      • 10 Long-running operations

      • 11 Rerunnable jobs

    • PART 4: RESOURCE RELATIONSHIPS

      • 12 Singleton sub-resources

      • 13 Cross references

      • 14 Association resources

      • 15 Add and remove custom methods

      • 16 Polymorphism

    • PART 5: COLLECTIVE OPERATIONS

      • 17 Copy and move

      • 18 Batch operations

      • 19 Criteria-based deletion

      • 20 Anonymous writes

      • 21 Pagination

      • 22 Filtering

      • 23 Importing and exporting

    • PART 6: SAFETY AND SECURITY

      • 24 Versioning and compatibility

      • 25 Soft deletion

      • 26 Request deduplication

      • 27 Request validation

      • 28 Resource revisions

      • 29 Request retrial

      • 30 Request authentication

Designing APIs with Swagger and OpenAPI

  • Summary

    • PART 1 DESCRIBING APIS

      • 1 Introducing APIs and OpenAPI

      • 2 Getting set up to make API requests

      • 3 Our first taste of OpenAPI definitions

      • 4 Using Swagger Editor to write OpenAPI definitions

      • 5 Describing API responses

      • 6 Creating resources

      • 7 Adding authentication and authorization

      • 8 Preparing and hosting API documentation

    • PART 2 DESIGN-FIRST

      • 9 Designing a web application

      • 10 Creating an API design using OpenAPI

      • 11 Building a change workflow around API design–first

      • 12 Implementing frontend code and reacting to changes

      • 13 Building a backend with Node.js and Swagger Codegen

      • 14 Integrating and releasing the web application

    • PART 3 EXTENDING APIS

      • 15 Designing the next API iteration

      • 16 Designing schemas with composition in OpenAPI

      • 17 Scaling collection endpoints with filters and pagination

      • 18 Supporting the unhappy path: Error handling with problem+json

      • 19 Improving input validation with advanced JSON Schema

      • 20 Versioning an API and handling breaking changes

      • 21 The API prerelease checklist

Designing Data-Intensive Applications

 

IGotAnOffer

  • 2023.03.10 - Design Spotify (with ex-Google EM)

    • Summary

      • He goes through the various use cases of Spotify he can think of, then asks which one he should focus on, which the interviewer limits to “finding and playing music”.

      • He asks how many users and how many songs the interviewer wants to support, then goes through some math to try to estimate how much data they’ll need to store, but he never ends up using that information later on.

      • He draws out a high-level diagram of the system: a phone connects to a load balancer which connects to a bunch of web servers which connect to a database.

      • He goes deeper on the database and explains that they’ll want to have a relational db for user metadata (AWS RDS) and blob storage for the songs (AWS S3). He then lists some example fields you might want to use for the song table in the relational db.

      • 23:05 - He discusses how to send the audio data to the user, he initially suggests using websockets to send chunks to the webserver, then decides that for only 5mb it would be “almost instantaneous” to send data from S3 to the webservers, and would remove any lag from hitting the database. So that makes me wonder whether the S3 db would be at the same datacenter as the webservers and what kind of bandwidth you can get between them.

        • I’m interested in knowing the process for determining whether to chunk between the EC2 instance and the S3 bucket.

      • 25:37 - After creating his relatively-simple design for Spotify, he asks himself, “What could go wrong here?” and realizes that certain songs/artists could be way more popular than others. To avoid wasting bandwidth, he recommends using a CDN (a cache).

      • 33:00 - He’s talking about other types of caches: a cache layer between the db and the webservers (redis?), a local cache within each webserver, and then a cache within the phone app to store frequently-played songs.

      • 35:20 - More on the load balancer - Normally load balancers strive to equalize the CPU load across servers, but for this app he might focus more on I/O and/or RAM usage; he’d want to make it smarter than a normal round-robin system. [NW: Can a load balancer dynamically divert traffic like that? That’s not how I thought they worked.]

      • 38:20 - You may want to locate your replicas closer to the geographies where the data is more likely to be used.

    • Comments

      • Like many others I was designing my own version alongside Mark's and I think the area he was a little weaker in (which he himself admits) was load balancing. My background is systems administration so I may have a different perspective on this. I think going back a few steps, chunking the data also serves an important role in the load balancing process. I would have songs chunked, from every retrieval source, so that as soon as the user presses Play, the song begins playing, and playback should always be an instantaneous process unless the servers are over capacity, which can occur because some song or album has gone viral.

        I would structure the web server load balancing so that client apps attempt to contact the server geographically closest to them first and utilize GSLB (global site load balancing) which combines layer 4 and layer 7 load balancing, as I/O capacity or concurrent connections (the two metrics I would prioritize) reach a threshold.

        Again, when talking about load balancing, it's important to determine what happens when maximum capacity on all servers is exceeded. When this happens in my design, the system will issue "tickets" for data chunks, served in the order they arrive in. This is where song chunking comes into play. Because we are chunking the MP3 data, we can still serve the first chunk of the song from the nearest available server as soon as that I/O is available, further ensuring near-instantaneous playback upon pressing the Play button. The rest of the song then has some time to download and cache to the client device, reducing the number of interruptions and pauses in playback due to bandwidth and concurrent connection overages.

  • 2023.06.30 - Design TikTok (with ex-Google EM)

    • He just straight up asks the interviewer what use case he wants him to focus on without describing anything about how the app works. The interviewer says he wants him to ignore how the app itself works and instead focus on the backend that handles uploading and downloading videos. He clarifies that the interviewer doesn’t want him to spend any time on how people will create accounts etc.

    • He asks the interviewer what scale / usage to build for and he’s told 1B users in 150 countries, 1B views per day, 10B videos uploaded in a year.

    • He asks the interviewer what kind of success metrics we should be aiming for and is asked to come up with them. He comes up with “time in app”.

    • He asks how much time we should expect an average active user to spend in the app, the interviewer says an hour.

  • System Design Interviews: 10 Key Principles (with ex-Google EM)

    • Communicate efficiently.

      • The interview is definitely an artificially time-constrained environment.

      • So you need to be efficient. You don’t want the interviewer wondering what you’re thinking. You want the interviewer to be able to follow your thinking.

      • They work with a lot of engineers who understand the technical part but struggle to clearly communicate what they’re thinking.

    • Scope the problem.

      • Real systems (like YouTube) are too complicated to fully design in a one-hour interview.

      • So you need to ask, “Which part of the app/feature-set should I focus on?”

      • You need to go along with what the interviewer wants to focus on.

    • Start drawing ~15 minutes in

      • This isn’t a hard number, it’s just a guideline.

      • If you start drawing too soon, you may not fully understand what the interviewer wants you to focus on, and start going down a wrong path.

      • If you wait too long, you may not have enough time to come up with a working solution.

      • The interviewer says that in one mock the Google EM didn’t start until 20 minutes in and would’ve been struggling to finish.

    • Start with a simple design

      • Don’t add requirements that don’t exist.

        • Example: Adding tiering storage could prevent you from having a complete solution by the end of the interviewer.

      • You can tell the interviewer that a particular optimization is available and how you’d use it, but don’t let it stop you.

      • If the interviewer asks you to go deeper on a subject, follow their lead.

    • Use prep resources

      • GitHub has a system design study guide, for example.

      • You might see some differences across guides

    • Properly understand the problem

      • Talk through specific use cases.

      • Imagine actually calling the API.

      • Try to catch assumptions you may have made.

      • Check in with the interviewer to see if you’re on the right path.

      • He mentions the name of the doc used for a full spec is “PRD” - Product Requirements Document

    • Practice, practice, practice!

      • You can watch and read a lot of material and think you understand it, but still not be able to perform when it comes time to do it for real.

      • Practicing will also make you relax when you have a real interview.

      • If you don’t have another person or coach, just record yourself.

      • The English guy said his mom pointed out he touches his hair and face too much.

      • Don’t underestimate how much time it’ll take for a mock interview; if you want enough time for back-and-forth, you may want to block out 2 hours. 10 minutes for feedback is pretty tight.

    • Explain your thinking

      • Example: Don’t just say “I’m going to use a database”, or “I’m going to use a MySQL/relational db”; explain why you’re choosing the particular technology you’re using. “The data is pretty well structured, I’ll need multiple tables, do queries across the data (join tables)”.

      • You don’t want to come across like you’re repeating memorized solutions without understanding the pros and cons of the technology.

      • The interviewer wants to see how clearly you can communicate.

      • It’s could be fine if the interviewer doesn’t agree with your choice.

    • Get comfortable with the math.

      • They want to see that you can make some assumptions and do some really basic back-of-the-envelope math.

      • Make sure the interviewer can follow your math.

      • If you have a billion queries a day, with ~100,000 seconds in a day you’re dealing with ~10,000 requests per second.

      • I want to hear from you that you know about peaks (in traffic), that you’re aware of it. So a peak factor of 5 means you could hit 50,000 requests per second.

      • If a server can handle 1,000 requests per second, that means we need ~50 servers.

      • Get comfortable with the units for larger amounts of data (terabytes, petabytes, exabytes). Showing familiarity with that

      • Don’t get bits and bytes confused. Network bandwidth is measured in bits per second, data is measured in bytes. You can keep it simple by estimating 10 bits per byte instead of 8.

      • Using a calculator is fine.

    • Use the drawing tool efficiently.

      • Be aware of what drawing tool they’ll want you to use.

      • You might be able to negotiate with the recruiter about what tool you use.

      • You want to be able to draw things quickly.

      • You need to know how to draw three things:

        • Boxes

        • Arrows

        • Text

      • Colors are not important, perfectness of arrows is not important, the text just needs to be good enough that you can refer to it.

      • It just needs to be clear what you’re pointing at when you have your mouse over something.

      • You don’t want the interviewer to have to keep track of stuff in their head.

      • It’s totally fine to change your diagram.

NeetCode

  • 2023.03.11 - NeetCode - 20 System Design Concepts Explained in 10 Minutes

    • Summary

      • Vertical Scaling - Adding more RAM or CPU to your server(s)

      • Horizontal Scaling - Adding more servers that replicate your app. This also adds fault tolerance, but it’s much more complicated.

      • Load Balancer - This is a server known as a reverse proxy to route traffic to your different app servers. To choose which server to send the request to you could use round robin or hash the request ID, or route to the nearest location if you have your servers located around the world.

      • CDN - A network of servers located around the world. They take files on your servers and copy them to the CDN servers. This can be done on a push or pull basis.

      • Caching - Creating copies of data so it can be re-fetched faster in the future. Browsers cache data from the internet on disk, but that can be expensive, so they’ll cache in memory, but that can also be expensive, so the computer will copy a subset of the data into the L1 / L2 / L3 CPU cache.

      • IP Address - How computers communicate.

      • TCP / IP - The Internet Protocol Suite. Also includes UDP. These are the rules that decide how we send data on the internet. Files are broken down into packets, the packets are numbered so they can be re-assembled. HTTP and Websockets are built on top of TCP / IP.

      • DNS - A decentralized service that that translates a domain name to an IP address. Your computer will cache the A Record so it doesn’t need to look up the IP address for every request.

      • HTTP - Why do we use HTTP to view websites? TCP is too low level. HTTP is an “application-layer protocol”. It follows the client-server model: a client will initiate a request, which includes two parts: a request header, which is like a shipping label on a package. The second part is the request body, which is like the package contents. There are multiple API patterns we can follow while using HTTP: REST, GraphQL, gRPC.

      • REST - A standardization aimed at making APIs stateless (i.e. a request can be diverted to any of numerous duplicated app servers). Includes ideas like a successful request should include a 200 header, a bad request gets a 400, and an error with the server gets a 500 response.

      • GraphQL - An API pattern introduced by Facebook in 2015. You can make a “query” and specify exactly which resources and which fields you want. This reduces the number of API requests needed and the amount of unnecessary data sent.

      • gRPC - An RPC (remote procedure call) API framework introduced by Google for server-to-server communication. gRPC Web lets you use gRPC from a browser. It has a performance boost over JSON because it converts the data to a compressed form in binary using a library called Protocol Buffers, but it’s not human-readable like JSON.

      • WebSocket - This is an API pattern helpful for stuff like chat apps where the client should be notified immediately of an update.

      • SQL - A db is better than storing stuff in plaintext files (like CSVs) because it allows for fast retrieval/querying of data.

      • ACID compliance - ACID (Atomicity, Consistency, Isolation, Durability) - Durability means the data will be there if the machine shuts down. Isolation means that different transactions won’t interfere with each other. Atomicity means that each transaction is all-or-nothing; you won’t have a partial transaction if the machine turns off in the middle of writing data. Consistency means that foreign key constraints will be enforced.

      • NoSQL databases - These dbs dropped the consistency constraint of SQL dbs and the idea of relations to make it easier to scale. Popular types are: key-value stores, document stores, and graph dbs. Not needing to worry about foreign key constraints means we can break up our data across different machines (sharding).

      • Sharding - This is when you break up your db. You split up the data based on a “shard key”, which can just be the ID of a user.

      • Replication - A simpler way to scale reads is to make copies of the entire database. When the copies are read-only, it’s called “leader-follower replication”. You can also do “leader-leader” replication but it can lead to inconsistencies, so it’s best to do it where you can ensure a given user will only be interacting with one of them, like if you have them in different regions in the world. It can be complicated to keep them in sync.

      • CAP thoerem - The idea that we have to choose between data consistency and data availability. It’s a somewhat controversial theorem, which led to the development of the PACELC theorem.

      • Message Queues - Kind of like db because they have durable storage, can be replicated / sharded. If your system is receiving more data than it can process, the queue can prevent requests from getting lost/dropped.

      • In summary: software engineering is all about storing and moving data around.

Scalability Rules

  • Key things I want to remember for the purposes of system design interviews:

    • Think about how you could break down an app into different services (e.g. login vs searching vs. completing a cart) hosted in different server pools, or how you could shard the data effectively (for example, maybe first by geography, then by user ID).

  • Summary / Notes

    1. Reduce the Equation

      1. Rule 1 - Don’t Overengineer the Solution

      2. Rule 2 - Design Scale into the Solution (D-I-D Process) - Design for 20x capacity, Implement for 3x capacity, Deploy for ~1.5x capacity

      3. Rule 3 - Simplify the Solution 3 Times Over

      4. Rule 4 - Reduce DNS Lookups

      5. Rule 5 - Reduce Objects Where Possible

      6. Rule 6 - Use Homogenous Networks

    2. Distribute Your Work

      1. Rule 7 - Design to Clone Things (X Axis) - Be able to duplicate data/services

      2. Rule 8 - Design to Split Different Things (Y Axis) - You can split up your app / data / servers based on either actions the user/app can take (“services”) or by data the app may need to access (“resources”).

        1. Services examples - signup, login, search, browse, view, add-to-cart, purchase/buy - “The data needed to perform any one of these can vary from the data needed for the others”

        2. Resources examples - product catalog, product inventory, user account information, marketing information

      3. Rule 9 - Design to Split Similar Things (Z Axis) - Set data up into unique and separated shards or swimlanes - Separate the db / servers by things like customer ID, name, geography, etc.

    3. Design to Scale Out Horizontally

      1. Rule 10 - Design Your Solution to Scale Out--Not Just Up

      2. Rule 11 - Use Commodity Systems (Goldfish Not Thoroughbreds)

      3. Rule 12 - Scale Out Your Data Centers

      4. Rule 13 - Design to Leverage the Cloud

    4. Use the Right Tools

      1. Rule 14 - Use Databases Appropriately

      2. Rule 15 - Avoid Using Firewalls - Firewalls cause issues with scalability and availability, so only use them when they significantly reduce risk or are required for compliance.

      3. Rule 16 - Actively Use Log Files

    5. Don’t Duplicate Your Work

      1. Rule 17 - Don’t Check Your Work - Don’t read data you just wrote unless it’s a regulatory / legal requirement, and if so, consider doing it asynchronously.

      2. Rule 18 - Stop Redirecting Traffic

      3. Rule 19 - Relax Temporal Constraints - For example, requiring items being available from when a viewer views them until they purchase them.

    6. Use Caching Aggressively

      1. Rule 20 - Use CDNs

      2. Rule 21 - Use ‘Expires’ Headers

      3. Rule 22 - Cache Ajax Calls

      4. Rule 23 - Use Page Caches

      5. Rule 24 - Use Application Caches

      6. Rule 25 - Use Object Caches - These are used for data that may be expensive to regenerate, like the result set of complex db queries.

      7. Rule 26 - Put Object Caches on Their Own Servers

        1. They have a diagram of multiple web servers connecting to an application server object cache, which connects to multiple app servers, which connect to a db object cache, which connects to db servers.

        2. They use the word “tier” to refer to these servers being another entire row in their diagram.

    7. Learn from Your Mistakes

      1. Rule 27 - Learn Aggressively - Use A/B testing, postmortems.

      2. Rule 28 - Don’t Rely on QA to Find Mistakes

      3. Rule 29 - Failing to Design for Rollback Is Designing for Failure - Practice rolling back in staging or QA.

      4. Rule 30 - Discuss and Learn from Failures - Do postmortems

    8. Database Rules

      1. Rule 31 - Be Aware of Costly Relationships

      2. Rule 32 - Use the Right Type of Database Lock

      3. Rule 33 - Don’t Use Multiphase Commits

      4. Rule 34 - Try Not to Use “Select for Update”

      5. Rule 35 - Don’t Select Everything

    9. Design for Fault Tolerance and Graceful Failure

      1. Rule 36 - Design Using Fault Isolative “Swimlanes”

      2. Rule 37 - Never Trust Single Points of Failure - Identify single instances on design diagrams and try to turn them into active/active configurations.

      3. Rule 38 - Avoid Putting Systems in Series

      4. Rule 39 - Ensure You Can Wire On and Off Functions

    10. Avoid or Distribute State

      1. Rule 40 - Strive for Statelessness

      2. Rule 41 - Maintain Sessions in the Browser When Possible - Use cookies, this lets the user request be served by any web server in the pool.

      3. Rule 42 - Use a Distributed Cache for States

    11. Asynchronous Communication and Message Buses

      1. Rule 43 - Communicate Asynchronously As Much As Possible

      2. Rule 44 - Make Sure Your Message Bus Can Scale

      3. Rule 45 - Avoid Overcrowding Your Message Bus

    12. Miscellaneous Rules

      1. Rule 46 - Be Wary of Scaling Through Third Parties - Own your destiny, reduce your total cost of ownership.

      2. Rule 47 - Purge, Archive, and Cost-Justify Storage

      3. Rule 48 - Remove Business Intelligence from Transaction Processing

      4. Rule 49 - Design Your Application to Be Monitored

      5. Rule 50 - Be Competent

    13. Rule Review and Prioritization

System Design Interview: An Insider’s Guide (Vol. 1) by Alex Xu

Foreword

  1. Scale from zero to millions of users

  2. Back-of-the-envelop estimation

  3. A framework for system design interviews

  4. Design a rate limiter

  5. Design consistent hashing

  6. Design a key-value store

  7. Design a unique ID generator in distributed systems

  8. Design a URL shortener

  9. Design a web crawler

  10. Design a notification system

  11. Design a news feed system

  12. Design a chat system

  13. Design a search autocomplete system

  14. Design YouTube

  15. Design Google Drive

  16. The Learning Continues

Afterword

System Design Primer

 

Articles / Forum posts

Topics

Examples of FAANG employees not knowing everything about system design

  • Motivation: The goal here isn’t to make fun of the people shown here, but to criticize the way interviews are done today, by showing that smart, productive people don’t have to come into the job already knowing everything about everything to do their job well; people can research issues/decisions as they come up, or read books to get onboarded. I want to be able to point to examples when I tell people that the degree of strictness used to evaluate candidates in these interviews is not helping select better engineers. There are other attributes that matter more than whether a person has managed to memorize everything about system design, data structures, and algorithms. Using these tests as a proxy for “determination” isn’t ideal, because a person can be determined to get the job offer but end up hating the actual work. That’s why you’ll see guys totally determined to become Navy SEALs and then drop out of the teams when it turns out they’re tasked with working as bodyguards and have to spend a lot of time away from their family, or you’ll see people totally determined to get into FAANG and then quit because of the lack of work/life balance. It would be better to just try to be very open with candidates about the work, maybe make it easy for them to get a taste of the lifestyle, and then just try to predict who is going to put in the time necessary to do a good job and isn’t going to quit.

  • Design Spotify (with ex-Google EM) - He was an engineering manager for 13 years at Google and then 2 years at Uber. He doesn’t know that a CDN generally acts as the DNS and sits in front of the load balancer; he has an arrow pointing from the CDN to the db and another from the CDN to the webservers (rather than the load balancer). He thinks the the webserver would actively dictate when songs should start getting cached instead of the CDN using a static configuration with a heuristic like Least-Frequently-Used to determine what to cache. He says the web server would send a message to the user to redirect them to the CDN.

    • 31:50 - He thinks Flask might be the name of a CDN. He can’t name CloudFlare as an example of CDN.

Resumes

  • Stuff to maybe include on a second page of your resume:

    • Locations you’re open to working - Countries, states, cities; fully in-office, hybrid, remote.

    • References - name, the job you worked with them at, their contact info, maybe something impressive about them to add weight to their recommendation.

    • Continuing education (books/courses you’ve read and summarized, with links to your summaries).

  • https://www.amazon.com/Google-R%C3%A9sum%C3%A9-Prepare-Microsoft-Company/dp/151138459X

    • Thoughts:

      • Despite the name of the book, from reading the chapter listing it seems to not really be focused on resumes.

      • From reading the chapter on the resume, while I can say it’s certainly better than nothing and could be helpful for a new college grad, it’s like a LinkedIn post in depth, and IMO doesn’t really earn the title of the book (“The Google Resume”).

    • Reviews

      • Negative

        • Content of the book is 90% of 'How to Get a Job' another million of books.

        • doesn't tell you anything that you already don't know by common knowledge

    • Summary / Notes

      • Not resume-related: Chapter 1 - Introduction, Chapter 2 - Advanced Preparation, Chapter 3 - Getting in the Door

      • Chapter 4 - Resumes

        • Six Hallmarks of a Powerful Resume

          • Accomplishment Oriented - Everyone wants an employee who “gets things done”.

          • Quantified Results - Listing accomplishments without the numbers may make them harder to evaluate. Quantifying results with dollars will make the strongest impact.

          • Well-Targeted - Tailor your resume to the position: mention experience relevant to what they’re looking for.

          • Universally Meaningful - Avoid technical jargon, acronyms; use plain English.

          • Clean, Professional, Concise

          • Well-Structured and Clear

        • The Structure

          • The Objective

          • Summary / Key Accomplishments

          • Work Experience

          • Projects

          • Education

          • Skills

          • Awards & Honors

          • What Not to Include

        • How Long is Too Long? - People with less than 5-10 years of experience should stick to one page.

        • Your Questions Answered

          • Q: Can I add an explanation for a gap in my resume to my resume?

            • A: Personal details can be TMI; the resume should focus on what you actually did.

          • Q: Should I not list my GPA if it’s low?

            • A: It’s normal not to list your GPA if it’s below 3.0. If it’s been improving every year and more-recent years are above 3.0, you can list the more-recent years.

          • Q: Can I use more than one page if I need it, even if I don’t have many years of work experience?

            • A: Yes but you risk frustrating/annoying the recruiter; just focus on the best, most-relevant accomplishments.

      • Chapter 5 - Deconstructing the Resume

        • Resume A: Bill Jobs

        • Resume B: Steve Gates

        • Resume C: Geena Roberts

        • Parting Words

        • Additional Resources

      • Chapter 6 - Cover Letters and References

      • Not resume-related: Chapter 7 - Interview Prep and Overview, Chapter 8 - Interview Questions, Chapter 9 - The Programming Interview, Chapter 10 - Getting into Gaming, Chapter 11 - The Offer, Chapter 12 - On the Job, Chapter 13 - Final Thoughts: Luck, Determination, and What You Can Do

      • Appendix A - 156 Action Words to Make Your Resume Jump

      • Appendix B - Answers to Behavioral Interview Questions

  • IGotAnOffer

    • Engineering resume review (with top ex-Google recruiter)

      • The format doesn’t matter.

      • The top (first page) is the prime real estate.

      • Page 2, page 3 are less valuable real estate, it’s stuff the recruiter might look at.

      • Aim to answer recruiters' questions as quickly as possible.

      • We want the resume to be easy-to-read.

      • You don’t need your full address.

      • Include your name, contact info, LinkedIn, locations you’re open to working (countries/cities, hybrid, remote).

      • Don’t have “fluff” that doesn’t really say anything: “keen to learn and extremely adaptable” is something anyone can say. Give specific achievements.

      • Recruiters may have requirements for a certain number of years of experience with certain tech, so make it easy for them to answer that question.

        • NW: maybe have a table of tech with years of experience on a second page?

      • “Numbers tell a better story”, “we want to see impact/result”

        • NW: How do they verify the numbers?

        • How many designs and iterations? How many implementations?

        • I built X product, X revenue, X traffic.

        • If you’re a great manager, what was your attrition rate over five years? (10% is incredible)

      • We want it to be the most-groundbreaking thing you did; only include your most impressive achievements.

      • If you feel like you haven’t done anything earth-shattering, just include whatever numbers you have available.

        • “It’s ok because I at least have an impact that you’re making, and that’s what we wanna communicate: our impact and why we’re valuable.”

        • NW: This guy really strikes me as someone who hasn’t thought deeply about how to do his job.

      • It’s OK to talk about what you did at your uni / internships.

      • I don’t want to have to dig for:

        • how many years of experience you have in the industry

        • how many years of experience you have in management

      • Let’s stick to what you did as a manager that was impactful. What things did your team deliver, what was the impact, what was the result, tell me through numbers.

        • It’s not helpful to list numbers for non-outstanding/unimpressive work/results, like: “Interviewed 250 people, hired X people, promoted X people”.

      • I want to see those two-to-three bullet points that make my jaw drop and say “Wow”.

      • Comments:

        • If [recruiters are] in the process it's prudent to write two resumes: one technical, for engineers, and one for HR. E.g. skip LinkedIn on the technical version and include links to code. It's stronger than easily-fluffed numbers on the resume.

          • This is exactly what I’m thinking. People can just make up numbers, but if that’s how nontechnical recruiters are choosing between resumes, then you’ll want to do what they’re looking for until you can get to someone who actually knows how to evaluate your “real” resume.

 

 

 

 

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