Trung Nguyen

Imagine you just joined Google as an SWE. It’s your first week, and you’re overthinking everything. You’re surrounded by world-class engineers, new systems, and an overwhelming urge not to mess up. The pressure to prove yourself is real, and so is the anxiety…so what should you do? Over the years, I’ve moved across orgs, teams, and sometimes, entire companies. Here’s exactly what I did (and still do) to ramp up faster and enjoy the process without letting anxiety win → Don’t let yourself stay stuck. If you’re blocked for more than 30 minutes, ask for help. Share what you’ve tried, show initiative, and intent. → Read way more code than you’re assigned. Dive into related modules, historic PRs, and old design docs. The context you gain helps you spot patterns and avoid mistakes. → Ask about the story behind the system. Find out why things are built a certain way, what’s failed before, and what’s on the roadmap. Understanding the “why” beats memorizing the “what.” → Volunteer for unglamorous work. Take ownership of tests, docs, or nagging integration bugs. This is where you quietly learn the most and build your internal network. → Offer help before you’re an expert. Chime in when you see questions, review PRs, or share a script. Even small contributions get you noticed for the right reasons. → Connect with skip-levels and cross-team leads. Set up short intros to learn about the broader vision and team priorities. You’ll spot opportunities to add value beyond your immediate tasks. → Document what you learn as you go. Share notes, update READMEs, or post quick how-tos in team channels. Your future self and your teammates will thank you. → Shadow someone during incidents or launches. Sit in on debugging sessions or war rooms, even if you’re just observing. You’ll see how senior folks think, react, and communicate under pressure. → Keep a running list of “how did that break?” moments. Every time you see a system fail or a hotfix go live, dig in. Reverse-engineering past problems is the fastest way to understand the architecture. → Show up with curiosity and intent, every day. Don’t just tick off JIRA tickets. Look for the bigger picture. People notice when you genuinely want to learn and contribute, not just “do your job.” Some orgs assign mentors, some don’t. Either way, these habits put you in the driver’s seat. Ramping up is less about brilliance, more about consistent intent. And the ones who learn this early end up leading teams later.

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I approved code with O(n x m) complexity over O(n). My team thought I'd lost it... Here's what happened: A junior engineer optimized our API from O(n x m) to O(n). Textbook improvement, right? The optimization: → Old: Loop through n items, check against m items locally → New: Loop through n items, make 1 network call to get certain value. The math looked beautiful on paper. For n = 1000, m = 1000: → Old: 1,000,000 operations → New: 1000 operations This looks thousand times faster but.. That "single network call": → 15ms average latency  → 99th percentile: 45ms  → Payload size for 1000 items: 0.5MB   → If retry needed: 200ms+ Meanwhile, the O(n x m) approach:  → n = 100, m = 100: 0.8ms  → n = 1000, m = 1000: 12ms  → n = 10000, m = 10000: 180ms The dev was shocked: "But we're doing 100 MILLION operations for 10k items!" Here's what Big O doesn't tell you: Modern CPUs are FAST: A 3GHz processor = 3 billion operations/second 100 million operations = 33ms Networks are SLOW and inconsistent and if a service is down it adds retries. The rule I follow now: 1ms of network latency = 1 million CPU operations We continued using the O(n x m) solution It's been running for 2 years. Zero incidents. Have you ever chosen "worse" complexity for better reliability?

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If you want to become better at system design rounds, definitely read these 10 case studies by Neo Kim. I am 100% sure, they will make you smarter for your next interview.  1/ How Meta Achieves 99.99999999% Cache Consistency: → https://lnkd.in/e88kUZAm 2/ How Uber Computes ETA at Half a Million Requests per Second: → https://lnkd.in/eVKV2ePC 3/ How PayPal Was Able to Support a Billion Transactions per Day With Only 8 Virtual Machines: → https://lnkd.in/eqcb7MpP 4/ How YouTube Was Able to Support 2.49 Billion Users With MySQL: → https://lnkd.in/efmJw4Dx 5/ How WhatsApp Was Able to Support 50 Billion Messages a Day With Only 32 Engineers: → https://lnkd.in/gRqBgXfc 6/ How Uber Finds Nearby Drivers at 1 Million Requests per Second: → https://lnkd.in/eeqH9Hjh 7/ How Stripe Prevents Double Payment Using Idempotent API: → https://lnkd.in/erMkqwq4 8/ How Google Ads Was Able to Support 4.77 Billion Users With a SQL Database: → https://lnkd.in/efnSvwJp 9/ How Amazon S3 Achieves 99.999999999% Durability: → https://lnkd.in/eutGiK35 10/ How Slack Works: → https://lnkd.in/eATMDjrK

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Interviewer: Explain where Single Sign-On (SSO) fits in modern backend architecture. Is it just an authentication detail, or does it impact broader system design? Break down how SSO actually works behind the scenes. Me: SSO is fundamentally a system design topic that is implemented primarily through backend services. [ Please Note: In a system design interview, you'd be expected to diagram the interaction between the user, browser, application, and identity provider, explaining the flow of authentication requests and tokens. But the system design is realized through backend code. The backend is responsible for the heavy lifting. ] Here’s how it works: 1/ User Initiates Login ◦ The user tries to access an internal app or third-party SaaS. ◦ Instead of authenticating locally, the app (Service X) redirects the user to a central Identity Provider (IdP). ◦ This redirect includes a request for identity verification and relevant parameters. 2/ Authentication with the Identity Provider ◦ The IdP presents a login page, where the user provides their credentials (password, OTP, or biometric). ◦ Credentials are checked against the IdP’s secure store, no passwords ever touch the original app. ◦ On successful login, the IdP establishes an authenticated session. 3/ Issuing the Security Token ◦ The IdP generates a cryptographically signed token (e.g., JWT, SAML assertion) containing user identity and authorization details. ◦ This token is sent back to Service X, which verifies the signature using the IdP’s public key. ◦ If the token is valid, the app grants access, no need to store or sync credentials locally. 4/ Access Across Multiple Services ◦ The same authentication token (or a new one, via IdP session) can be used to access additional apps, think Service Y, Z, or internal dashboards. ◦ Users move seamlessly between apps without logging in again (as long as the session/token remains valid). ◦ All connected services defer authentication to the IdP, ensuring consistent enforcement of security policies. Why SSO Makes Sense ◦ Seamless User Experience: One login, access everywhere, no more password fatigue or lockouts. ◦ Centralized Security: MFA, password rotation, and policy enforcement are all handled centrally. ◦ Operational Scale: Simplifies onboarding, offboarding, and managing access for large teams and enterprises. But, Don’t Miss These Risks ◦ Token Lifespan: Tokens should be short-lived and securely transmitted; a leaked token is a risk to all services. ◦ IdP Downtime: If the IdP is unavailable, every dependent service is locked out. ◦ Protocol Pitfalls: SAML, OAuth2, and OIDC require precise configuration; one misstep can open security holes.

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The first SDE-2 absolutely nailed the system design round. The question was: Design a live streaming system. The second SDE-2 was asked to Design Google Drive. He did well, spoke about trade-offs, but missed one important detail: chunking the file, especially for large uploads. Yet, the second engineer got the job. These are from real interviews I took around 4 months ago. You might ask: Why? Only one reason, the second one nailed the behavioral questions like a boss. The question was about working on a project with tight timelines. The first SDE-2 (subtly) blamed the external team’s API for delays, which led to tight project deadlines. The second one, on the other hand, shared a story about how he helped his manager prioritize requirements to navigate the crunch. That’s how important behavioral interviews are. Yet, 90% of SDEs leave them to luck. If everything else is equal: - DSA (both solved the coding problems), - LLD (designed with solid object-oriented principles), - HLD (covered all the P0 functional and non-functional requirements) then your behavioral answers become the deciding factor. Yes, the second engineer missed the file chunking detail. But it wasn’t a critical red flag. He covered all other parts well and crushed the behavioral round. As long as you're working with humans (no matter how many), you’ll be judged on your behavioral skills. Call them soft skills, leadership skills, whatever you like, but you should prepare them well in advance. You can start preparing for behavioural interviews right away, with these 5 MUST read behavioural interview posts I wrote. (tip: save this post for later, as it may take some time to complete all of them) [1] How to answer any behavioural interview question https://lnkd.in/gH3N8b5q [2] 4 step guide for behavioural interview preparation in 3 hours https://lnkd.in/gccxG3MP [3] How to create 10+ stories for your past projects https://lnkd.in/g4nv_jqU [4] How to answer a failure story? https://lnkd.in/gtN-Dfuv [5] 10 behavioural interview mistakes to avoid at all cost https://lnkd.in/gpupvYVs If you’re preparing for mid-senior SDE or EM roles and need dedicated help with behavioral interview prep, DM me COACH.

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If I had a system design interview tomorrow at Google, this is exactly how I’d approach it. (This framework helped me crack 3 FAANG+ companies in the past, including Amazon.) Most engineers fail system design interviews not because they lack knowledge but because they lack structure. You don’t need to memorize 100 architectures. You need a structured and easy-to-apply approach that works every time. Here’s how I break it down: 1/ Clarify the Problem Before Writing Anything - System design interviews aren’t about throwing buzzwords—they’re about trade-offs. - Start with scoping → Are we designing just one feature or the entire system? - Ask constraints upfront → How many users? Read/write ratio? Latency requirements? - Define success criteria → What matters most? Scalability? Cost? Low-latency? Most candidates assume things and jump into solutions. I make sure I know what we’re solving before I even start. 2/ Define Functional & Non-Functional Requirements Clearly - Functional: What features does the system need? - Non-functional: What are the performance expectations? - What’s the biggest technical challenge? (This helps guide the discussion.) Example: If we’re designing YouTube, is the focus on video uploads, recommendations, or live streaming? Each has a different set of constraints. 3/ Estimate the Scale & Plan Capacity Like an Engineer - Users per second? Requests per second? - Storage needs? If we store 10MB per user and have 100M users, what does that mean? - Throughput? Can a single database handle the load, or do we need sharding? Most candidates throw random numbers. I do quick, back-of-the-envelope calculations to validate my assumptions. 4/ Break the System into Core Components (High-Level Design) - Define the major building blocks → API Gateway, Load Balancer, Service Layers, Databases. - Don’t overcomplicate. Simple and scalable always wins. - Clearly define the interactions between services. If I’m designing a messaging app, I break it down into: — User Service (auth, profiles) — Messaging Service (storing chats) — Notification Service (real-time updates) — Media Storage (for images, videos) Each has different constraints, so I build around what’s most important. Continued Here: https://lnkd.in/eJ2T966N

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Netflix's processes 75,000 count requests per sec globally System Design of Distributed Counter Abstraction Functional Requirements - Build Distributed Counter to track millions of user interactions - Monitor how often specific features or experiences are shown to users The use cases can be classified into two 1. Best-Effort 1. The count doesn’t have to be very accurate or durable 2. Read current count at low latencies 3. Keep infrastructure costs to a minimum 2. Eventually Consistent 1. The count needs to be accurate and durable 2. Slight delay in accuracy is tolerable 3. Slightly higher infrastructure cost is fine Non Functional Requirements 1. Scalable 2. Low latency 3. High throughput 4. High availability HLD - Counter Abstraction to allow users to choose between different counting modes, such as Best-Effort or Eventually Consistent - After selecting a mode, users can interact with APIs - Counters are organised into separate namespaces set up by users. - Each namespace can be configured with different parameters, such as Type of Counter, Time-To-Live (TTL) and Counter Cardinality 🤔 over to you, what API should we support as part of LLD? Let me know in the comments! 👇 --- You've got System Design skills but showcasing them in 60 minutes interview is a completely different challenge for e.g. where to start? How much time to spend on HLD vs LLD etc Let me share a FREE 7-day email course that provides a step-by-step framework for acing your 60-minute System Design interview 📣 Join 1700+ subscribers and sign up for the course to learn time allocation to demonstrate full range of design skills (link in comments 👇)

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