Category
interview prep
35 articles across 10 sub-topics
Microservices Architecture: Decomposition, Communication, and Trade-offs
TLDR: Microservices let teams deploy and scale services independently — but every service boundary you draw costs you a network hop, a consistency challenge, and an operational burden. The architecture pays off only when your team and traffic scale h...
Distributed Transactions: 2PC, Saga, and XA Explained
TLDR: Distributed transactions require you to choose a consistency model before choosing a protocol. 2PC and XA give atomic all-or-nothing commits but block all participants on coordinator failure. Saga gives eventual consistency with explicit compen...
System Design HLD Example: Payment Processing Platform
TLDR: Payment systems optimize for correctness first, then throughput. This guide covers idempotency, double-entry ledgers, and reconciliation. Stripe processes over 250 million API requests per day, and every single payment must be idempotent: a us...
System Design HLD Example: Notification Service (Email, SMS, Push)
TLDR: A notification platform routes events to per-channel Kafka queues, deduplicates with Redis, and tracks delivery via webhooks — ensuring that critical alerts like password resets never get blocked by marketing batches. Uber sends over 1 million...
System Design HLD Example: File Storage and Sync (Dropbox and Google Drive)
TLDR: Cloud sync systems separate immutable blob storage (S3) from atomic metadata operations (PostgreSQL), using chunk-level deduplication to optimize storage costs and delta-sync events to minimize bandwidth. Dropbox serves 700 million registered ...
System Design HLD Example: Distributed Cache Platform
TLDR: Distributed caches trade strict consistency for sub-millisecond read latency, using consistent hashing to scale horizontally without causing database-shattering "cache stampedes" during cluster rebalancing. Instagram's primary database once se...
System Design Requirements and Constraints: Ask Better Questions Before You Draw
TLDR: In system design interviews, weak answers fail early because requirements are fuzzy. Strong answers start by turning vague prompts into explicit functional scope, measurable non-functional targets, and clear trade-off boundaries before any arch...
Little's Law: The Secret Formula for System Performance
TLDR: Little's Law ($L = \lambda W$) connects three metrics every system designer measures: $L$ = concurrent requests in flight, $\lambda$ = throughput (RPS), $W$ = average response time. If latency spikes, your concurrency requirement explodes with ...
The 8 Fallacies of Distributed Systems
TLDR TLDR: In 1994, L. Peter Deutsch at Sun Microsystems listed 8 assumptions that developers make about distributed systems — all of which are false. Believing them leads to hard-to-reproduce bugs, timeout cascades, and security holes. Knowing them...
Data Warehouse vs Data Lake vs Data Lakehouse: Which One to Choose?
TLDR: Warehouse = structured, clean data for BI and SQL dashboards (Snowflake, BigQuery). Lake = raw, messy data for ML and data science (S3, HDFS). Lakehouse = open table formats (Delta Lake, Iceberg) that bring SQL performance to raw storage — the ...
System Design Service Discovery and Health Checks: Routing Traffic to Healthy Instances
TLDR: Service discovery is how clients find the right service instance at runtime, and health checks are how systems decide whether an instance should receive traffic. Together, they turn dynamic infrastructure from guesswork into deterministic routi...
System Design Observability, SLOs, and Incident Response: Operating Systems You Can Trust
TLDR: Observability is how you understand system behavior from telemetry, SLOs are explicit reliability targets, and incident response is the execution model when those targets are at risk. Together, they convert operational chaos into measurable, re...
System Design Message Queues and Event-Driven Architecture: Building Reliable Asynchronous Systems
TLDR: Message queues and event-driven architecture let services communicate asynchronously, absorb bursty traffic, and isolate failures. The core design challenge is not adding a queue — it is defining delivery semantics, retry behavior, and idempote...
System Design Multi-Region Deployment: Latency, Failover, and Consistency Across Regions
TLDR: Multi-region deployment means running the same system across more than one geographic region so users get lower latency and the business can survive a regional outage. The design challenge is no longer just scaling compute. It is coordinating r...
System Design Interview Basics: A Beginner-Friendly Framework for Clear Answers
TLDR: System design interviews are not about inventing a perfect architecture on the spot. They are about showing a calm, repeatable process: clarify requirements, estimate scale, sketch a simple design, explain trade-offs, and improve it when constr...
System Design Databases: SQL vs NoSQL and Scaling
TLDR: SQL gives you ACID guarantees and powerful relational queries; NoSQL gives you horizontal scale and flexible schemas. The real decision is not "which is better" — it is "which trade-offs align with your workload." Understanding replication, sha...
System Design Protocols: REST, RPC, and TCP/UDP
TLDR: 🎯 Use REST (HTTP + JSON) for public, browser-facing APIs where interoperability matters. Choose gRPC (HTTP/2 + Protobuf) for internal microservice communication when latency counts. Under the hood, TCP guarantees reliable ordered delivery; UDP...
System Design Networking: DNS, CDNs, and Load Balancers
TLDR: When you hit a URL, DNS translates the name to an IP, CDNs serve static assets from the edge nearest to you, and Load Balancers spread traffic across many servers so no single machine becomes a bottleneck. These three layers are the traffic con...
System Design Core Concepts: Scalability, CAP, and Consistency
TLDR: 🚀 Scalability, the CAP Theorem, and consistency models are the three concepts that determine whether a distributed system can grow, stay reliable, and deliver correct results. Get these three right and you can reason about any system design qu...
The Ultimate Guide to Acing the System Design Interview
TLDR: System Design interviews are collaborative whiteboard sessions, not trick-question coding tests. Follow the framework — Requirements → Estimations → API → Data Model → High-Level Architecture → Deep-Dive — and you turn vague product ideas into ...
Exploring Different Types of Binary Trees
TLDR: A Binary Tree has at most 2 children per node, but the shape of the tree determines performance. A Full tree has 0 or 2 children. A Complete tree fills left-to-right. A Perfect tree is a symmetric triangle. A Degenerate tree becomes a linked li...
Exploring Backtracking Techniques in Data Structures
TLDR: Backtracking is "Recursion with Undo." You try a path, explore it deeply, and if it fails, you undo your last decision and try the next option. It explores the full search space but prunes invalid branches early, making it far more efficient th...
The Ultimate Data Structures Cheat Sheet
TLDR: Data structures are tools. Picking the right one depends on what operation you do most: lookup, insert, delete, ordered traversal, top-k, prefix search, or graph navigation. Start from operation frequency, not from habit. 📖 Why Structure Cho...
Tree Data Structure Explained: Concepts, Implementation, and Interview Guide
TLDR: Trees are hierarchical data structures used everywhere — file systems, HTML DOM, databases, and search algorithms. Understanding Binary Trees, BSTs, and Heaps gives you efficient $O(\log N)$ search, insertion, and deletion — and helps you ace a...
Mastering Binary Tree Traversal: A Beginner's Guide
TLDR: Binary tree traversal is about visiting every node in a controlled order. Learn pre-order, in-order, post-order, and level-order, and you can solve many interview and production problems cleanly. 📖 Four Ways to Walk a Tree — and Why the Orde...
Key Terms in Distributed Systems: The Definitive Glossary
TLDR: Distributed systems vocabulary is precise for a reason. Mixing up read skew and write skew costs you an interview. Confusing Snapshot Isolation with Serializable costs you a production outage. This glossary organises every critical term into co...
System Design Sharding Strategy: Choosing Keys, Avoiding Hot Spots, and Resharding Safely
TLDR: Sharding means splitting one logical dataset across multiple physical databases so no single node carries all the data and traffic. The hard part is not adding more nodes. The hard part is choosing a shard key that keeps data balanced and queri...
System Design Replication and Failover: Keep Services Alive When a Primary Dies
TLDR: Replication means keeping multiple copies of your data so the system can survive machine, process, or availability-zone failures. Failover is the coordinated act of promoting a healthy replica, rerouting traffic, and recovering without corrupti...
Elasticsearch vs Time-Series DB: Key Differences Explained
TLDR: Elasticsearch is built for search — full-text log queries, fuzzy matching, and relevance ranking via an inverted index. InfluxDB and Prometheus are built for metrics — numeric time series with aggressive compression. Picking the wrong one waste...
Real-Time Communication: WebSockets, SSE, and Long Polling Explained
TLDR: 🔌 WebSockets = bidirectional persistent channel — use for chat, gaming, collaborative editing. SSE = one-way server push over HTTP with built-in reconnect — use for AI streaming, live logs, notifications. Long Polling = held HTTP requests — th...
System Design API Design for Interviews: Contracts, Idempotency, and Pagination
TLDR: In system design interviews, API design is not a list of HTTP verbs. It is a contract strategy: clear resource boundaries, stable request and response shapes, pagination, idempotency, error semantics, and versioning decisions that survive scale...
System Design: Complete Guide to Caching — Patterns, Eviction, and Distributed Strategies
TLDR: Caching is the single highest-leverage performance tool in distributed systems. This guide covers every read/write pattern (Cache-Aside through Refresh-Ahead), every eviction policy (LRU through ARC), cache invalidation pitfalls, thundering her...