Category
distributed systems
43 articles across 12 sub-topics
Modernization Architecture Patterns: Strangler Fig, Anti-Corruption Layers, and Modular Monoliths
TLDR: Large-scale modernization usually fails when teams try to replace an entire legacy platform in one synchronized rewrite. The safer approach is to create seams, translate old contracts into stable new ones, and move traffic gradually with measur...
Integration Architecture Patterns: Orchestration, Choreography, Schema Contracts, and Idempotent Receivers
TLDR: Integration failures usually come from weak contracts, unsafe retries, and missing ownership rather than from choosing the wrong transport. Orchestration, choreography, schema contracts, and idempotent receivers are patterns for making cross-bo...
Event Sourcing Pattern: Auditability, Replay, and Evolution of Domain State
TLDR: Event sourcing pays off when regulatory audit history and replay are first-class requirements — but it demands strict schema evolution, a snapshot strategy, and a framework that owns aggregate lifecycle. Spring Boot + Axon Framework is the fast...
CQRS Pattern: Separating Write Models from Query Models at Scale
TLDR: CQRS works when read and write workloads diverge, but only with explicit freshness budgets and projection reliability. The hard part is not separating models — it is operating lag, replay, and rollback safely. An e-commerce platform's order se...
Cloud Architecture Patterns: Cells, Control Planes, Sidecars, and Queue-Based Load Leveling
TLDR: Cloud scale is not created by sprinkling managed services around a diagram. It comes from isolating failure domains, separating coordination from request serving, and smoothing bursty work before it overloads synchronous paths. TLDR: Cloud patt...
Bulkhead Pattern: Isolating Capacity to Protect Critical Workloads
TLDR: Bulkheads isolate capacity so one overloaded dependency or workload class cannot consume every thread, queue slot, or connection in the service. TLDR: Use bulkheads when different workloads do not deserve equal blast radius. The practical goal ...
System Design HLD Example: URL Shortener (TinyURL and Bitly)
TLDR: Design a URL shortener like TinyURL or Bitly. This article now follows your system design interview template flow: use cases, requirements, estimations, design goals, HLD, and design deep dive. TLDR: A URL shortener converts long links into com...
System Design HLD Example: Search Autocomplete
TLDR: Design search autocomplete for a large-scale product. This article follows the system design interview template flow: use cases, requirements, estimations, design goals, HLD, and design deep dive. TLDR: Sub-10ms prefix lookups via Redis sorted ...
System Design HLD Example: Rate Limiter (Global API Protection)
TLDR: Design a distributed rate limiter for public APIs. This article covers the full solution: per-user/per-key policies, endpoint quotas, burst handling with token bucket, atomic Redis Lua enforcement, and retry metadata. TLDR: A distributed rate l...
System Design HLD Example: Payment Processing Platform
TLDR: Design a payment processing system for online checkout. This article covers idempotent authorization, two-phase authorize-capture, double-entry ledger writes, webhook delivery with retry, and nightly reconciliation — with concrete schema, Redis...
System Design HLD Example: Notification Service (Email, SMS, Push)
TLDR: Design a notification service for email, SMS, and push channels. This article covers the full solution: data model, per-channel queue isolation, deduplication, retry with dead-letter queue, provider failover, and delivery tracking. TLDR: A noti...
System Design HLD Example: News Feed (Home Timeline)
TLDR: Design a news feed for a social platform. This article now follows your system design interview template flow: use cases, requirements, estimations, design goals, HLD, and design deep dive. TLDR: A news feed system builds personalized timelines...
System Design HLD Example: File Storage and Sync (Google Drive and Dropbox)
TLDR: Design a cloud file storage and sync system like Dropbox. This article now follows your system design interview template flow: use cases, requirements, estimations, design goals, HLD, and design deep dive. TLDR: File storage and sync systems se...
System Design HLD Example: Distributed Cache Platform
TLDR: A distributed cache reduces read latency and source-of-truth load while introducing consistency trade-offs. This walkthrough solves the full problem: consistent hashing for online scaling, per-namespace eviction policies, synchronous invalidati...
System Design HLD Example: Chat and Messaging Platform
TLDR: Design a chat application like WhatsApp or Slack direct messages. This article covers the full solution: data model, per-feature deep dives, WebSocket fan-out, delivery receipts, presence, reconnect sync, and multi-device behavior. TLDR: A chat...
System Design HLD Example: API Gateway for Microservices
TLDR: Design an API gateway for a microservice platform. This article now follows your system design interview template flow: use cases, requirements, estimations, design goals, HLD, and design deep dive. TLDR: An API gateway centralizes ingress conc...
System Design Roadmap: A Complete Learning Path from Basics to Advanced Architecture
TLDR: This roadmap organizes every system-design-tagged post in this repository into learning groups and a recommended order. It is designed for interview prep and practical architecture thinking, from fundamentals to scaling, reliability, and implem...
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...
Understanding Consistency Patterns: An In-Depth Analysis
TLDR TLDR: Consistency is about whether all nodes in a distributed system show the same data at the same time. Strong consistency gives correctness but costs latency. Eventual consistency gives speed but requires tolerance for briefly stale reads. C...
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 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 ...
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...
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: Caching and Asynchronism
TLDR: Caching stores hot data in fast RAM so you skip slow database round-trips. Asynchronism moves slow tasks (email, video processing) off the critical path via message queues. Together, they turn a blocking, slow system into a responsive, scalable...
How Kafka Works: The Log That Never Forgets
TLDR: Kafka is a distributed event store. Unlike a traditional queue (RabbitMQ) where messages disappear after reading, Kafka stores them in a persistent Log. This allows multiple consumers to read the same data at their own pace, replay history, and...
Consistent Hashing: Scaling Without Chaos
TLDR: Standard hashing (key % N) breaks when $N$ changes — adding or removing a server reshuffles almost all keys. Consistent Hashing maps both servers and keys onto a ring (0–360°). When a server is added, only its immediate neighbors' keys move, mi...
A Guide to Raft, Paxos, and Consensus Algorithms
TLDR TLDR: Consensus algorithms allow a cluster of computers to agree on a single value (e.g., "Who is the leader?"). Paxos is the academic standard — correct but notoriously hard to understand. Raft is the practical standard — designed for understa...