System Design / Cloud / Code
Real-world system design, backend patterns, and practical guides for engineers who build at scale.
Browse all postsA comprehensive guide to Elasticsearch for system design interviews — covering inverted indexes, cluster architecture (master/data/coordinating nodes), sharding and replication, the write path (translog, refresh, flush, merge), the read path (scatter-gather, BM25 scoring), mappings, analyzers, aggregations, scaling strategies, and production best practices for building search-heavy systems.
A comprehensive guide to Apache Kafka for system design interviews — covering its distributed commit log architecture, partitioning and replication, producer and consumer internals, consumer groups, exactly-once semantics, compacted topics, schema evolution, Connect and Streams APIs, and production patterns for building event-driven systems at scale.
A comprehensive guide to Redis — covering its single-threaded architecture, data structures (strings, hashes, sorted sets, streams), persistence (RDB/AOF), replication, clustering with hash slots, pub/sub, Lua scripting, caching patterns, rate limiting, distributed locks, and production best practices for running Redis at scale.
A comprehensive guide to API Gateways for system design interviews — covering request lifecycle, authentication and authorization, rate limiting algorithms (token bucket, sliding window), routing and load balancing, circuit breakers, response caching, request aggregation, TLS termination, protocol translation, and production patterns with Kong, NGINX, AWS API Gateway, and Envoy.
A comprehensive guide to designing production-grade REST APIs — covering resource naming, HTTP methods, pagination strategies, versioning approaches, error handling, rate limiting, and the communication problems REST solves.
A hands-on guide to data modelling — from entity-relationship design to normalization tradeoffs, indexing strategies, access-pattern-driven schemas, and safe schema evolution. Covers SQL and NoSQL with real-world examples.
A comprehensive guide to caching in distributed systems — covering caching layers (browser, CDN, application, database), invalidation strategies (cache-aside, write-through, write-behind), eviction policies (LRU, LFU, TTL), consistency pitfalls (thundering herd, hot keys, cache penetration), and production patterns with Redis.
How to build real-time systems that handle millions of concurrent connections — covering WebSockets, SSE, pub/sub fan-out, presence tracking, backpressure, and reconnection strategies with production code examples.
A practical guide to scaling write-heavy systems — covering sharding strategies, write-ahead logs, async queues, event sourcing, LSM trees, and batching with architecture diagrams and production code.
How to design systems that reliably execute tasks taking seconds to hours -- covering async job queues, progress tracking, retry strategies, sagas, checkpointing, dead letter queues, idempotency, and distributed task orchestration with production code examples.
How to design systems that efficiently upload, store, process, and serve large binary objects (images, videos, documents) at scale -- covering presigned URLs, chunked uploads, content pipelines, storage tiering, CDN delivery, and deduplication strategies.
A deep dive into the SAGA pattern for managing distributed transactions — covering orchestration vs choreography, compensation flows, state machines, and production-ready code examples in Node.js and Python.
A comprehensive guide to consistent hashing — how it works, why it matters for distributed systems, virtual nodes, replication strategies, and production implementations in Redis, Cassandra, and DynamoDB.
A practical guide to choosing between AWS Lambda and containers (ECS/Kubernetes) for production workloads. Covers cold starts, cost modeling, architecture patterns, and the real tradeoffs I've learned shipping both.
A practical guide to scaling read-heavy systems — covering caching strategies, read replicas, CQRS, materialized views, and CDN edge caching with architecture diagrams and real code examples.
A practical, end-to-end guide to building a Retrieval-Augmented Generation (RAG) pipeline using LangChain, OpenAI embeddings, and a vector database. Covers chunking strategies, embedding models, retrieval tuning, prompt engineering, and evaluation.
Six battle-tested prompt engineering patterns for production LLM systems — system prompt layering, chain-of-thought, few-shot examples, output guardrails, prompt chaining, and eval-driven iteration. With real code, real failure modes, and the stuff nobody puts in tutorials.
How to create Jenkins pipeline using Jenkinsfile, and schedule job on cron schedule, not on code commit.
How to run Automation on Different Environment (Dev/Stage/Prod) by Jenkinsfile and Overriding Environment Variables. Also taking credentials.
Learn how to Git Clone Another Repository from Jenkin Pipeline in Jenkinsfile.
Learn how to Fetch Multiple Credentials and Expose them in Environment using Jenkinsfile pipeline
How to Create UI Form Text fields, Drop-down and Run for Different Conditions or different environments
How to programmatically initialize a Log4j JSON logger in Java with custom global and dynamic key-value pairs in log output.
How to solve HTTP 504 Gateway Timeout errors in Kubernetes caused by Ingress proxy timeout settings.
A guide to creating an admin subdomain in Cloudflare with Nginx reverse proxy running in Docker with SSL.
Learn Why it is important to implement Exponential Backoff in Rabbitmq or In Event-Driven Systems
Learn how to implement exponential backoff in rabbitmq by using amqp in node.js
Learn how to send HTML Email from Python Code with Authentication at SMTP server end.
In Python code to send email, we encounter error as Sender Address Rejected Not Owned By User. See, how we to solve this error.
While connecting to MongoDB from Nodejs code with Mongoose, the number of connections keep on increasing on MongoDB. They needs to be explicitly closed.
Learn how to build docker image for Django project and run it on Apache with Python 3.9
How to enforce code quality in Python projects by using pylint scores and unittest coverage thresholds in the build process.
Example of a Jenkin Groovy Pipeline Script for Building Python Projects with Git Events and Push to Artifactory after building it.
