Hands On System Design Course - Code Everyday
Build a complete, production-ready distributed log processing system from scratch. Each day features practical, hands-on tasks with concrete outputs that incrementally develop your expertise in distributed systems architecture, scalable data processing.
Day 44: Real-Time Monitoring Dashboard with Kafka Streams
The post walks through building a production‑grade real‑time monitoring dashboard that ingests over 40,000 events per second using Kafka Streams. It shows how windowed aggregations, percentile calculations, and anomaly detection run on RocksDB‑backed state stores with exactly‑once guarantees. The stream processor exposes interactive query endpoints, feeding a WebSocket‑driven UI while Grafana tracks processor health. Fault‑tolerant state recovery and back‑pressure handling are demonstrated to keep latency sub‑second even during crashes or rebalances.
Day 149: Orchestrating Your Log Processing Empire with Kubernetes
The post walks readers through turning a complex, distributed log‑processing stack—collectors, RabbitMQ, query engines, and storage—into a single Kubernetes deployment. By providing complete manifests, it shows how to launch the entire ecosystem with one command, while Kubernetes handles health checks,...
Day 43: Implement Log Compaction for State Management
The post outlines a production‑grade state management layer built on Kafka log‑compacted topics, featuring a keyed state producer, a consumer that materializes current snapshots, and a Redis‑backed query API. By retaining only the latest record per entity key, log compaction...
Day 148: Natural Language Queries with NLP - Ask Your Logs Anything
The blog announces a natural language query engine for log platforms, letting users ask questions like “show me errors from payment service in the last hour” and receive instant results. By converting conversational intent into optimized SQL, the system removes...
Day 42: Exactly-Once Processing Semantics in Distributed Log Systems
The post details a new Kafka‑based log pipeline that guarantees exactly‑once processing, eliminating duplicate handling even during failures. It combines idempotent producers, transactional consumer commits, a Redis‑backed deduplication layer, and a state‑reconciliation service to create an end‑to‑end exactly‑once flow. The...
Day 146: Time Series Database Integration - Turning Logs Into Queryable Metrics
Today's post highlights the shift from raw log files to queryable metrics using time‑series databases. It explains why traditional relational databases falter with high‑write, append‑only workloads and showcases InfluxDB and TimescaleDB as purpose‑built solutions. The article illustrates how these databases...