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.
Lesson 6: Stream Processing with Kafka — “Turn Log Floods Into Live Intelligence”
The lesson walks readers through building a Kafka‑based streaming pipeline that can handle massive event rates, such as Uber’s million‑ride‑events‑per‑minute workload. It covers setting up a partitioned Kafka cluster, creating idempotent producers, and configuring a three‑worker consumer group that guarantees exactly‑once payment processing using SQLite deduplication. The tutorial also demonstrates a compacted user‑state topic and a live WebSocket dashboard for real‑time metrics. By the end, learners have a production‑grade streaming stack ready for real‑time analytics.
Day 167: Automated Root Cause Analysis - Finding the Needle in the Haystack
The post outlines a hands‑on project to build an intelligent root‑cause analysis (RCA) platform that automatically traces incidents across distributed microservices. By ingesting massive log streams, constructing dependency graphs, and detecting temporal event patterns, the system can pinpoint the exact...
Day 61: Circuit Breakers for Handling Component Failures
The post details the integration of circuit breakers into a multi‑region log processing pipeline, wrapping outbound calls to Kafka, Redis, and PostgreSQL. It introduces a state‑machine‑driven failure detector with configurable thresholds, timeouts, and half‑open probing. Fallback mechanisms ensure continuous ingestion...
Day 60: Multi-Region Replication for Log Data
The lesson walks through building a multi‑region log pipeline using Kafka MirrorMaker 2 to replicate events across two simulated regions. It implements an active‑active topology, conflict‑resolution via idempotency keys, region‑aware API routing, and end‑to‑end monitoring of lag, throughput and divergence. The...
Day 59: Implement Active-Passive Failover for Critical Components
The post details building an active‑passive failover system for Kafka consumers, featuring automatic leader election, heartbeat‑based health monitoring, and zero‑data‑loss state migration. It demonstrates sub‑second recovery times and contrasts active‑passive with more complex active‑active designs. By moving from 99.9% to...
Day 164: Building Change Impact Analysis - Predicting the Ripple Effects
The post walks readers through building a change impact analysis system for distributed architectures, adding predictive intelligence to a live service‑dependency graph. It outlines components such as a change analyzer, graph‑traversal engine, risk‑scoring algorithm, visualization dashboard, and mitigation recommendations. Real‑world...
Start Here — How to Use SDCourse
The SDCourse "Start Here" guide outlines a 254‑lesson curriculum that walks learners through building LogStream, a production‑grade distributed log processing platform used by firms like Cloudflare, Datadog, and Stripe. The program is split into six modules covering ingestion, Kafka messaging,...
Day 163: Build Service Dependency Mapping
The post outlines building an automated service‑dependency mapping system that parses logs to generate a real‑time graph of microservice interactions. It details four core components—a log parser, graph builder, visualization dashboard, and health‑impact analyzer. By weighting edges with call frequency...
Week 3 Integrated Logging Pipeline (MVP): From Serialization to Production-Style Observability
The post walks developers through building an end‑to‑end logging pipeline MVP that mirrors a production observability path: ingestion, normalization, optional validation, enrichment, and output. It reuses Week 3 course lessons—JSON logs (Day 15), canonical normalization (Day 18), and context enrichment (Day 21)—and stitches them...
Build a Distributed Logging Pipeline(TCP, UDP, Batching, Compression, TLS) – Week 2 Integration Project
The blog post showcases a merged repository that consolidates days 8‑14 of a distributed logging course into a runnable demo platform. It includes producers that ship logs, receivers that persist them, and a dashboard for health metrics, all configurable with...
Day 57: Full-Text Search with Relevance Scoring
The post outlines how Elasticsearch powers a distributed full‑text search layer for massive log streams, leveraging the BM25 ranking algorithm with custom scoring functions. It supports multi‑field queries across structured and unstructured log data and exposes a real‑time API that...
Day 162: Log-Based Network Traffic Analysis
The post outlines how to build a real‑time network security monitoring system that parses firewall, proxy and packet‑capture logs to detect threats, map traffic patterns, and flag anomalies. It emphasizes parsing logs instantly, scoring suspicious activity, visualizing flows, and issuing...
Day 56: Real-Time Indexing of Incoming Logs
A near‑real‑time indexing pipeline now indexes incoming logs within 100 ms, using a distributed inverted index optimized with LSM‑trees for high write throughput. An index coordination layer manages shard distribution and replication across nodes, while a low‑latency query API provides millisecond‑scale...
Curriculum Index : Hands On System Design with "Distributed Systems Implementation - 254-Lesson’s Curriculum"
The post promotes a 254‑lesson curriculum that walks learners through building a complete distributed log processing system from scratch. It outlines daily, hands‑on tasks—from Docker environment setup to TLS‑encrypted network transport, Kafka streaming, and multi‑node storage clusters—culminating in dashboards and...
Day 53: Distributed Indexing Across Multiple Nodes
The post outlines a distributed indexing architecture that spreads a partitioned search index across three or more nodes using consistent hashing, a scatter‑gather query coordinator, and a primary‑replica replication layer. It highlights the limitations of single‑node indexes—RAM exhaustion, I/O‑bound write...