Distributed Systems & Streaming
Backend Architecture • Event Driven Systems • Kafka
System Motivation
[cite_start]Modern systems live or die by their data flow. While managed solutions like Kafka or RabbitMQ exist, true system design mastery requires understanding the mechanics under the hood: partitioning, coordination, fault tolerance, and backpressure[cite: 16].
System Topology
ARCHITECTURE SPLIT: INFRASTRUCTURE vs APPLICATION LAYERS
1. Message Broker (mini-Kafka)
[cite_start]A simplified Kafka-like system implemented to reason about distributed log internals[cite: 18].
Topic & Partition Management
Sequential vs. Concurrent I/O
Broker Coordination
2. Real-Time Streaming Pipeline
[cite_start]A practical alerting application demonstrating how infrastructure supports real-time decisions[cite: 19].
Async Event Processing
Condition-based Alerting
Persistent Storage
Engineering Kernels
Event-Driven Architecture
Producers and consumers are strictly decoupled via topics. [cite_start]This separation allows independent scaling of ingestion and processing layers[cite: 16].
Ordering & Consistency
[cite_start]Investigated how partition-level ordering guarantees function and the trade-offs involved in maintaining total vs. causal ordering[cite: 16].
Fault Awareness
The system assumes failure is inevitable. [cite_start]Components are designed to recover cleanly from broker crashes or network partitions without data corruption[cite: 16].
Backpressure & Lag
[cite_start]Evaluated system stability when producer ingestion rates exceed consumer processing capabilities, ensuring the system degrades gracefully[cite: 16].
PHILOSOPHY_LOG
PHILOSOPHY_LOG
This implementation prioritized conceptual clarity and correctness over full protocol compatibility or raw throughput. The goal was observability: ensuring system behavior under load could be reasoned about and debugged effectively.
"An exploration of distributed systems fundamentals through hands-on implementation and applied streaming pipelines."