Writing a technical blog post is like solving a system-design problem: clarity and structure matter more than storytelling or formalities. Engineers read to understand a problem, see a solution, and apply it themselves.
This post shows a practical, engineering-grade structure that makes blog posts logical, concise, and reusable.
Engineering Blog Post Framework
Structure Template Title: [Action verb] + [Specific outcome] + [Context/Technology]
1. Problem Statement (10%)
- What problem exists
- Why it matters
- Who it affects
2. Context/Background (15%)
- Prior approaches
- Constraints faced
- Why existing solutions fell short
3. Solution (50%)
- Architecture/design decisions
- Implementation details with code
- Trade-offs considered
4. Results/Evaluation (15%)
- Metrics/benchmarks
- Before/after comparisons
- Lessons learned
5. Conclusion (10%)
- Key takeaways
- Future work
Example
Title
Reducing Payment Failures from 3% to 0.01% with Event-Driven Architecture
Problem Statement
Our payment processing service was dropping 3% of transactions during peak hours (Black Friday, flash sales). Each dropped transaction cost us approximately €50 in lost revenue and customer support overhead.
We needed a solution that could handle 10x traffic spikes without dropping transactions.
Context/Background
Connection Pooling: Implemented HikariCP with 50 connections. Reduced failures to 2%, but database became the bottleneck.
Why These Failed: Both approaches treated symptoms, not the root cause: our synchronous architecture couldn’t absorb traffic bursts. We needed to decouple ingestion from processing.
Solution
We implemented an event-driven architecture using Kafka as a buffer.
Trade-offs Considered
- Kafka
- Pros: High throughput, durable
- Cons: Operational complexity
- RabbitMQ
- Pros: Simpler setup
- Cons: Lower throughput
- Redis Streams
- Pros: Fast, simple
- Cons: Less durable
We chose Kafka despite complexity because durability was non-negotiable for financial transactions.
Results
Monitor queue depth: We added alerts when queue exceeds 10,000 messages to detect upstream issues early.
Consumer scaling matters: Started with 3 consumers, but needed 8 to keep up with peak processing.
Idempotency is essential: During testing, we discovered retry storms could cause duplicate processing without idempotency keys.
Conclusion
Key Takeaways
- Decouple ingestion from processing – Accepting requests and processing them are separate concerns with different scaling needs.
- Design for failure – Idempotency and dead-letter queues saved us during the first production incident.
- Measure before optimizing – Our initial assumption (database was slow) was wrong. Profiling showed the bottleneck was thread contention in our connection pool.