This is a production systems series.
It’s about why systems fail even when CPU is low, database looks healthy, and dashboards appear green.
Every article builds on queueing theory, bounded resources, and real-world failure patterns.
Client
│
▼
Load Balancer
│
▼
App Server
│
├── ThreadPool
│
├── Connection Pool ──► Database
│
├── Redis
│
└── External Services
Failures do not start at the database.
They start at:
And they propagate upward.
Production failures typically follow this pattern:
Cache illusion
↓
Lock illusion
↓
Retry amplification
↓
Tail latency amplification
↓
Connection pool exhaustion
↓
ThreadPool expansion
↓
Exactly-once myth exposed
Each article in this series isolates one step.
Why caching does not eliminate load, it only shifts it. Explains hit ratio illusion, tail latency, and amplification effects.
Why Redis locks fail under partitions and why mutual exclusion is not correctness.
How production systems achieve correctness through replay-safe operations.
Why retries without backoff create exponential load amplification.
Why P99 determines system collapse, not average latency.
Why pool exhaustion causes timeouts even when DB CPU is low.
How auto-scaling threads hide saturation and increase instability.
Why distributed correctness comes from idempotency, not magical guarantees.
If you are optimizing for 5% performance gain, this is not for you.
If you are responsible for systems that cannot fail, this is.
Production systems fail at resource boundaries.
Not at feature boundaries.
Bounded concurrency. Backpressure. Idempotent design. Queue awareness.
These are survival skills.