Software Engineering Fun Facts
Surprising, practical, and sometimes humbling truths about building software at scale.
“Google's first storage system used cheap consumer hardware because failures were expected, not avoided.”
Fault tolerance patterns“A cache hit in Redis can be thousands of times faster than a disk database lookup.”
Caching patterns“The term "bug" became famous after an actual moth was found inside a computer relay in 1947.”
“Netflix intentionally breaks parts of its own systems in production using Chaos Engineering.”
Netflix case study“Most software bugs happen because of edge cases, not the main logic path.”
“A Bloom Filter can tell you something is "definitely not present" extremely fast using very little memory.”
Bloom filters explained“Kafka keeps messages on disk, yet can still process millions of messages per second.”
Message queues guide“The JVM does not directly execute Java code. It executes bytecode generated by the compiler.”
Java roadmap“A single slow database query can increase cloud bills across multiple services.”
Performance optimization“DNS lookups happen before most internet requests, but many developers forget they add latency too.”
Networking basics“Git was created by Linus Torvalds in just a few days after a licensing dispute with BitKeeper.”
“"Premature optimization is the root of all evil" is one of the most quoted engineering principles.”
When to optimize“Most distributed systems problems become visible only under high traffic or partial failures.”
Fault tolerance patterns“UUID v4 has so many combinations that collisions are practically impossible for most applications.”
Distributed ID generation“Many microservices eventually rediscover monolith-like problems through excessive inter-service communication.”
Microservices architecture“Amazon once found that every extra 100ms of latency reduced revenue noticeably.”
Latency impact“Java's String pool exists mainly to save memory by reusing immutable string objects.”
Java roadmap“Kubernetes was originally inspired by Google's internal Borg system.”
System architecture“The hardest part of scaling systems is often data consistency, not compute power.”
Distributed data stores“A memory leak may not crash your app immediately — it slowly reduces system stability over time.”
Performance optimization“Most APIs spend more time waiting on networks and databases than executing business logic.”
System concepts“Event-driven systems improve scalability, but debugging them is usually harder than debugging monoliths.”
Event-driven architecture“"Exactly once delivery" in distributed systems is much harder than it sounds.”
Distributed transactions“The first 90% of coding is often faster than the last 10% of debugging.”