Two-phase commit guarantees atomicity across multiple databases. It also blocks everything if the coordinator dies. Here’s why microservices moved on.

Every public write endpoint is an abuse vector. Layered defense with validation, rate limiting, and async scanning keeps your system safe without killing performance.

Counting unique items across billions of events. A HashSet needs gigabytes. HyperLogLog does it in 12KB. The trick is accepting a little error.

100% availability is impossible and pursuing it wastes engineering time. SLOs turn reliability into a number you can reason about.

Auto-increment IDs break the moment you have more than one database. Snowflake IDs, UUIDs, and database sequences each solve this differently.

Showing every individual event overwhelms users. Grouping related events into summaries is a distributed systems problem hiding as a UX problem.

Green dot means online. Simple, right? Behind that dot is a distributed system making heartbeat-based guesses about user liveness.

User posts an update. Do you push it to all followers immediately, or let them pull it when they check? The trade-off shapes your entire architecture.

Your client needs real-time updates from the server. HTTP wasn’t built for this. Here’s how long polling, SSE, and WebSockets solve it differently.

Cache expires. 10,000 requests hit the database simultaneously. Your DB collapses. How request coalescing and probabilistic expiration prevent the stampede.

Grep through 50 log files to find one request. Or use structured logging with correlation IDs and find it in seconds.

Your average latency looks great. Your P99 is a disaster. Why tail latency matters more than averages, and what you can actually do about it.

Events arrive out of order. User updates overwrite each other. Here’s how partition keys, sequence numbers, and causal ordering keep things straight.

Your consumer retried a bad message 10,000 times. It will never succeed. Dead letter queues catch the messages that can’t be processed so the rest of your system keeps moving.

Exactly-once delivery is impossible across boundaries. Here’s the pattern that actually works: at-least-once delivery with idempotent consumers.

Kafka says exactly-once. Your consumer processed the message twice anyway. Here’s why exactly-once is impossible across system boundaries.

Why setting timeouts is harder than it looks. Cascading failures, timeout budgets, and the art of picking the right number.

Why distributed locking is harder than it looks. Naive Redis locks, Redlock, fencing tokens, and when to avoid locks entirely.

How CRDTs let distributed systems merge updates without coordination. The math that makes ‘conflict-free’ possible.

How distributed systems spread information without a central coordinator. The surprisingly effective technique of random peer-to-peer chatter.

How distributed systems track ‘what happened before what’ without trusting wall clocks. Lamport timestamps for ordering, vector clocks for detecting conflicts.

How distributed systems agree on state. A practical look at Raft’s Leader Election and Log Replication, finally making sense of consensus.

Why the CAP Theorem is the most misunderstood rule in system design. Addressing the ‘Pick 2’ lie and how it sets the stage for consensus algorithms.

When a request vanishes into a maze of 10 microservices. How Distributed Tracing and OpenTelemetry keep you from going insane during an outage.

Why standard views are just aliases and how materialized views act as an ‘in-database cache’ to solve the cross-shard query problem.

The thought experiment that proves distributed consensus can’t be guaranteed over unreliable networks. Why acknowledgments create infinite regress and what it means for real systems.

How to partition database across multiple servers. Hash-based vs range-based sharding, rebalancing strategies, and the complexity that comes with it.

Protecting services from overload with rate limiting. Token bucket and leaky bucket algorithms explained with Java implementations and real-world trade-offs.

Handling slow consumers in distributed systems. Queue growth, memory exhaustion, and strategies for applying backpressure - rejection, rate limiting, and flow control.

How to retry failed requests without overwhelming servers. Exponential backoff, jitter, and when to give up. Java implementations and real-world patterns.