You add 24 more workers to fix Kafka lag — and they all sit idle. Meanwhile one huge message is stuck at the front of a partition, and nothing behind it moves.
Why “our queue guarantees exactly-once” is almost always a lie once your flow crosses a component boundary: Kafka EOS scope, ack-after-process, unique constraints, SQS FIFO, DB transactions with SMTP, retries without…
The bell curve taught you the average is a fair summary and outliers are rare. In software both halves are false: latencies, key popularity, file sizes and follower counts are heavy-tailed power laws where the outliers…
"Stamp each write with the current time and keep the latest" feels obvious — and it silently deletes data, because two machines can never agree on what time it is.
Most engineers think a leader election "picks the best node." It doesn't — it gets every node to agree on the same node, and agreement under failure is consensus.