Charlie’s Law: The DynamoDB Doctrine and the Engineering Cost of Infinite Scale

... Would He Have Approved My Exception Request? I Dunno...

Let me tell you about Charlie Bell from Amazon Web Services.

First of all, I owe a lot to Charlie. He taught me a great deal over the eight years I spent at AWS. I owe him a ton - whether it was OpsMetrics on Wednesday. Or a 1:1 where he physically demonstrated that even senior leaders wear pants, and the main reason they appear to be geniuses is because of an information advantage. Or a security review where he basically insinuated that I wasn’t deserving of running an AWS service (he was probably right, as it was my first week on the job).

His impact as a leader? I cried the day he left AWS to go to Microsoft.

Charlie was the kind of leader who didn’t just ask if your system would scale—he assumed it wouldn’t, and then asked why you thought you could be trusted to run anything at all.

He ran operations at Amazon the way NASA runs launch day: nothing left to chance, every decision filtered through the cold, rational logic of uptime, latency, and durability. So when Charlie said—declared—that every new AWS service must use DynamoDB, it wasn’t a preference.

It was the law.

You could attempt to get an exception when building your service. But I didn’t try and I wasn’t aware of many AWS service owners who tried to get this exception.

Now, let me be clear: I have tremendous respect for Charlie. His rule came from real scars. But like all doctrine forged in fire, it became rigid. And rigidity in architecture, while offering stability, comes with its own cost: it slows us down.

Let’s dig deep into the internals of DynamoDB, the Aurora that could have been, and the subtle, yet painful, latency truths hiding beneath Charlie’s perfect rule.

How DynamoDB Works Under the Hood (And Why It’s Beautiful)

DynamoDB isn’t a database. It’s a distributed, eventually consistent, multi-tenant key-value juggernaut dressed up like a table. Internally, it’s inspired by the original Dynamo paper (2007), which gave birth to the design principles of eventual consistency, anti-entropy, and decentralized data partitioning.