But Nothing Has Changed on Our Side!

In my 15-plus years in software development, I have heard this phrase more times than I can count.

Usually, it goes like this: something crashes in production. The relevant teams gather in a conference room, a Teams meeting, a Slack channel, or a phone call. The goals? Understand what happened, fix it, and of course, find out who to blame.

Each team representative takes their turn declaring that “nothing has changed on our side,” somewhat suggesting that the issue must be related to another team’s deployments. The situation quickly begins to resemble a classic murder mystery: there are plenty of suspects, a room full of pointing fingers, and absolutely no clarity on the true culprit until the very end.

Interestingly, during the initial investigation, it really does look like everything has remained static. But as the clock ticks, someone inevitably recalls: “Well, we did change X, but it’s completely unrelated to the issue.” By the end of the post-mortem, we always discover that something did change somewhere. The connection is rarely trivial, making it incredibly difficult to spot at first glance.

To illustrate this, here are three recent production mysteries my Big Data team had to deal with.

Case 1: The Optimized Storage Bill

Our team manages a streaming data lake that stores fresh events arriving from an upstream messaging service. Our infrastructure team keeps a close eye on operational costs, and one day they alerted us about a sudden spike in our data lake spend.

Naturally, our first reaction was: “But nothing has changed on our side!” We started looking elsewhere. Did infrastructure change something? Did the upstream service start sending larger payloads? Did the cloud provider secretly raise their prices? No. Nothing had changed on their ends, either.

Eventually, we remembered that a couple of months ago, we had upgraded the framework responsible for writing events to the data lake. At the time, it didn’t seem relevant. But it was.

As it turned out, the framework upgrade optimized our writer job. It was now writing events much faster, which meant individual file sizes shrank, but the total number of files skyrocketed. Meanwhile, a downstream job was reading those files to perform computations. Because cloud object storage providers charge per GET request, exploding the file count increased our API calls (and our bill).

The twist: An optimization on the writer side caused a massive cost degradation on the processing side.

Case 2: The Silent Cloud Update

Another mystery began when one of our production jobs that writes data to a distributed database suddenly started failing. We jumped on a call with Operations and our DBAs to investigate.

“Nothing has changed on our side!” they claimed. This time, we were equally confident that nothing had changed on ours either. So, who was the culprit?

After digging deep into the system logs, we found the culprit: our cloud provider had automatically updated the Java minor version on our underlying virtual machines. It was a seemingly harmless update (17.0.17 –> 17.0.18), but it introduced a regression in how encrypted TLS connections were established, causing our database writes to fail.

The twist: This was one of those rare scenarios where absolutely nothing had changed across any of our internal teams, yet the ground shifted beneath our feet anyway.

Case 3: The Overconfident AI Assistant

Of course, you can’t talk about modern software engineering incidents without mentioning AI.

In this final case, a developer was working on a new feature and used an AI coding assistant. The feature was completed, reviewed, and deployed. Almost immediately, the production job crashed.

Now, we couldn’t claim that nothing changed; we did just deploy a feature. However, the scale of the failure felt completely disproportionate. The new feature was minor, yet it managed to bring down an entire distributed compute engine containing hundreds of machines. Nothing we changed could justify that scale of destruction!

What happened is this: During the coding process, the AI assistant decided to be helpful and added a caching operation to one of the dataframes to speed things up. Unfortunately, that specific dataframe represented our entire data lake. The cluster attempted to cache hundreds of terabytes of data into memory, and collapsed.

The twist: A tiny, well-intentioned line of AI-generated code caused a massive infrastructure failure.

The Post-Mortem Lessons

When the production smoke clears, a few universal truths remain:

1. Optimizations can be dangerous: Making a process faster or better in one place can cause harm in another (Case 1).
2. Infrastructure is never truly static: Cloud providers constantly roll out minor patches. Even a microscopic version bump can introduce breaking changes (Case 2).
3. Trust, but verify your AI: AI coding tools are incredibly powerful, but they lack architectural context. Always review AI-generated code and apply protective measures before deploying (Case 3).
4. Embrace humility: When production breaks, and it feels like everything is exactly as it was yesterday, remember that something always changed. It might even be on your side. It is better to approach debugging with humility rather than defensiveness, because you truly never know.

Disclaimer: The views expressed in this post are the author’s own and do not necessarily reflect the position of their employer.

Gregory “Grisha” Weintraub is a Big Data Team Lead at IBM and a Computer Science Ph.D. with over 15 years of industry experience specializing in large-scale cloud data systems.

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But Nothing Has Changed on Our Side!

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