No, monitoring for HTTP response code is a subset of observability and not one that generally gives you the best insights into which subsystems are misbehaving nor why.

There are synthetic tests, where you can generate API request calls or even simulate an entire user journey. These allow you to control the user agent, the payloads, and thus you know anything errors back are actual errors. These are triggered by the observability platform (think like running a cron-job) and thus you're not tied to user activity to see when problems arise.

There are other metrics outside of HTTP response codes too. Think like free RAM, CPU usage, disk space, etc. This is just naming some obvious ones because these types of metrics are generally bespoke to the type of application your monitoring. And with these types of monitors, you'd not just have an alert when things have failed, but ideally have alerts when an irregular trend is showing that things are likely to fail too. This latter type of monitors helps you get ahead of the problem before it become customer facing.

Then you have more traditional stuff like logs. This will also be bespoke to the application. But you'd expect errors in logs to get surfaced quickly. Assuming Github have good hygiene in what's being logged.

Tie that up with APMs, RUM, and other goodies like that and you'll have diagnostics to investigate issues when they appear.

(this is just a super high level view of observability too)

It’s where monitoring for 9s is more important at that scale than absolute errors. So long as degradation is graceful or retried it should not be a massive problem.

It does require constant tuning and adjustment though.

Bitflips are something that can happen in consumer-grade RAM, so that tracks (and it's comforting that wayward cosmic rays are a substantial reason for an application's crashes!), but on enterprise servers, they will run ECC RAM that is very resistant to bit flips.

This is why data hoarders who have NASes with lots of space insist on running their servers with ECC RAM despite it being significantly more expensive. Because bit flips, for all intents and purposes, cannot happen. The RAM itself detects and corrects for them.

I wouldn't expect bit flips to be a significant contributor to enterprise problems.

I've been oncall for a different G service that nearly paged on every error. It used the standard error budget tooling, but on hundreds of user buckets because the engineering around locality-specific configuration was... suspect. Many of these buckets had single-digits user. If a user was on their phone and lost signal, I was paged. Very poor oncall experience.

The sub-service at IBM cloud I worked on had an insanely small error budget such that pages were nearly constant. On call was hell week until a few of us insisted on fixing the issues. The "few" of us were contractors. The employees seemed more than willing to just let the pages continue.

I worked at a large fintech moving billions of dollars in volume a day.

I had a fairly long tenure, where I maintained multiple key services in critical online payments flow. Authentication, authorization, core business and risk data, as well as some cross-cutting control plane stuff, etc. You needed one or more of our services to take a payment, serve any request from the employee dashboard - pretty much everything hit our services. The entire company ground to a halt without my team.

We paged for every single 500. In instances where a particular class of 500 was spurious or not worth fixing, we would leave it acked or mark it as noise. But typically we'd just put in a fix as soon as possible so we didn't page.

Our graceful shutdown and traffic shaping stack was great, but occasionally we'd get a few pages during deploys or failovers.

Oncall was typically not bad, but when it did get bad it was terrible. I've been involved in huge outages that cost hundreds of millions of dollars. Usually it was the fault of multiple teams having compounding runaway failures rather than one service or bug in particular.

It's inexcusable to have a customer's payments not go through. We engineered around resilience. We had strict five nines SLAs and p99 targets and evaluated our adherence with even the smallest partial outage. Hundreds of other services depended on ours, and downstream impacts were huge, so we had to keep a tight ship.

We didn't have "business hours"-only paging either as our platform was available globally, including a heavy install base in Asia.

Shouldn't Github be large enough to not have anyone on-call, but just rotate the responsible team around the world?

It depends what you're monitoring. If it's response codes from user generated queries, then I'd agree with you.

But if it is synthetic queries sent from the monitoring platform, then you control the user agent, payload, and endpoints. So any failed requests are a symptom of a misconfiguration and/or failure that should be investigated. Albeit not necessarily as a P1 priority.