They do not appear to understand the fundamental difference between logs, traces, and metrics. Sure, if you can log every event you want to record, then everything is just events (I will ignore the fact that they are still stuck on formatted text strings as a event format). The difference is what do you do when you can not record everything you want to either at build time or runtime.
Logs are independent. When you can not store every event, you can drop them randomly. You lose a perfect view of every logged event, but you still retain a statistical view. As we have already assumed you can not log everything, this is the best you can do anyways.
Traces are for correlated events where you want every correlated event (a trace) or none of them (or possibly the first N in a trace). Losing events within a trace makes the entire trace (or at least the latter portions) useless. When you can not store every event, you want to drop randomly at the whole trace level.
Metrics are for situations where you know you can not log everything. You aggregate your data at log time, so instead of getting a statistically random sample you instead get aggregates that incorporate all of your data at the cost of precision.
Note that for the purposes of this post, I have ignored the reason why you can not store every event. That is an orthogonal discussion and techniques that relieve that bottleneck allow more opportunities to stay on the happy path of "just events with post-processed analysis" that the author is advocating for.
Logs are independent. When you can not store every event, you can drop them randomly. You lose a perfect view of every logged event, but you still retain a statistical view. As we have already assumed you can not log everything, this is the best you can do anyways.
Traces are for correlated events where you want every correlated event (a trace) or none of them (or possibly the first N in a trace). Losing events within a trace makes the entire trace (or at least the latter portions) useless. When you can not store every event, you want to drop randomly at the whole trace level.
Metrics are for situations where you know you can not log everything. You aggregate your data at log time, so instead of getting a statistically random sample you instead get aggregates that incorporate all of your data at the cost of precision.
Note that for the purposes of this post, I have ignored the reason why you can not store every event. That is an orthogonal discussion and techniques that relieve that bottleneck allow more opportunities to stay on the happy path of "just events with post-processed analysis" that the author is advocating for.