> The engineer is forced into manual correlation: jumping between dashboards, aligning timelines by eye, [and] inferring causality from coincidence
I just generate a random UUID in the application and make sure to log it everywhere across the entire stack along with a timestamp.
Any old log aggregator can give me an accurate timeline grouped by request UUID across every backend component all in one dashboard.
It's the very first thing that I have the application do when handling a request. It's injected it at the log handler level. There's nothing to break and nothing to think about.
So, I have no problem knowing precise cause and effect with regard to all logs for a given isolated request, but I agree that there may be blips that affect multiple requests (outages, etc.). We have synthetic tests for outages though.
I too am struggling to understand what this tool does beyond grouping all logs by a unique request identifier.
I just generate a random UUID in the application and make sure to log it everywhere across the entire stack along with a timestamp.
Any old log aggregator can give me an accurate timeline grouped by request UUID across every backend component all in one dashboard.
It's the very first thing that I have the application do when handling a request. It's injected it at the log handler level. There's nothing to break and nothing to think about.
So, I have no problem knowing precise cause and effect with regard to all logs for a given isolated request, but I agree that there may be blips that affect multiple requests (outages, etc.). We have synthetic tests for outages though.
I too am struggling to understand what this tool does beyond grouping all logs by a unique request identifier.