Earlier quoted context omitted.
Interesting, I'd think logging is one of the clearest situations when you want best effort. Logging is, almost by definition, not the "core" of your application, so failure to log properly should not prevent the core of the program from working. Killing the whole program because logging server is clearly throwing the baby out with the bathwater. What people probably mean is "logging is important, let's avoid losing l…
If you lose logs when your service crashes you're losing logs at the time they are most important.
The real realtime preemption end game
81–90 of 281 posts
Re: The real realtime preemption end game
#82Earlier quoted context omitted.
I have discussions with cow-orkers around logging; "We have Best-Effort and Guaranteed-Delivery APIs" "I want Guaranteed Delivery!!!" "If the GD logging interface is offline or slow, you'll take downtime; is that okay?" "NO NO Must not take downtime!" "If you need it logged, and can't log it, what do you do?" These days I just point to the CAP theorem and suggest that logging is the same as any other distributed syst…
> "If the GD logging interface is offline or slow, you'll take downtime; is that okay?" > [edit: added "GD" to clarify that I was referring to the guaranteed delivery logging api, not the best effort logging API] i read GD as god-damned :-)
Re: The real realtime preemption end game
#83Earlier quoted context omitted.
The better way to do this is to write the logs to a file or an in-memory ring buffer and have a separate thread/process push logs from the file/ring-buffer to the logging service, allowing for retries if the logging service is down or slow (for moderately short values of down/slow). Promtail[1] can do this if you're using Loki for logging. [1] https://grafana.com/docs/loki/latest/send-data/promtail/
But that's still not guaranteed delivery. You're doing what the OP presented - choosing to drop logs under some circumstances when the system is down. a) If your service crashes and it's in-memory, you lose logs b) If your service can't push logs off (upstream service is down or slow) you either drop logs, run out of memory, or block
In other words it's good enough.
Re: The real realtime preemption end game
#84Earlier quoted context omitted.
You can divide realtime applications into safety-critical and non-safety-critical ones. For safety-critical apps, you're totally right. For non-critical apps, if it's late and therefore buggy once in a while, that sucks but nobody dies. Examples of the latter include audio and video playback and video games. Nobody wants pauses or glitches, but if you get one once in a while, nobody dies. So people deliver these on n…
This kind of makes the same point I made though -- apps without hard realtime requirements aren't "really realtime" applications
Re: The real realtime preemption end game
#85Earlier quoted context omitted.
The better way to do this is to write the logs to a file or an in-memory ring buffer and have a separate thread/process push logs from the file/ring-buffer to the logging service, allowing for retries if the logging service is down or slow (for moderately short values of down/slow). Promtail[1] can do this if you're using Loki for logging. [1] https://grafana.com/docs/loki/latest/send-data/promtail/
But that's still not guaranteed delivery. You're doing what the OP presented - choosing to drop logs under some circumstances when the system is down. a) If your service crashes and it's in-memory, you lose logs b) If your service can't push logs off (upstream service is down or slow) you either drop logs, run out of memory, or block
Re: The real realtime preemption end game
#86Synchronous logging strikes again! We ran into this some at work with GLOG (Google's logging library), which can, e.g., block on disk IO if stdout is a file or whatever. GLOG was like, 90-99% of culprits when our service stalled for over 100ms.
I would posit that if your product's availability hinges on +/- 100ms, you are doing something deeply wrong, and it's not your logging library's fault. Users are not going to care if a button press takes 100 more ms to complete.
Re: The real realtime preemption end game
#87I wonder if this being fixed will result in it displacing some notable amount of made-for-realtime hardware/software combos. Especially since there's now lots of cheap, relatively low power, and high clock rate ARM and x86 chips to choose from. With the clock rates so high, perfect real-time becomes less important as you would often have many cycles to spare for misses. I understand it's less elegant, efficient, etc.…
I get the sense that applications with true realtime requirements generally have hard enough requirements that they cannot allow even the remote possibility of failure. Think avionics, medical devices, automotive, military applications. If you really need realtime, then you really need it and "close enough" doesn't really exist. This is just my perception as an outsider though.
Re: The real realtime preemption end game
#88Re: The real realtime preemption end game
#89Earlier quoted context omitted.
The better way to do this is to write the logs to a file or an in-memory ring buffer and have a separate thread/process push logs from the file/ring-buffer to the logging service, allowing for retries if the logging service is down or slow (for moderately short values of down/slow). Promtail[1] can do this if you're using Loki for logging. [1] https://grafana.com/docs/loki/latest/send-data/promtail/
We did something like this at Weebly for stats. The app sent the stats to a local service via UDP, so shoot and forget. That service aggregated for 1s and then sent off server.
Re: The real realtime preemption end game
#90Synchronous logging strikes again! We ran into this some at work with GLOG (Google's logging library), which can, e.g., block on disk IO if stdout is a file or whatever. GLOG was like, 90-99% of culprits when our service stalled for over 100ms.
I would posit that if your product's availability hinges on +/- 100ms, you are doing something deeply wrong, and it's not your logging library's fault. Users are not going to care if a button press takes 100 more ms to complete.