Is it me or are observability stacks kind of ridiculous. Logs, metrics, and traces, each with their own databases, sidecars, visualization stacks. Language-specific integration libraries written by whoever felt like it. MASSIVE cloud bills.
Then after you go through all that effort most of the data is utterly ignored and rarely are the business insights much better then the trailer park version ssh'ing into a box and greping a log file to find the error output.
Like we put so much effort into this ecosystem but I don't think it has paid us back with any significant increase in uptime, performance, or ergonomics.
I can say that going from a place that had all of that observability tooling set up to one that was at the "ssh'ing into a box and greping a log" stage, you best believe I missed company A immensely. Even knowing which box to ssh into, which log file to grep, and which magic words to search far was nigh impossible if you weren't the dev that set up the machine and wrote the bug in the first place.
I completely agree with you but I also think, like many aspects of "tech" certain segments of it have been monopolised and turned into profit generators for certain organisations. DevOps, Agile/Scrum, Observability, Kubernetes, are all examples of this.
This dilutes the good and helpful stuff with marketing bullshit.
Grafana seemingly inventing new time series databases and engines every few months is absolutely painful to try keep up to date with in order to make informed decisions.
So much so I've started using rrdtool/smokeping again.
You might look into https://openobserve.ai/ - you can self host it and it's a single binary that ingests logs/metrics/traces. I've found it useful for my side projects.
if you're working on a system simple enough that "SSH to the box and grep the log file" works, then by all means have at it.
but many systems are more complicated than that. the observability ecosystem exists for a reason, there is a real problem that it's solving.
for example, your app might outgrow running on a single box. now you need to SSH into N different hosts and grep the log file from all of them. or you invent your own version of log-shipping with a shell script that does SCP in a loop.
going a step further, you might put those boxes into an auto-scaling group so that they would scale up and down automatically based on demand. now you really want some form of automatic log-shipping, or every time a host in the ASG gets terminated, you're throwing away the logs of whatever traffic it served during its lifetime.
or, maybe you notice a performance regression and narrow it down to one particular API endpoint being slow. often it's helpful to be able to graph the response duration of that endpoint over time. has it been slowing down gradually, or did the response time increase suddenly? if it was a sudden increase, what else happened around the same time? maybe a code deployment, maybe a database configuration change, etc.
perhaps the service you operate isn't standalone, but instead interacts with services written by other teams at your company. when something goes wrong with the system as a whole, how do you go about root-causing the problem? how do you trace the lifecycle of a request or operation through all those different systems?
when something goes wrong, you SSH to the box and look at the log file...but how do you know something went wrong to begin with? do you rely solely on user complaints hitting your support@ email? or do you have monitoring rules that will proactively notify you if a "huh, that should never happen" thing is happening?
Overall, I think centralized logging and metrics are super valuable. But stacks are all missing the mark.
For example, every damn log message has hundreds of fields,. Most of which never change. Why not push this information once, on service startup an not with every log message?
OK, obviously the current system provides huge bills to the benefit of the company or's offering these services.
> For example, every damn log message has hundreds of fields,. Most of which never change. Why not push this information once, on service startup an not with every log message?
If the log field doesn't change with every log entry, then good databases for logs (such as VictoriaLogs) compress such a field by 1000x and more times, so its' storage space usage can be ignored, and it doesn't affect query performance in any way.
Storing many fields per every log entry simplifies further analysis of these logs, since you can get all the needed information from a single log entry instead of jumping over big number of interconnected logs. This also improves analysis of logs at scale by filtering and grouping the logs by any subset of numerous fields. Such logs with big number of fields are named "wide events". See the following excellent article about this type of logs - https://jeremymorrell.dev/blog/a-practitioners-guide-to-wide... .
It's not just you - OSS toolstacks can be sprawling and involve long manual processes while costs from most enterprise vendors are too steep for fully mapped observability.
Coroot is an open source project I'm working with to try and to tackle this. eBPF automatically gathers your data into a centralized service map, and then the tool provides RCA insights (with things like mapped incident timeframes) to help implement fixes quicker and improve uptime.
Then after you go through all that effort most of the data is utterly ignored and rarely are the business insights much better then the trailer park version ssh'ing into a box and greping a log file to find the error output.
Like we put so much effort into this ecosystem but I don't think it has paid us back with any significant increase in uptime, performance, or ergonomics.