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We have a user reporting a bug in learning and we don't have the instrumentation in place to allow us to figure out where things are breaking.
There are two ways we could handle this.
Use OTEL to create traces that track the learning process for blocks
Use logging
I'm leaning towards OTEL. The reason being we probably want aggregate statics/down sampling because otherwise we will be wasting lots of storage. Our JSON logs are already quite big and we don't have any sort of log rotation/etc...
I think it might better to reserver JSONLogs for large structured payloads that OTEL doesn't handle well.
The downside of OTEL is I'm not sure if there's a good local option. Of course people could use Honeycomb's free tier.
@sourishkrout any thoughts? Do you have an OTEL backend you already using?
The text was updated successfully, but these errors were encountered:
@sourishkrout any thoughts? Do you have an OTEL backend you already using?
I have used Jaeger successfully with Runme before. It worked well for local-only and is very cost-effective before considering the usual suspects. It's also super easy to export and import traces for simple collab.
See: #215 (comment)
We have a user reporting a bug in learning and we don't have the instrumentation in place to allow us to figure out where things are breaking.
There are two ways we could handle this.
I'm leaning towards OTEL. The reason being we probably want aggregate statics/down sampling because otherwise we will be wasting lots of storage. Our JSON logs are already quite big and we don't have any sort of log rotation/etc...
I think it might better to reserver JSONLogs for large structured payloads that OTEL doesn't handle well.
The downside of OTEL is I'm not sure if there's a good local option. Of course people could use Honeycomb's free tier.
@sourishkrout any thoughts? Do you have an OTEL backend you already using?
The text was updated successfully, but these errors were encountered: