A few days ago I made something to use in addition to head/tail: Venetianblinds.js shows equally spaced chunks of a file https://github.com/firasd/venetianblinds
So I can kinda see these as being part of the same workflow like VBlinds for 'what does this file even look like' before calling the decompose
Finally, a tool that tackles this problem without forcing you to adopt an entire ecosystem. I appreciate how this leans into the Unix philosophy by just focusing on the decomposition step and doing it well. Really appreciate the modular architecture here.
I really like the idea of this. What I'd be looking for is to see how you convert those into OpenTelemetry metrics/logs or even traces.
if you find correlations between multiple log rows, those could be put into logs and mark with a trace/span id according to OpenTelemetry spec. Then even more tools could be used to actually analyze, alert, etc on those logs.
Also, extracting attributes, group logs, aggregate, etc. Those could be made searchable before being retrieved of a tool like sluicio, grafana, datadog etc so less data is being shipped (thereby keeping cost low).
Thereby, going from million of log rows no-one will read, to valuable metrics, shipping to a tool for visualization.
A few days ago I made something to use in addition to head/tail: Venetianblinds.js shows equally spaced chunks of a file https://github.com/firasd/venetianblinds
So I can kinda see these as being part of the same workflow like VBlinds for 'what does this file even look like' before calling the decompose
if you find correlations between multiple log rows, those could be put into logs and mark with a trace/span id according to OpenTelemetry spec. Then even more tools could be used to actually analyze, alert, etc on those logs. Also, extracting attributes, group logs, aggregate, etc. Those could be made searchable before being retrieved of a tool like sluicio, grafana, datadog etc so less data is being shipped (thereby keeping cost low).
Thereby, going from million of log rows no-one will read, to valuable metrics, shipping to a tool for visualization.
good work!