Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[FEAT] Remove the temperature graph #583

Open
zuavra opened this issue Feb 25, 2024 · 2 comments
Open

[FEAT] Remove the temperature graph #583

zuavra opened this issue Feb 25, 2024 · 2 comments

Comments

@zuavra
Copy link

zuavra commented Feb 25, 2024

May I suggest killing the temperature graph altogether? It's of limited use and has lots of bugs logged against it.

Since scrutiny logs to influxdb and it's trivial to connect that to grafana, you can make much more interesting widgets and graphs that way, based on the full range of attributes.

IMHO Scrutiny should focus on what it does best – logging SMART data, massaging it for human consumption, and presenting it in terms of Backblaze failure probability – but delegate fancy graphical representations of the data to projects that focus on that.

@SaraDark
Copy link

An option to turn on/off the temperature graph would be ok :)

@zuavra
Copy link
Author

zuavra commented Mar 26, 2024

FWIW InfluxDB2 also comes with the ability to make graphs and dashboards so you might not even need Grafana.

This will give you the equivalent of the temperature graph:

from(bucket: "metrics")
  |> range(start: -15d)
  |> filter(fn: (r) => r["_field"] == "attr.194.transformed_value")
  |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)
  |> yield(name: "last")

This is an example of plotting a single attribute (keeping an eye on attribute 188, Command Timeout):

from(bucket: "metrics")
  |> range(start: -15d)
  |> filter(fn: (r) => r["_field"] == "attr.188.transformed_value")
  |> group(columns: ["device_wwn"])
  |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)
  |> yield(name: "last")

This is an example of plotting multiple "bad" attributes (5, 197, 198) to see if any of them deviates from zero:

from(bucket: "metrics")
  |> range(start: -15d)
  |> filter(fn: (r) => r["_field"] == "attr.197.transformed_value" or r["_field"] == "attr.198.transformed_value" or r["_field"] == "attr.5.transformed_value")
  |> aggregateWindow(every: v.windowPeriod, fn: last, createEmpty: false)
  |> yield(name: "last")

Hope this helps somebody.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants