-
Notifications
You must be signed in to change notification settings - Fork 41
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
patch time sync plugin to handle stack too deep errors #993
Comments
There is also a community PR open that could fix this |
@jmcook1186 any chance you could point us to the manifest that was failing? |
The manifest below yields the following error:
However - i really had to push the time sync parameters to an extreme set of values to cause this! name: nesting
description: a manifest that includes nested child components
tags:
kind: web
complexity: moderate
category: on-premise
aggregation:
metrics:
- carbon
type: "both"
initialize:
plugins:
"interpolate":
method: Interpolation
path: "builtin"
config:
method: linear
x: [0, 10, 50, 100]
y: [0.12, 0.32, 0.75, 1.02]
input-parameter: "cpu/utilization"
output-parameter: "cpu-factor"
parameter-metadata:
inputs:
cpu/utilization:
unit: percentage
description: refers to CPU utilization.
aggregation-method:
time: avg
component: sum
outputs:
cpu-factor:
unit: kWh
description: result of interpolate
aggregation-method:
time: avg
component: avg
"cpu-factor-to-wattage":
method: Multiply
path: builtin
config:
input-parameters: ["cpu-factor", "cpu/thermal-design-power"]
output-parameter: "cpu-wattage"
parameter-metadata:
inputs:
cpu-factor:
unit: kWh
description: result of interpolate
aggregation-method:
time: avg
component: avg
cpu/thermal-design-power:
unit: kWh
description: thermal design power for a processor
aggregation-method:
time: avg
component: avg
outputs:
cpu-wattage:
unit: kWh
description: the energy used by the CPU
aggregation-method:
time: sum
component: sum
"wattage-times-duration":
method: Multiply
path: builtin
config:
input-parameters: ["cpu-wattage", "duration"]
output-parameter: "cpu-wattage-times-duration"
"wattage-to-energy-kwh":
method: Divide
path: "builtin"
config:
numerator: cpu-wattage-times-duration
denominator: 3600000
output: cpu-energy-raw
parameter-metadata:
inputs:
cpu-wattage-times-duration:
unit: kWh
description: CPU wattage multiplied by duration
aggregation-method:
time: sum
component: sum
outputs:
cpu-energy-raw:
unit: kWh
description: Raw energy used by CPU in kWh
aggregation-method:
time: sum
component: sum
"calculate-vcpu-ratio":
method: Divide
path: "builtin"
config:
numerator: vcpus-total
denominator: vcpus-allocated
output: vcpu-ratio
parameter-metadata:
outputs:
vcpu-ratio:
unit: none
description: Ratio of vCPUs
aggregation-method:
time: copy
component: copy
"correct-cpu-energy-for-vcpu-ratio":
method: Divide
path: "builtin"
config:
numerator: cpu-energy-raw
denominator: vcpu-ratio
output: cpu-energy-kwh
sci-embodied:
path: "builtin"
method: SciEmbodied
"operational-carbon":
method: Multiply
path: builtin
config:
input-parameters: ["cpu-energy-kwh", "grid/carbon-intensity"]
output-parameter: "carbon-operational"
parameter-metadata:
inputs:
cpu-energy-kwh:
unit: kWh
description: Corrected CPU energy in kWh
aggregation-method:
time: sum
component: sum
grid/carbon-intensity:
unit: gCO2eq/kWh
description: Carbon intensity for the grid
aggregation-method:
time: avg
component: avg
outputs:
carbon-operational:
unit: gCO2eq
description: Operational carbon footprint
aggregation-method:
time: sum
component: sum
sci:
path: "builtin"
method: Sci
config:
functional-unit: "requests"
parameter-metadata:
inputs:
requests:
unit: none
description: expressed the final SCI value
aggregation-method:
time: sum
component: sum
"sum-carbon":
path: "builtin"
method: Sum
config:
input-parameters:
- carbon-operational
- embodied-carbon
output-parameter: carbon
parameter-metadata:
inputs:
carbon-operational:
description: Operational carbon footprint
unit: gCO2eq
aggregation-method:
time: sum
component: sum
embodied-carbon:
description: Embodied carbon footprint
unit: gCO2eq
aggregation-method:
time: sum
component: sum
outputs:
carbon:
description: Total carbon footprint
unit: gCO2eq
aggregation-method:
time: sum
component: sum
time-sync:
method: TimeSync
path: "builtin"
config:
start-time: '2023-01-01T00:00:00.000Z'
end-time: '2024-01-01T00:00:00.000Z'
interval: 2
allow-padding: true
parameter-metadata:
inputs:
timestamp:
unit: RFC3339
description: refers to the time of occurrence of the input
aggregation-method:
time: none
component: none
duration:
unit: seconds
description: refers to the duration of the input
aggregation-method:
time: sum
component: sum
cloud/instance-type:
unit: none
description: type of Cloud Instance name used in the cloud provider APIs
aggregation-method:
time: copy
component: copy
cloud/region:
unit: none
description: region cloud instance
aggregation-method:
time: copy
component: copy
time-reserved:
unit: seconds
description: time reserved for a component
aggregation-method:
time: avg
component: avg
network/energy:
description: "Energy consumed by the Network of the component"
unit: "kWh"
aggregation-method:
time: sum
component: sum
tree:
children:
child-0:
defaults:
cpu/thermal-design-power: 100
grid/carbon-intensity: 800
device/emissions-embodied: 1533.120 # gCO2eq
time-reserved: 3600 # 1hr in seconds
device/expected-lifespan: 94608000 # 3 years in seconds
vcpus-allocated: 1
vcpus-total: 8
pipeline:
compute:
- interpolate
- cpu-factor-to-wattage
- wattage-times-duration
- wattage-to-energy-kwh
- calculate-vcpu-ratio
- correct-cpu-energy-for-vcpu-ratio
- sci-embodied
- operational-carbon
- sum-carbon
- time-sync
- sci
inputs:
- timestamp: "2023-01-01T00:00:00.000Z"
cloud/instance-type: A1
cloud/region: uk-west
duration: 2629800
cpu/utilization: 50
network/energy: 0.000001
requests: 50
- timestamp: "2023-02-01T00:00:00.000Z"
duration: 2629800
cpu/utilization: 20
cloud/instance-type: A1
cloud/region: uk-west
network/energy: 0.000001
requests: 60
- timestamp: "2023-03-01T00:00:00.000Z"
duration: 2629800
cpu/utilization: 15
cloud/instance-type: A1
cloud/region: uk-west
network/energy: 0.000001
requests: 70
- timestamp: "2023-04-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
- timestamp: "2023-05-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
- timestamp: "2023-06-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
- timestamp: "2023-07-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
child-1:
defaults:
cpu/thermal-design-power: 100
grid/carbon-intensity: 800
device/emissions-embodied: 1533.120 # gCO2eq
time-reserved: 3600 # 1hr in seconds
device/expected-lifespan: 94608000 # 3 years in seconds
vcpus-allocated: 1
vcpus-total: 8
pipeline:
compute:
- interpolate
- cpu-factor-to-wattage
- wattage-times-duration
- wattage-to-energy-kwh
- calculate-vcpu-ratio
- correct-cpu-energy-for-vcpu-ratio
- sci-embodied
- operational-carbon
- sum-carbon
- time-sync
- sci
inputs:
- timestamp: "2023-01-01T00:00:00.000Z"
cloud/instance-type: A1
cloud/region: uk-west
duration: 2629800
cpu/utilization: 50
network/energy: 0.000001
requests: 50
- timestamp: "2023-02-01T00:00:00.000Z"
duration: 2629800
cpu/utilization: 20
cloud/instance-type: A1
cloud/region: uk-west
network/energy: 0.000001
requests: 60
- timestamp: "2023-03-01T00:00:00.000Z"
duration: 2629800
cpu/utilization: 15
cloud/instance-type: A1
cloud/region: uk-west
network/energy: 0.000001
requests: 70
- timestamp: "2023-04-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
- timestamp: "2023-05-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
- timestamp: "2023-06-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
- timestamp: "2023-07-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
child-2:
children:
child-2-0:
defaults:
cpu/thermal-design-power: 100
grid/carbon-intensity: 800
device/emissions-embodied: 1533.120 # gCO2eq
time-reserved: 3600 # 1hr in seconds
device/expected-lifespan: 94608000 # 3 years in seconds
vcpus-allocated: 1
vcpus-total: 8
pipeline:
compute:
- interpolate
- cpu-factor-to-wattage
- wattage-times-duration
- wattage-to-energy-kwh
- calculate-vcpu-ratio
- correct-cpu-energy-for-vcpu-ratio
- sci-embodied
- operational-carbon
- sum-carbon
- time-sync
- sci
inputs:
- timestamp: "2023-01-01T00:00:00.000Z"
cloud/instance-type: A1
cloud/region: uk-west
duration: 2629800
cpu/utilization: 50
network/energy: 0.000001
requests: 50
- timestamp: "2023-02-01T00:00:00.000Z"
duration: 2629800
cpu/utilization: 20
cloud/instance-type: A1
cloud/region: uk-west
network/energy: 0.000001
requests: 60
- timestamp: "2023-03-01T00:00:00.000Z"
duration: 2629800
cpu/utilization: 15
cloud/instance-type: A1
cloud/region: uk-west
network/energy: 0.000001
requests: 70
- timestamp: "2023-04-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
- timestamp: "2023-05-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
- timestamp: "2023-06-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
- timestamp: "2023-07-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
child-2-1:
defaults:
cpu/thermal-design-power: 100
grid/carbon-intensity: 800
device/emissions-embodied: 1533.120 # gCO2eq
time-reserved: 3600 # 1hr in seconds
device/expected-lifespan: 94608000 # 3 years in seconds
vcpus-allocated: 1
vcpus-total: 8
pipeline:
compute:
- interpolate
- cpu-factor-to-wattage
- wattage-times-duration
- wattage-to-energy-kwh
- calculate-vcpu-ratio
- correct-cpu-energy-for-vcpu-ratio
- sci-embodied
- operational-carbon
- sum-carbon
- time-sync
- sci
inputs:
- timestamp: "2023-01-01T00:00:00.000Z"
cloud/instance-type: A1
cloud/region: uk-west
duration: 2629800
cpu/utilization: 50
network/energy: 0.000001
requests: 50
- timestamp: "2023-02-01T00:00:00.000Z"
duration: 2629800
cpu/utilization: 20
cloud/instance-type: A1
cloud/region: uk-west
network/energy: 0.000001
requests: 60
- timestamp: "2023-03-01T00:00:00.000Z"
duration: 2629800
cpu/utilization: 15
cloud/instance-type: A1
cloud/region: uk-west
network/energy: 0.000001
requests: 70
- timestamp: "2023-04-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
- timestamp: "2023-05-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
- timestamp: "2023-06-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55
- timestamp: "2023-07-01T00:00:00.000Z"
duration: 2629800
cloud/instance-type: A1
cloud/region: uk-west
cpu/utilization: 15
network/energy: 0.000001
requests: 55 |
@jmcook1186 please attach the new manifest that throws this error |
@narekhovhannisyan your suggestion - to break down the file into logical chunks |
Put it aside given #1057 |
as #1057 is not going to be implemented, this again becomes an issue to fix |
Will get back to this after discovering what was the blocker with the group by issue |
Status update: expecting a PR by early next week |
Why: Sub of #949 . The framework is implemented to deal with very granular time units (seconds), but in real life the units for measurements come in larger time intervals that the IF is unable to handle, causing overflow errors.
What: Find an MVP solution for the time being to enable working with larger time intervals. Will return to this problem at a later date to implement a more robust solution
Context
We often have patchy data, we don't have granular data, or we are only interested in values at e.g. monthly time resolution.
We don't work well in these situations, even though they are very common - we're really just set up for situations where we can load in granular time series.
We often see people (and this is what I ended up doing in v1 of the GSF site manifest) have a single timestep with a duration of 1 month or one year in seconds and then execute a single, large pipeline that eventually yields SCI. This is because it's very fiddly and repetitive to compartmentalise into individual components and often we can't / don't really need to access granular temporal data anyway.
If I just want an overall SCI value for my entire application, I'm not going to spend a week trying to source granular data, or even worse naively chunking up the single value that fits my needs into time block just because that's what a manifest favours.
As a user in this situation it's much easier to interpret my manifest when I've just constructed a pipeline, can follow through the logic and read off my single value as compared to interpreting aggregated data, which is presented in quite a non-aesthetic way.
A separate time issue is the current behaviour of time sync. It works as a plugin, which means we have to invoke it in every pipeline in the tree. But we encountered some problematic side effects associated with making it a “global” feature. On balance we decided to stick with the status quo for now, but we need to revisit the way we handle time, perhaps starting from a blank page.
The current
time-sync
plugin often errors out with “RangeError: Maximum call stack size exceeded” when we’re using time units greater than minutes. For example, time-sync applied to monthly data, even when we just want to create time series at a resolution of 10 or even 100 hours, throws with this error. Maybe this is because of our “base” resolution being fixed at 1s, which creates too much data when we’re trying to operate over monthly data. Either way, this needs fixing or we’re only applicable to observations over seconds -> minute ranges, not week -> month -> year ranges.Scope of work:
The text was updated successfully, but these errors were encountered: