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k8s.yml
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k8s.yml
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apiVersion: influxdata.com/v2alpha1
kind: Label
metadata:
name: keen-sutherland-9f8001
spec:
color: '#066fc5'
name: inputs.kube_inventory
---
apiVersion: influxdata.com/v2alpha1
kind: Label
metadata:
name: reverent-ardinghelli-9f8005
spec:
color: '#00a3ff'
name: inputs.kubernetes
---
apiVersion: influxdata.com/v2alpha1
kind: Label
metadata:
name: thirsty-matsumoto-9f8003
spec:
color: '#757888'
name: K8S
---
apiVersion: influxdata.com/v2alpha1
kind: Variable
metadata:
name: quirky-taussig-9f8007
spec:
language: flux
name: bucket
query: |-
buckets()
|> rename(columns: {"name": "_value"})
|> keep(columns: ["_value"])
type: query
---
apiVersion: influxdata.com/v2alpha1
kind: Dashboard
metadata:
name: competent-mendel-9f8001
spec:
associations:
- kind: Label
name: keen-sutherland-9f8001
- kind: Label
name: thirsty-matsumoto-9f8003
charts:
- height: 1
kind: Markdown
name: Name this Cell
note: '## Kubernetes Landscape - Overview'
width: 7
- colors:
- hex: '#00C9FF'
name: laser
type: text
decimalPlaces: 2
height: 1
kind: Single_Stat
name: Pods in replicas
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_deployment")
|> filter(fn: (r) => r._field == "replicas_available")
|> last()
|> drop(columns: ["deployment_name"])
|> sum(column: "_value")
width: 1
yPos: 1
- height: 1
kind: Markdown
name: Name this Cell
note: '## Containers List'
width: 5
yPos: 2
- colors:
- hex: '#00C9FF'
name: laser
type: text
fieldOptions:
- visible: true
- displayName: result
fieldName: result
visible: true
- displayName: table
fieldName: table
visible: true
- displayName: container_name
fieldName: container_name
visible: true
- displayName: namespace
fieldName: namespace
visible: true
- displayName: pod_name
fieldName: pod_name
visible: true
- displayName: state
fieldName: state
visible: true
height: 5
kind: Table
name: Running Containers
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_pod_container")
|> filter(fn: (r) => r._field == "state_code")
|> group(columns:["pod_name","container_name"])
|> last()
|> group(columns: ["host"])
|> keep(columns:["namespace","container_name","pod_name", "state"])
tableOptions:
verticalTimeAxis: true
timeFormat: YYYY-MM-DD HH:mm:ss
width: 7
yPos: 3
- height: 1
kind: Markdown
name: Name this Cell
note: '## Nodes CPU Usage'
width: 5
yPos: 8
- axes:
- base: "10"
name: x
scale: linear
- base: "10"
label: nanocore
name: y
scale: linear
colors:
- hex: '#31C0F6'
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
name: Nineteen Eighty Four
type: scale
geom: line
height: 4
kind: Xy
name: Pods CPU Usage in Nanocores
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_system_container")
|> filter(fn: (r) => r._field == "cpu_usage_nanocores")
|> filter(fn: (r) => r.container_name == "pods")
|> aggregateWindow(every: v.windowPeriod, fn: last)
|> yield(name: "last")
shade: true
width: 5
xCol: _time
yCol: _value
yPos: 9
- height: 1
kind: Markdown
name: Name this Cell
note: '## Memory Consumption'
width: 6
yPos: 13
- axes:
- base: "10"
name: x
scale: linear
- base: "10"
name: y
scale: linear
suffix: B
colors:
- hex: '#31C0F6'
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
name: Nineteen Eighty Four
type: scale
geom: line
height: 4
kind: Xy
name: Pods MEM usage in bytes
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_system_container")
|> filter(fn: (r) => r._field == "memory_usage_bytes")
|> filter(fn: (r) => r.container_name == "pods")
|> aggregateWindow(every: v.windowPeriod, fn: last)
|> yield(name: "last")
shade: true
width: 3
yPos: 14
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: y
scale: linear
- base: "10"
name: y2
scale: linear
geom: line
height: 3
kind: Xy
name: Node memory
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> filter(fn: (r) => r._field == "capacity_memory_bytes" or r._field == "allocatable_memory_bytes")
|> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)
|> yield(name: "mean")
shade: true
width: 3
xCol: _time
yCol: _value
yPos: 18
- colors:
- hex: '#00C9FF'
name: laser
type: text
decimalPlaces: 2
height: 1
kind: Single_Stat
name: Capacity Pods
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> filter(fn: (r) => r._field == "allocatable_pods")
|> last()
|> drop(columns: ["node_name"])
|> sum(column: "_value")
width: 1
xPos: 1
yPos: 1
- colors:
- hex: '#545667'
name: graphite
type: text
decimalPlaces: 0
height: 1
kind: Single_Stat
name: Allocatable Pods
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> filter(fn: (r) => r._field == "allocatable_pods")
|> last()
|> drop(columns: ["node_name"])
|> sum(column: "_value")
width: 1
xPos: 2
yPos: 1
- colors:
- hex: '#4ED8A0'
name: rainforest
type: text
- hex: '#FFB94A'
name: pineapple
type: text
value: 30
decimalPlaces: 2
height: 1
kind: Single_Stat
name: Running Pods
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_pod_container")
|> filter(fn: (r) => r._field == "state_code")
|> filter(fn: (r) => r.state == "running")
|> last()
|> drop(columns: ["container_name","node_name"])
|> distinct(column: "pod_name")
|> drop(columns: ["pod_name"])
|> count()
width: 1
xPos: 3
yPos: 1
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: y
scale: linear
- base: "10"
name: y2
scale: linear
geom: line
height: 7
kind: Xy
name: Resource memory
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_pod_container")
|> filter(fn: (r) => r._field == "resource_requests_memory_bytes")
|> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)
|> yield(name: "mean")
shade: true
width: 4
xCol: _time
xPos: 3
yCol: _value
yPos: 14
- colors:
- hex: '#00C9FF'
name: laser
type: text
decimalPlaces: 2
height: 1
kind: Single_Stat
name: Replicas count
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_deployment")
|> filter(fn: (r) => r._field == "replicas_available")
|> last()
|> drop(columns: ["deployment_name"])
|> count(column: "_value")
width: 1
xPos: 4
yPos: 1
- colors:
- hex: '#00C9FF'
name: laser
type: text
- hex: '#4ED8A0'
name: rainforest
type: text
value: 20
decimalPlaces: 2
height: 2
kind: Single_Stat
name: Running Containers
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_pod_container")
|> filter(fn: (r) => r._field == "state_code")
|> filter(fn: (r) => r.state == "running")
|> last()
|> drop(columns: ["container_name","pod_name","node_name"])
|> count()
width: 2
xPos: 5
yPos: 1
- height: 1
kind: Markdown
name: Name this Cell
note: '## CPU Cores'
width: 2
xPos: 5
yPos: 8
- colors:
- hex: '#545667'
name: graphite
type: text
decimalPlaces: 0
height: 2
kind: Single_Stat
name: Allocatable
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> filter(fn: (r) => r._field == "allocatable_cpu_cores")
|> last()
|> drop(columns: ["node_name"])
|> sum(column: "_value")
|> yield(name: "last")
width: 1
xPos: 5
yPos: 9
- axes:
- base: "10"
name: y2
scale: linear
- base: "10"
name: x
scale: linear
- base: "10"
name: y
scale: linear
geom: monotoneX
height: 2
kind: Xy
name: Resource Requests CPU
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_pod_container")
|> filter(fn: (r) => r._field == "resource_requests_millicpu_units")
|> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)
|> yield(name: "mean")
shade: true
width: 2
xCol: _time
xPos: 5
yCol: _value
yPos: 11
- colors:
- hex: '#00C9FF'
name: laser
type: text
decimalPlaces: 2
height: 2
kind: Single_Stat
name: K8s Capacity CPUs
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> filter(fn: (r) => r._field == "capacity_cpu_cores")
|> last()
|> drop(columns: ["node_name"])
|> sum(column: "_value")
|> yield(name: "last")
width: 1
xPos: 6
yPos: 9
- colors:
- hex: '#7A65F2'
name: star
type: text
decimalPlaces: 2
height: 1
kind: Single_Stat
name: Allocatable memory
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> filter(fn: (r) => r._field == "allocatable_memory_bytes")
|> last()
|> map(fn: (r) => ({r with _value: r._value / 1073741824}))
|> drop(columns: ["node_name"])
|> sum(column: "_value")
suffix: ' GB'
width: 1
xPos: 6
yPos: 13
- height: 3
kind: Markdown
name: Name this Cell
note: |-
## Legend
**The dashboard shows Kubernetes metrics based on the `kube inventory` APIs.**
Collected via `Telegraf kube inventory plugin`.
Note: Make sure you have selected `v.bucket` variable.
width: 5
xPos: 7
- height: 1
kind: Markdown
name: Name this Cell
note: '## DaemonSets'
width: 5
xPos: 7
yPos: 3
- axes:
- base: "10"
name: y2
scale: linear
- base: "10"
name: x
scale: linear
- base: "10"
name: y
scale: linear
geom: line
height: 5
kind: Xy
name: DaemonSets
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_daemonset")
|> filter(fn: (r) => r._field == "number_ready" or r._field == "number_available" or r._field == "number_unavailable" or r._field == "updated_number_scheduled" or r._field == "number_misscheduled")
|> group(columns: ["daemonset_name","_field"])
width: 5
xCol: _time
xPos: 7
yCol: _value
yPos: 4
- height: 1
kind: Markdown
name: Name this Cell
note: '## Resources'
width: 5
xPos: 7
yPos: 9
- axes:
- base: "10"
name: y
scale: linear
- base: "10"
name: y2
scale: linear
- base: "10"
name: x
scale: linear
geom: line
height: 3
kind: Xy
name: Resource limit milliscpu
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_pod_container" and r._field == "resource_limits_millicpu_units")
|> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)
shade: true
width: 5
xCol: _time
xPos: 7
yCol: _value
yPos: 10
- height: 1
kind: Markdown
name: Name this Cell
note: '## Persistent Volumes Status'
width: 5
xPos: 7
yPos: 13
- colors:
- hex: '#00C9FF'
name: laser
type: text
fieldOptions:
- visible: true
- displayName: result
fieldName: result
visible: true
- displayName: table
fieldName: table
visible: true
- displayName: phase
fieldName: phase
visible: true
- displayName: pv_name
fieldName: pv_name
visible: true
height: 7
kind: Table
name: Persistent volumes status
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_persistentvolume")
|> filter(fn: (r) => r._field == "phase_type")
|> group(columns: ["host", "_measurement", "pv_name", "phase" ,"node_name"], mode:"by")
|> last()
|> keep(columns: ["pv_name", "phase"])
tableOptions:
verticalTimeAxis: true
timeFormat: YYYY-MM-DD HH:mm:ss
width: 5
xPos: 7
yPos: 14
description: From Telegraf inputs.kube_inventory plugin - A collection of useful
visualizations for monitoring your Kubernetes Control Plane
name: Kubernetes Inventory
---
apiVersion: influxdata.com/v2alpha1
kind: Dashboard
metadata:
name: eager-swartz-df8001
spec:
associations:
- kind: Label
name: reverent-ardinghelli-9f8005
- kind: Label
name: thirsty-matsumoto-9f8003
charts:
- height: 1
kind: Markdown
name: Name this Cell
note: '# Nodes Overview'
width: 7
- colors:
- hex: '#545667'
name: graphite
type: background
decimalPlaces: 2
fieldOptions:
- displayName: _time
fieldName: _time
- displayName: Name
fieldName: _value
visible: true
height: 3
kind: Table
name: Current and Past Nodes
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
//|> filter(fn: (r) => r._field == "cpu_usage_nanocores")
|> last()
|> keep(columns: ["node_name", "_time"])
|> rename(columns: {node_name: "_value"})
|> group(columns: ["node_name"], mode:"by")
|> unique(column: "_value")
// |> sort(columns: "_time")
tableOptions:
sortBy: _time
verticalTimeAxis: true
timeFormat: YYYY/MM/DD HH:mm:ss
width: 5
yPos: 1
- height: 1
kind: Markdown
name: Name this Cell
note: '# CPU Metrics'
width: 5
yPos: 4
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
label: nanocores
name: y
scale: linear
colors:
- hex: '#31C0F6'
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
name: Nineteen Eighty Four
type: scale
geom: monotoneX
height: 5
kind: Xy
name: CPU Usage
position: overlaid
queries:
- query: "import \"regexp\"\nfrom(bucket: v.bucket)\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\n |> filter(fn: (r) => r._measurement == \"kubernetes_node\")\n
\ |> filter(fn: (r) => r._field == \"cpu_usage_nanocores\")\n// |>
map(fn: (r) => ({ r with node_name: \n// regexp.replaceAllString(r:
/placelongnamehere/, v: r.node_name, t: \"..\")\n// }))\n |> group(columns:
[\"node_name\"] )\n |> aggregateWindow(every: v.windowPeriod, fn:
last)\n |> yield(name: \"last\")"
width: 5
xCol: _time
yCol: _value
yPos: 5
- height: 1
kind: Markdown
name: Name this Cell
note: '# Memory Consumption Metrics'
width: 7
yPos: 10
- axes:
- base: "10"
name: x
scale: linear
- base: "10"
label: bytes
name: y
scale: linear
colors:
- hex: '#31C0F6'
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
name: Nineteen Eighty Four
type: scale
geom: monotoneX
height: 5
kind: Xy
name: Memory Usage Bytes
queries:
- query: "import \"regexp\"\nfrom(bucket: v.bucket)\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\n |> filter(fn: (r) => r._measurement == \"kubernetes_node\")\n
\ |> filter(fn: (r) => r._field == \"memory_usage_bytes\")\n |> keep(columns:
[\"_time\",\"_value\",\"node_name\"])\n |> aggregateWindow(every:
v.windowPeriod, fn: last)\n// |> map(fn: (r) => ({ r with node_name:
\n// regexp.replaceAllString(r: /placelongnamehere/, v: r.node_name,
t: \"..\")\n// }))\n |> yield(name: \"last\")"
width: 5
yPos: 11
- height: 1
kind: Markdown
name: Name this Cell
note: '# Filesystem Metrics'
width: 7
yPos: 16
- axes:
- base: "10"
name: x
scale: linear
- base: "10"
name: y
scale: linear
colors:
- hex: '#31C0F6'
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
name: Nineteen Eighty Four
type: scale
geom: line
height: 5
kind: Xy
name: Filesystem capacity / used
position: overlaid
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> filter(fn: (r) => r._field == "fs_used_bytes" or r._field == "fs_capacity_bytes")
|> aggregateWindow(every: v.windowPeriod, fn: last)
|> yield(name: "last")
width: 7
xCol: _time
yCol: _value
yPos: 17
- height: 1
kind: Markdown
name: Name this Cell
note: '# Network Consumption Metrics'
width: 7
yPos: 22
- axes:
- base: "10"
name: x
scale: linear
- base: "10"
label: Rate
name: y
scale: linear
colors:
- hex: '#31C0F6'
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
name: Nineteen Eighty Four
type: scale
geom: line
height: 4
kind: Xy
name: Network tx/rx
position: overlaid
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> filter(fn: (r) => r._field == "network_rx_bytes" or r._field == "network_tx_bytes")
|> aggregateWindow(every: v.windowPeriod, fn: last)
|> yield(name: "last")
width: 7
xCol: _time
yCol: _value
yPos: 23
- colors:
- hex: '#545667'
name: graphite
type: text
- hex: '#4ED8A0'
name: rainforest
type: text
value: 2
decimalPlaces: 2
height: 3
kind: Single_Stat
name: Node Count
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> keep(columns: ["node_name"])
|> rename(columns: {node_name: "_value"})
|> group(columns: ["table"], mode:"by")
|> unique() |> count()
width: 2
xPos: 5
yPos: 1
- colors:
- hex: '#545667'
name: graphite
type: text
- hex: '#545667'
name: graphite
type: text
value: 100
decimalPlaces: 2
height: 1
kind: Single_Stat
name: Pods capacity
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> filter(fn: (r) => r._field == "capacity_pods")
|> last()
width: 1
xPos: 5
yPos: 4
- colors:
- hex: '#383846'
name: pepper
type: text
- hex: '#4591ED'
name: ocean
type: text
value: 2
decimalPlaces: 0
height: 2
kind: Single_Stat
name: Allocatable CPU Cores
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> filter(fn: (r) => r._field == "allocatable_cpu_cores")
|> aggregateWindow(every: v.windowPeriod, fn: last)
|> yield(name: "last")
width: 1
xPos: 5
yPos: 5
- axes:
- name: x
binCount: 30
colors:
- hex: '#31C0F6'
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
name: Nineteen Eighty Four
type: scale
height: 3
kind: Histogram
name: Nanocore Distribution By Node
position: stacked
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> filter(fn: (r) => r._field == "cpu_usage_nanocores")
|> group(columns: ["node_name"], mode:"by")
width: 2
xCol: _value
xPos: 5
yPos: 7
- axes:
- name: x
binCount: 30
colors:
- hex: '#31C0F6'
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
name: Nineteen Eighty Four
type: scale
height: 5
kind: Histogram
name: Mem Distribution
position: stacked
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> filter(fn: (r) => r._field == "memory_usage_bytes")
|> keep(columns: ["_time","_value", "node_name"])
|> aggregateWindow(every: v.windowPeriod, fn: last)
|> yield(name: "last")
width: 2
xCol: _value
xPos: 5
yPos: 11
- colors:
- hex: '#383846'
name: pepper
type: text
decimalPlaces: 2
height: 1
kind: Single_Stat
name: Host Count
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> keep(columns: ["host"])
|> rename(columns: {host: "_value"})
|> group(columns: ["table"], mode:"by")
|> unique() |> count()
width: 1
xPos: 6
yPos: 4
- colors:
- hex: '#545667'
name: graphite
type: text
decimalPlaces: 2
height: 2
kind: Single_Stat
name: Average CPU Capacity
queries:
- query: |-
from(bucket: v.bucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r._measurement == "kubernetes_node")
|> filter(fn: (r) => r._field == "capacity_cpu_cores")
|> aggregateWindow(every: v.windowPeriod, fn: last)
|> movingAverage(n: 1)
width: 1
xPos: 6
yPos: 5
- height: 27
kind: Markdown
name: Name this Cell
note: |
# Kubernetes Node Metrics Legend
**This dashboard can be used to display Kubernetes Node metrics. The K8S infrastrucure supports Google Cloud Platform.**
[Download](https://github.com/influxdata/telegraf/tree/master/plugins/inputs/kubernetes) and install Telegraf `Kubernetes` input plugin to your monitoring namespace to collect data. The measurement `kubernetes_node` is used. Set `v.bucket` variable.
### Basic K8S Concepts
A K8S `cluster` consists of a set of worker machines called nodes. A `node` may be a VM or physical machine, depending on the cluster. Nodes host pods. `Pod` is a set of running containers.
### Application Description
The monitored application consists of:
1. Microservices providing REST APIs. Example technology stack can be based on eg "NodeJs > MySQL / Mongo app framework".
2. Application provides administration web UI.
3. Influxdata monitoring infrastructure, based on Telegraf plugins.
Telegraf note: host = node; and in Telegraf the meaning of host = pod hostname
### CPU Units and Metrics
| Unit | Description |
| ---- | ----------- |
| Nanocore | 1024 M = 1G = 1 CPU |