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kafka_kubernetes.yml
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apiVersion: influxdata.com/v2alpha1
kind: Label
metadata:
name: hardcore-hertz-2b7001
spec:
color: '#0f0e15'
name: kafka
---
apiVersion: influxdata.com/v2alpha1
kind: Variable
metadata:
name: busy-gould-2b7009
spec:
associations:
- kind: Label
name: hardcore-hertz-2b7001
language: flux
name: kafka_namespace
query: "import \"influxdata/influxdb/schema\"\r\n\r\nschema.tagValues(\r\n bucket:
v.bucket,\r\n tag: \"namespace\",\r\n predicate: (r) => r._measurement ==
\"prometheus\" \r\n and r._field == \"kafka_controller_kafkacontroller_activecontrollercount_value\",\r\n
\ start: v.timeRangeStart\r\n)"
type: query
---
apiVersion: influxdata.com/v2alpha1
kind: Variable
metadata:
name: inspiring-nash-2b7005
spec:
associations:
- kind: Label
name: hardcore-hertz-2b7001
language: flux
name: bucket
query: |-
buckets()
|> filter(fn: (r) => r.name !~ /^_/)
|> rename(columns: {name: "_value"})
|> keep(columns: ["_value"])
type: query
---
apiVersion: influxdata.com/v2alpha1
kind: Dashboard
metadata:
name: stubborn-dewdney-ab7001
spec:
associations:
- kind: Label
name: hardcore-hertz-2b7001
charts:
- height: 1
kind: Markdown
name: Name this Cell
note: '# **General**'
width: 6
- colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
kind: Single_Stat
name: Active controller status
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_controller_kafkacontroller_activecontrollercount_value\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)\r\n
\ |> map(fn: (r) => ({ r with _value: \r\n if string(v: r._value)
== \"1\"\r\n then \"OK\"\r\n else \"KO\"\r\n }))\r\n//
\ |> keep(columns: [\"_start\",\"_stop\",\"_time\",\"_value\"])"
width: 3
yPos: 1
- colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
kind: Single_Stat
name: Unclean leader election
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_controller_controllerstats_uncleanleaderelectionspersec_count\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 1
yPos: 3
- axes:
- base: "10"
name: x
scale: linear
- base: "10"
name: "y"
scale: linear
colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
hoverDimension: auto
kind: Single_Stat_Plus_Line
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: Bytes rate
position: overlaid
queries:
- query: "import \"experimental/aggregate\"\r\nall = from(bucket: v.bucket)\r\n
\ |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |>
filter(fn: (r) => r[\"_measurement\"] == \"prometheus\")\r\n\r\nb_in
= all\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_server_brokertopicmetrics_total_bytesinpersec_count\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> filter(fn: (r) => exists r._value) \r\n |> increase(columns:
[\"_value\"])\r\n |> aggregate.rate(every: 1m, unit: 1s)\r\n\r\nb_out
= all\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_server_brokertopicmetrics_total_bytesoutpersec_count\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> filter(fn: (r) => exists r._value) \r\n |> increase(columns:
[\"_value\"])\r\n |> aggregate.rate(every: 1m, unit: 1s) \r\n\r\n\r\njoin(tables:
{b_in:b_in, b_out:b_out}, on: [\"_time\", \"_start\", \"_stop\"])\r\n
\ |> map(fn: (r) => ({\r\n _time: r._time,\r\n _value: if r._value_b_out
== 0. then r._value_b_in / 1. else r._value_b_in / r._value_b_out\r\n
\ })\r\n )"
width: 3
xCol: _time
yCol: _value
yPos: 5
- axes:
- base: "10"
name: x
scale: linear
- base: "10"
name: "y"
scale: linear
colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
hoverDimension: auto
kind: Single_Stat_Plus_Line
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: Messages IN
position: overlaid
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_server_brokertopicmetrics_total_messagesinpersec_oneminuterate\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xCol: _time
yCol: _value
yPos: 7
- axes:
- base: "10"
name: x
scale: linear
- base: "10"
name: "y"
scale: linear
colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
hoverDimension: auto
kind: Single_Stat_Plus_Line
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: In-sync replicas
position: overlaid
queries:
- query: "import \"experimental/aggregate\"\r\nfrom(bucket: v.bucket)\r\n
\ |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |>
filter(fn: (r) => r[\"_measurement\"] == \"prometheus\")\r\n |> filter(fn:
(r) => r[\"_field\"] == \"kafka_server_replicamanager_total_isrshrinkspersec_count\"
or r[\"_field\"] == \"kafka_server_replicamanager_total_isrexpandspersec_count\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)\r\n
\ |> group(columns: [\"_field\"], mode:\"by\")"
width: 3
xCol: _time
yCol: _value
yPos: 9
- colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
kind: Single_Stat
name: Under replicated partitions
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_topic_partition_under_replicated_partition\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 1
xPos: 1
yPos: 3
- colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
kind: Single_Stat
name: Offline partitions count
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_controller_kafkacontroller_offlinepartitionscount_value\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 1
xPos: 2
yPos: 3
- colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
kind: Single_Stat
name: In-sync replicas shrinks
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_server_replicamanager_total_isrshrinkspersec_count\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)\r\n
\ |> keep(columns: [\"_start\",\"_stop\",\"_time\",\"_value\"])"
width: 1
xPos: 3
yPos: 1
- colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
kind: Single_Stat
name: Kafka brokers
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_brokers\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 1
xPos: 3
yPos: 3
- axes:
- base: "10"
name: x
scale: linear
- base: "10"
name: "y"
scale: linear
colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
hoverDimension: auto
kind: Single_Stat_Plus_Line
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: Number of partitions
position: overlaid
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_server_replicamanager_total_partitioncount_value\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xCol: _time
xPos: 3
yCol: _value
yPos: 5
- axes:
- base: "10"
name: x
scale: linear
- base: "10"
name: "y"
scale: linear
colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
hoverDimension: auto
kind: Single_Stat_Plus_Line
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: Bytes IN
position: overlaid
queries:
- query: "import \"experimental/aggregate\"\r\nfrom(bucket: v.bucket)\r\n
\ |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |>
filter(fn: (r) => r[\"_measurement\"] == \"prometheus\")\r\n |> filter(fn:
(r) => r[\"_field\"] == \"kafka_server_brokertopicmetrics_total_bytesinpersec_count\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> filter(fn: (r) => exists r._value)\r\n |> increase(columns:
[\"_value\"])\r\n |> aggregate.rate(every: 1m, unit: 1s)"
width: 3
xCol: _time
xPos: 3
yCol: _value
yPos: 7
- axes:
- base: "10"
name: x
scale: linear
- base: "10"
name: "y"
scale: linear
colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
hoverDimension: auto
kind: Single_Stat_Plus_Line
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: Bytes OUT
position: overlaid
queries:
- query: "import \"experimental/aggregate\"\r\nfrom(bucket: v.bucket)\r\n
\ |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |>
filter(fn: (r) => r[\"_measurement\"] == \"prometheus\")\r\n |> filter(fn:
(r) => r[\"_field\"] == \"kafka_server_brokertopicmetrics_total_bytesoutpersec_count\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> filter(fn: (r) => exists r._value)\r\n |> increase(columns:
[\"_value\"])\r\n |> aggregate.rate(every: 1m, unit: 1s)"
width: 3
xCol: _time
xPos: 3
yCol: _value
yPos: 9
- colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
kind: Single_Stat
name: In-sync replicas expands
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_server_replicamanager_total_isrexpandspersec_count\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)\r\n
\ |> keep(columns: [\"_start\",\"_stop\",\"_time\",\"_value\"])"
width: 1
xPos: 4
yPos: 1
- colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
kind: Single_Stat
name: Total CPUs
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"java_lang_operatingsystem_availableprocessors\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
suffix: ' vCPU'
width: 1
xPos: 4
yPos: 3
- colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 0
height: 2
kind: Single_Stat
name: Partition count
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_server_replicamanager_total_partitioncount_value\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 1
xPos: 5
yPos: 1
- colors:
- hex: '#ffffff'
id: base
name: white
type: text
decimalPlaces: 1
height: 2
kind: Single_Stat
name: Total memory
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"java_lang_operatingsystem_totalphysicalmemorysize\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)\r\n
\ |> map(fn: (r) => ({ r with _value: r._value / 1073741824. }))"
suffix: ' GB'
width: 1
xPos: 5
yPos: 3
- height: 1
kind: Markdown
name: Name this Cell
note: '# **jvm**'
width: 6
xPos: 6
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: "y"
scale: linear
colors:
- hex: '#31C0F6'
id: 6526c1d4-27ba-42fe-a9a2-078f08b7d80b
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
id: e54907d5-919f-45f0-b795-dddd98855cbe
name: Nineteen Eighty Four
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id: 4fcd23a4-1f07-4929-9770-d867f657f9ab
name: Nineteen Eighty Four
type: scale
geom: line
height: 2
hoverDimension: auto
kind: Xy
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: JVM memory
position: overlaid
queries:
- query: "import \"experimental/aggregate\"\r\nfrom(bucket: v.bucket)\r\n
\ |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |>
filter(fn: (r) => r[\"_measurement\"] == \"prometheus\")\r\n |> filter(fn:
(r) => r[\"_field\"] == \"java_lang_memory_heapmemoryusage_used\"
or r[\"_field\"] == \"java_lang_memory_heapmemoryusage_max\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xCol: _time
xPos: 6
yCol: _value
yPos: 1
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: "y"
scale: linear
colors:
- hex: '#31C0F6'
id: 6526c1d4-27ba-42fe-a9a2-078f08b7d80b
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
id: e54907d5-919f-45f0-b795-dddd98855cbe
name: Nineteen Eighty Four
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type: scale
geom: line
height: 2
hoverDimension: auto
kind: Xy
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: CPU load
position: overlaid
queries:
- query: "import \"experimental/aggregate\"\r\nfrom(bucket: v.bucket)\r\n
\ |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |>
filter(fn: (r) => r[\"_measurement\"] == \"prometheus\")\r\n |> filter(fn:
(r) => r[\"_field\"] == \"java_lang_operatingsystem_systemcpuload\"
or r[\"_field\"] == \"java_lang_operatingsystem_processcpuload\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xCol: _time
xPos: 6
yCol: _value
yPos: 3
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: "y"
scale: linear
colors:
- hex: '#31C0F6'
id: 6526c1d4-27ba-42fe-a9a2-078f08b7d80b
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
id: e54907d5-919f-45f0-b795-dddd98855cbe
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
id: 4fcd23a4-1f07-4929-9770-d867f657f9ab
name: Nineteen Eighty Four
type: scale
geom: line
height: 2
hoverDimension: auto
kind: Xy
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: System memory
position: overlaid
queries:
- query: "import \"experimental/aggregate\"\r\nfrom(bucket: v.bucket)\r\n
\ |> range(start: v.timeRangeStart, stop: v.timeRangeStop)\r\n |>
filter(fn: (r) => r[\"_measurement\"] == \"prometheus\")\r\n |> filter(fn:
(r) => r[\"_field\"] == \"java_lang_operatingsystem_totalphysicalmemorysize\"
\r\n or r[\"_field\"] == \"java_lang_operatingsystem_processcpuload\"\r\n
\ or r[\"_field\"] == \"java_lang_operatingsystem_freephysicalmemorysize\"\r\n
\ or r[\"_field\"] == \"java_lang_operatingsystem_totalswapspacesize\"\r\n
\ or r[\"_field\"] == \"java_lang_operatingsystem_freeswapspacesize\"\r\n
\ or r[\"_field\"] == \"java_lang_operatingsystem_committedvirtualmemorysize\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xCol: _time
xPos: 6
yCol: _value
yPos: 5
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: "y"
scale: linear
colors:
- hex: '#31C0F6'
id: 6526c1d4-27ba-42fe-a9a2-078f08b7d80b
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
id: e54907d5-919f-45f0-b795-dddd98855cbe
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
id: 4fcd23a4-1f07-4929-9770-d867f657f9ab
name: Nineteen Eighty Four
type: scale
geom: line
height: 2
hoverDimension: auto
kind: Xy
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: System load
position: overlaid
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"java_lang_operatingsystem_systemloadaverage\"
\r\n or r[\"_field\"] == \"java_lang_operatingsystem_availableprocessors\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xCol: _time
xPos: 6
yCol: _value
yPos: 7
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: "y"
scale: linear
colors:
- hex: '#31C0F6'
id: 6526c1d4-27ba-42fe-a9a2-078f08b7d80b
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
id: e54907d5-919f-45f0-b795-dddd98855cbe
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
id: 4fcd23a4-1f07-4929-9770-d867f657f9ab
name: Nineteen Eighty Four
type: scale
geom: line
height: 2
hoverDimension: auto
kind: Xy
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: Open file descriptor
position: overlaid
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"java_lang_operatingsystem_openfiledescriptorcount\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xCol: _time
xPos: 6
yCol: _value
yPos: 9
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: "y"
scale: linear
colors:
- hex: '#31C0F6'
id: 6526c1d4-27ba-42fe-a9a2-078f08b7d80b
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
id: e54907d5-919f-45f0-b795-dddd98855cbe
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
id: 4fcd23a4-1f07-4929-9770-d867f657f9ab
name: Nineteen Eighty Four
type: scale
geom: line
height: 2
hoverDimension: auto
kind: Xy
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: Idle network processors
position: overlaid
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_network_processor_idlepercent_value\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xCol: _time
xPos: 6
yCol: _value
yPos: 11
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: "y"
scale: linear
colors:
- hex: '#31C0F6'
id: 6526c1d4-27ba-42fe-a9a2-078f08b7d80b
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
id: e54907d5-919f-45f0-b795-dddd98855cbe
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
id: 4fcd23a4-1f07-4929-9770-d867f657f9ab
name: Nineteen Eighty Four
type: scale
geom: line
height: 2
hoverDimension: auto
kind: Xy
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: Class loading
position: overlaid
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"java_lang_classloading_loadedclasscount\"\r\n
\ or r[\"_field\"] == \"java_lang_classloading_loadedclasscount_total\"\r\n
\ or r[\"_field\"] == \"java_lang_classloading_unloadedclasscount_total\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xCol: _time
xPos: 9
yCol: _value
yPos: 1
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: "y"
scale: linear
colors:
- hex: '#31C0F6'
id: 6526c1d4-27ba-42fe-a9a2-078f08b7d80b
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
id: e54907d5-919f-45f0-b795-dddd98855cbe
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
id: 4fcd23a4-1f07-4929-9770-d867f657f9ab
name: Nineteen Eighty Four
type: scale
geom: line
height: 2
hoverDimension: auto
kind: Xy
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: Threads
position: overlaid
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"java_lang_threading_threadcount\"\r\n
\ or r[\"_field\"] == \"java_lang_threading_daemonthreadcount\"\r\n
\ or r[\"_field\"] == \"java_lang_threading_peakthreadcount\"\r\n
\ or r[\"_field\"] == \"java_lang_threading_totalstartedthreadcount\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xCol: _time
xPos: 9
yCol: _value
yPos: 3
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: "y"
scale: linear
colors:
- hex: '#31C0F6'
id: 6526c1d4-27ba-42fe-a9a2-078f08b7d80b
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
id: e54907d5-919f-45f0-b795-dddd98855cbe
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
id: 4fcd23a4-1f07-4929-9770-d867f657f9ab
name: Nineteen Eighty Four
type: scale
geom: line
height: 2
hoverDimension: auto
kind: Xy
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: Number of requests
position: overlaid
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_network_requestmetrics_requestspersec_count\"\r\n
\ or r[\"_field\"] == \"kafka_network_requestmetrics_requestspersec_count\"\r\n
\ or r[\"_field\"] == \"kafka_network_requestmetrics_requestspersec_count\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xCol: _time
xPos: 9
yCol: _value
yPos: 5
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: "y"
scale: linear
colors:
- hex: '#31C0F6'
id: 6526c1d4-27ba-42fe-a9a2-078f08b7d80b
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
id: e54907d5-919f-45f0-b795-dddd98855cbe
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
id: 4fcd23a4-1f07-4929-9770-d867f657f9ab
name: Nineteen Eighty Four
type: scale
geom: line
height: 2
hoverDimension: auto
kind: Xy
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: Purgatory delayed fetch operations
position: overlaid
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_server_delayedoperationpurgatory_numdelayedoperations_fetch_value\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xCol: _time
xPos: 9
yCol: _value
yPos: 7
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: "y"
scale: linear
colors:
- hex: '#31C0F6'
id: 6526c1d4-27ba-42fe-a9a2-078f08b7d80b
name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
id: e54907d5-919f-45f0-b795-dddd98855cbe
name: Nineteen Eighty Four
type: scale
- hex: '#FF7E27'
id: 4fcd23a4-1f07-4929-9770-d867f657f9ab
name: Nineteen Eighty Four
type: scale
geom: line
height: 2
hoverDimension: auto
kind: Xy
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: Log size
position: overlaid
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_log_log_size\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xPos: 9
yPos: 9
- axes:
- base: "10"
name: x
scale: linear
- base: "2"
name: "y"
scale: linear
colors:
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name: Nineteen Eighty Four
type: scale
- hex: '#A500A5'
id: e54907d5-919f-45f0-b795-dddd98855cbe
name: Nineteen Eighty Four
type: scale
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id: 4fcd23a4-1f07-4929-9770-d867f657f9ab
name: Nineteen Eighty Four
type: scale
geom: line
height: 2
hoverDimension: auto
kind: Xy
legendColorizeRows: true
legendOpacity: 1
legendOrientationThreshold: 1e+08
name: Idle request handler threads
position: overlaid
queries:
- query: "from(bucket: v.bucket)\r\n |> range(start: v.timeRangeStart,
stop: v.timeRangeStop)\r\n |> filter(fn: (r) => r[\"_measurement\"]
== \"prometheus\")\r\n |> filter(fn: (r) => r[\"_field\"] == \"kafka_server_kafkarequesthandlerpool_total_requesthandleravgidlepercent_oneminuterate\")\r\n
\ |> filter(fn: (r) => r[\"namespace\"] == v.kafka_namespace)\r\n
\ |> aggregateWindow(every: 1m, fn: last)\r\n |> filter(fn: (r) =>
exists r._value)\r\n |> fill(column: \"_value\", usePrevious: true)"
width: 3
xCol: _time
xPos: 9
yCol: _value
yPos: 11
description: 'Single dashboard for measuring Kafka performance. It uses Prometheus
plugin in Telegraf as an input data provider. '
name: Kafka Kubernetes
---
apiVersion: influxdata.com/v2alpha1
kind: Telegraf
metadata:
name: elastic-kapitsa-ab7005
spec:
associations:
- kind: Label
name: hardcore-hertz-2b7001
config: |
# Configuration for telegraf agent
[agent]
## Default data collection interval for all inputs
interval = "10s"
## Rounds collection interval to 'interval'
## ie, if interval="10s" then always collect on :00, :10, :20, etc.
round_interval = true
## Telegraf will send metrics to outputs in batches of at most
## metric_batch_size metrics.
## This controls the size of writes that Telegraf sends to output plugins.
metric_batch_size = 1000