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downsampling_tasks.yml
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apiVersion: influxdata.com/v2alpha1
kind: Task
metadata:
name: downsample-all-1d
spec:
every: 24h
name: 04. downsample_all_1d
query: |-
import "experimental"
fromBucket = "telegraf"
toBucket = "telegraf_" + string(v: task.every)
all_data = from(bucket: fromBucket)
|> range(start: -task.every)
|> filter(fn: (r) =>
(r._measurement =~ /^cpu|disk|diskio|mem|processes|swap|system|internal_.+|net|netstat|procstat|procstat_lookup|smart_.+$/))
numeric_data = all_data
|> filter(fn: (r) =>
(r._field !~ /^uptime_format|health_ok$/))
bool_data = all_data
|> filter(fn: (r) =>
(r._field =~ /^health_ok$/))
numeric_data
|> aggregateWindow(every: task.every, fn: mean)
|> set(key: "aggregate", value: "mean")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> aggregateWindow(every: task.every, fn: min)
|> set(key: "aggregate", value: "min")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> aggregateWindow(every: task.every, fn: max)
|> set(key: "aggregate", value: "max")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> aggregateWindow(every: task.every, fn: sum)
|> set(key: "aggregate", value: "sum")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.999,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p99.9")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.99,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p99")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.95,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p95")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.9,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p90")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
all_bools = bool_data
|> aggregateWindow(every: task.every, fn: count)
true_bools = bool_data
|> filter(fn: (r) =>
(r._value == true))
|> aggregateWindow(every: task.every, fn: count)
experimental.join(left: all_bools, right: true_bools, fn: (left, right) =>
({left with _value: if left._value == 0 then 0.0 else float(v: right._value) / float(v: left._value)}))
|> set(key: "aggregate", value: "ratio")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
all_data
|> aggregateWindow(every: task.every, fn: count)
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> set(key: "aggregate", value: "count")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
all_data
|> aggregateWindow(every: task.every, fn: last)
|> set(key: "aggregate", value: "last")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
---
apiVersion: influxdata.com/v2alpha1
kind: Task
metadata:
name: downsample-all-1h
spec:
every: 1h
name: 03. downsample_all_1h
query: |-
import "experimental"
fromBucket = "telegraf"
toBucket = "telegraf_" + string(v: task.every)
all_data = from(bucket: fromBucket)
|> range(start: -task.every)
|> filter(fn: (r) =>
(r._measurement =~ /^cpu|disk|diskio|mem|processes|swap|system|internal_.+|net|netstat|procstat|procstat_lookup|smart_.+$/))
numeric_data = all_data
|> filter(fn: (r) =>
(r._field !~ /^uptime_format|health_ok$/))
bool_data = all_data
|> filter(fn: (r) =>
(r._field =~ /^health_ok$/))
numeric_data
|> aggregateWindow(every: task.every, fn: mean)
|> set(key: "aggregate", value: "mean")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> aggregateWindow(every: task.every, fn: min)
|> set(key: "aggregate", value: "min")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> aggregateWindow(every: task.every, fn: max)
|> set(key: "aggregate", value: "max")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> aggregateWindow(every: task.every, fn: sum)
|> set(key: "aggregate", value: "sum")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.999,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p99.9")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.99,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p99")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.95,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p95")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.9,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p90")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
all_bools = bool_data
|> aggregateWindow(every: task.every, fn: count)
true_bools = bool_data
|> filter(fn: (r) =>
(r._value == true))
|> aggregateWindow(every: task.every, fn: count)
experimental.join(left: all_bools, right: true_bools, fn: (left, right) =>
({left with _value: if left._value == 0 then 0.0 else float(v: right._value) / float(v: left._value)}))
|> set(key: "aggregate", value: "ratio")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
all_data
|> aggregateWindow(every: task.every, fn: count)
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> set(key: "aggregate", value: "count")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
all_data
|> aggregateWindow(every: task.every, fn: last)
|> set(key: "aggregate", value: "last")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
---
apiVersion: influxdata.com/v2alpha1
kind: Task
metadata:
name: downsample-all-10m
spec:
every: 10m
name: 02. downsample_all_10m
query: |-
import "experimental"
fromBucket = "telegraf"
toBucket = "telegraf_" + string(v: task.every)
all_data = from(bucket: fromBucket)
|> range(start: -task.every)
|> filter(fn: (r) =>
(r._measurement =~ /^cpu|disk|diskio|mem|processes|swap|system|internal_.+|net|netstat|procstat|procstat_lookup|smart_.+$/))
numeric_data = all_data
|> filter(fn: (r) =>
(r._field !~ /^uptime_format|health_ok$/))
bool_data = all_data
|> filter(fn: (r) =>
(r._field =~ /^health_ok$/))
numeric_data
|> aggregateWindow(every: task.every, fn: mean)
|> set(key: "aggregate", value: "mean")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> aggregateWindow(every: task.every, fn: min)
|> set(key: "aggregate", value: "min")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> aggregateWindow(every: task.every, fn: max)
|> set(key: "aggregate", value: "max")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> aggregateWindow(every: task.every, fn: sum)
|> set(key: "aggregate", value: "sum")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.999,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p99.9")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.99,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p99")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.95,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p95")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.9,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p90")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
all_bools = bool_data
|> aggregateWindow(every: task.every, fn: count)
true_bools = bool_data
|> filter(fn: (r) =>
(r._value == true))
|> aggregateWindow(every: task.every, fn: count)
experimental.join(left: all_bools, right: true_bools, fn: (left, right) =>
({left with _value: if left._value == 0 then 0.0 else float(v: right._value) / float(v: left._value)}))
|> set(key: "aggregate", value: "ratio")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
all_data
|> aggregateWindow(every: task.every, fn: count)
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> set(key: "aggregate", value: "count")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
all_data
|> aggregateWindow(every: task.every, fn: last)
|> set(key: "aggregate", value: "last")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
---
apiVersion: influxdata.com/v2alpha1
kind: Task
metadata:
name: downsample-all-1m
spec:
every: 1m
name: 01. downsample_all_1m
query: |-
import "experimental"
fromBucket = "telegraf"
toBucket = "telegraf_" + string(v: task.every)
all_data = from(bucket: fromBucket)
|> range(start: -task.every)
|> filter(fn: (r) =>
(r._measurement =~ /^cpu|disk|diskio|mem|processes|swap|system|internal_.+|net|netstat|procstat|procstat_lookup|smart_.+$/))
numeric_data = all_data
|> filter(fn: (r) =>
(r._field !~ /^uptime_format|health_ok$/))
bool_data = all_data
|> filter(fn: (r) =>
(r._field =~ /^health_ok$/))
numeric_data
|> aggregateWindow(every: task.every, fn: mean)
|> set(key: "aggregate", value: "mean")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> aggregateWindow(every: task.every, fn: min)
|> set(key: "aggregate", value: "min")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> aggregateWindow(every: task.every, fn: max)
|> set(key: "aggregate", value: "max")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> aggregateWindow(every: task.every, fn: sum)
|> set(key: "aggregate", value: "sum")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.999,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p99.9")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.99,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p99")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.95,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p95")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
numeric_data
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> quantile(
column: "_value",
q: 0.9,
method: "estimate_tdigest",
compression: 1000.0,
)
|> set(key: "aggregate", value: "p90")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket, timeColumn: "_stop")
all_bools = bool_data
|> aggregateWindow(every: task.every, fn: count)
true_bools = bool_data
|> filter(fn: (r) =>
(r._value == true))
|> aggregateWindow(every: task.every, fn: count)
experimental.join(left: all_bools, right: true_bools, fn: (left, right) =>
({left with _value: if left._value == 0 then 0.0 else float(v: right._value) / float(v: left._value)}))
|> set(key: "aggregate", value: "ratio")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
all_data
|> aggregateWindow(every: task.every, fn: count)
|> map(fn: (r) =>
({r with _value: float(v: r._value)}))
|> set(key: "aggregate", value: "count")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)
all_data
|> aggregateWindow(every: task.every, fn: last)
|> set(key: "aggregate", value: "last")
|> set(key: "rollup_interval", value: string(v: task.every))
|> to(bucket: toBucket)