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{ | |
"type": "timeseries", | |
"name": "orders-weather", | |
"default_datasource": "influx", | |
"max_evals": 50, | |
"bucket_interval": "1h", | |
"span": "auto", | |
"offset": 30, | |
"interval": "24h", |
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{ | |
"type": "timeseries", | |
"name": "orders", | |
"default_datasource": "influx", | |
"max_evals": 50, | |
"bucket_interval": "24h", | |
"span": "auto", | |
"offset": 30, | |
"interval": 60, |
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select temp, clouds from weather where time >='2015-06-30T00:00:00Z' limit 5 | |
name: weather | |
time temp clouds | |
---- ---- ------ | |
1435622400000000000 18.33000000000004 0 | |
1435626000000000000 17.760000000000048 0 | |
1435629600000000000 16.439999999999998 0 | |
1435633200000000000 15.840000000000032 0 | |
1435636800000000000 15.340000000000032 0 |
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select * from orders where time >='2015-06-30T00:00:00Z' limit 5 | |
name: orders | |
time count | |
---- ----- | |
1435622400000000000 388 | |
1435626000000000000 384 | |
1435629600000000000 375 | |
1435633200000000000 371 | |
1435636800000000000 368 |
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var model = 'telegraf_metrics_count_value_10s' | |
var from_measurement = 'loudml' | |
var out_db = 'telegraf' | |
var retention_policy = 'autogen' | |
var out_measurement = 'predicted' |
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var pos = data | |
|changeDetect('is_anomaly') | |
|alert() | |
.warn(lambda: "is_anomaly" == TRUE) | |
.crit(lambda: "is_anomaly" == TRUE AND "score" > 90.0) | |
.message('{{if ne .Level "OK" }} Hey, unexpected situation detected score={{ index .Fields "score" | printf "%0.3f" }} {{else}} Back to normal {{end}}') | |
.pagerDuty2() |
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data_dir = "/var/lib/kapacitor" | |
[pagerduty2] | |
enabled = true | |
routing-key = "" | |
url = "https://events.pagerduty.com/v2/enqueue" | |
global = false | |
[logging] | |
# Destination for logs |
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var neg = data | |
|stateCount(lambda: "is_anomaly" == FALSE) | |
|alert() | |
.info(lambda: "state_count" == 1) | |
.message('{{ .Time }}: situation back to normal model={{ index .Fields "model" }} state=finished score={{ index .Fields "score" | printf "%0.3f" }}.') | |
.slack() |
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var pos = data | |
// the next node counts abnormal data points | |
|stateCount(lambda: "is_anomaly" == TRUE) | |
|alert() | |
// Warn after 1 point | |
.warn(lambda: "state_count" >= 1) | |
// Critical after 5 points and abnormal higher than 90 / 100 | |
.crit(lambda: "state_count" >= 5 AND "score" > 90.0) | |
.message('{{ .Time }}: Hey, unexpected situation detected by model={{ index .Fields "model" }} state=ongoing score={{ index .Fields "score" | printf "%0.3f" }}.') | |
.slack() |
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var data = stream | |
// this from node selects all input data received in the given measurement | |
|from() | |
.measurement(from_measurement) | |
.where(lambda: "model" == model) | |
// this eval node remembers the model name | |
|eval(lambda: model) | |
.as('model') | |
.keep() | |
// the next five lines save the data to the target database into a new measurement |
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