startree-drift-discrete-query-dx

Description

Detect an anomaly if the distribution drift metric of a continuous variable is above a maximum threshold. Use the enumeratorQuery property to feed in a query that outputs different dimensions to explore.

Flowchart

Parameters

DATA

name description default value

aggregationColumn

The column to aggregate. Can be a derived metric.

–

dataSource

The Pinot datasource to use.

–

dataset

The dataset to query.

–

monitoringGranularity

The period of aggregation of the timeseries. In ISO-8601 format. Example: PT1H.

–

timezone

Timezone used to group by time. In TZ-identifier(opens in a new tab) format.

For instance, UTC or US/Pacific.

UTC

timeColumn

TimeColumn used to group by time. If set to AUTO (the default value), the Pinot primary time column is used.

AUTO

timeColumnFormat

Required if timeColumn is not AUTO. Learn more(opens in a new tab)

completenessDelay

The time for your data to be considered complete and ready for anomaly detection. In ISO-8601 format. Example: PT2H. Learn more(opens in a new tab)

P0D

queryFilters

Filters to apply when fetching data. Prefix with AND. Example: AND country='US'

${queryFilters}

queryLimit

Maximum number of timeseries point to fetch.

100000001

DETECTION

name description default value

lookback

Historical time period to use to train the model. In ISO-8601 format. Example: P21D.

P21D

baselineOffset

A list of baseline offsets in ISO-8601 format to use when computing the distribution drift. Eg [P7D, P14D, P21D] will compute the drift between the current distribution and the distribution 1 week, 2 weeks, and 3 weeks ago. The final score is the median of the drifts.

–

distance

The distribution drift distance to use.

–

max

If the current value is above this threshold, detect an anomaly.

–

min

If the current value is below this threshold, detect an anomaly.

FILTER

Time of week

name description default value

name	description	default value
daysOfWeek	Used to ignore anomalies that happen at specific time periods. A list of days. Anomalies happening on these days are ignored if timeOfWeekIgnore is true. Example: `["MONDAY", "SUNDAY"]`.	[]
hoursOfDay	Used to ignore anomalies that happen at specific time periods. A list of hours. Anomalies happening on these hours are ignored. Example: `[0,1,2,23]`	[]
dayHoursOfWeek	Used to ignore anomalies that happen at specific time periods. A mapping of `{DAY: [hours]}`. Anomalies happening on these timeframes are ignored if timeOfWeekIgnore is true. Example: `{"FRIDAY": [22, 23], "SATURDAY": [0, 1, 2]}`	{}

daysOfWeek

Used to ignore anomalies that happen at specific time periods. A list of days. Anomalies happening on these days are ignored if timeOfWeekIgnore is true. Example: ["MONDAY", "SUNDAY"].

[]

hoursOfDay

Used to ignore anomalies that happen at specific time periods. A list of hours. Anomalies happening on these hours are ignored. Example: [0,1,2,23]

[]

dayHoursOfWeek

Used to ignore anomalies that happen at specific time periods. A mapping of {DAY: [hours]}. Anomalies happening on these timeframes are ignored if timeOfWeekIgnore is true. Example: {"FRIDAY": [22, 23], "SATURDAY": [0, 1, 2]}

{}

Guardrail metric

name description default value

name	description	default value
guardrailMetricMin	Used to ignore anomalies that don’t meet the guardrail threshold. Minimum threshold of the guardrail metric. If `-1`, no minimum threshold is applied.	-1
guardrailMetricMax	Used to ignore anomalies that don’t meet the guardrail threshold. Maximum threshold of guardrailMetric. If `-1`, no maximum threshold is applied.	-1
guardrailMetric	Used to ignore anomalies that don’t meet the guardrail threshold. Metric to use as a threshold guardrail. Example: `COUNT(*)` and set `guardrailMetricMin = 100` to ignore anomalies detected when there is less than 100 observations in the period.	COUNT(*)

guardrailMetricMin

Used to ignore anomalies that don’t meet the guardrail threshold. Minimum threshold of the guardrail metric. If -1, no minimum threshold is applied.

-1

guardrailMetricMax

Used to ignore anomalies that don’t meet the guardrail threshold. Maximum threshold of guardrailMetric. If -1, no maximum threshold is applied.

-1

guardrailMetric

Used to ignore anomalies that don’t meet the guardrail threshold. Metric to use as a threshold guardrail. Example: COUNT(*) and set guardrailMetricMin = 100 to ignore anomalies detected when there is less than 100 observations in the period.

COUNT(*)

Special events

name description default value

eventFilterSqlFilter

Used to ignore anomalies that happen during events. Sql filter to apply on the events. Learn more

eventFilterLookaround

Used to ignore anomalies that happen during events. Offset to apply on startTime and endTime to look around the timeframe. In ISO-8601 format. Example: P1D.

P2D

eventFilterTypes

Used to ignore anomalies that happen during events. List of event types to fetch by. Example: ["HOLIDAY", "DEPLOYMENT"]. [] fetches all events. Use ["__NO_EVENTS"] to disable.

[‘__NO_EVENTS’]

eventFilterBeforeEventMargin

Used to ignore anomalies that happen during events. A period in ISO-8601 format that corresponds to a period that is also impacted by the event. Example: if beforeEventMargin is P1D, if event happens on [Dec 24 0:00, Dec 25 0:00[, the label will be applied to anomalies happening on [Dec 23 0:00 and Dec 25 0:00[

P0D

eventFilterAfterEventMargin

Used to ignore anomalies that happen during events. Same as eventFilterBeforeEventMargin at the end of the event.

P0D

POSTPROCESS

Data mutability

name description default value

mutabilityPeriod

Use if your data is mutable. ThirdEye will maintain the detection results up to date on the mutable period. For instance, if your last 10 days of data is mutable, set P10D. At each cron detection job, the detection results for the last 10 days will be updated.

P0D

reNotifyPercentageThreshold

For detection replay when data is mutable. If the percentage difference between an existing anomaly and a new anomaly on the same time frame is above this threshold, renotify. Combined with reNotifyAbsoluteThreshold. Both thresholds must pass to be re-notified. If zero, always renotify. If null or negative, never re-notifies.

-1

reNotifyAbsoluteThreshold

For detection replay when data is mutable. If the absolute difference between an existing anomaly and a new anomaly on the same time frame is above this threshold, renotify. Combined with reNotifyPercentageThreshold. Both thresholds must pass to be re-notified. If zero, always renotify. If null or negative, never re-notifies.

-1

Anomaly merger

name	description	default value
mergeMaxGap	Maximum gap between 2 anomalies for anomalies to be merged. In ISO-8601 format. Example: `PT2H`. The default behavior is to merge consecutive anomalies only. To disable anomaly merging entirely, set this value to `P0D`.
mergeMaxDuration	Maximum duration of an anomaly merger. At merge time, if an anomaly merger would get bigger than this limit, the anomalies are not merged. In ISO-8601 format. Example: `P7D`.

RCA

name

description

default value

rcaAggregationFunction

The aggregation function to use for RCA. If the detection metric name is known to ThirdEye, this parameter is optional.

rcaIncludedDimensions

List of the dimensions (columns in the dataset) to use in RCA drill-downs. If not set or empty, all dimensions of the table are used. Learn more(opens in a new tab).

[]

rcaExcludedDimensions

List of dimensions (columns in the dataset) to ignore in RCA drill-downs. If not set or empty, all dimensions of the table are used. rcaExcludedDimensions and rcaIncludedDimensions cannot be used at the same time.

[]

rcaEventTypes

A list of type to filter on for RCA. Only events that match such types will be shown in the RCA related events tab. Learn more(opens in a new tab).

[]

rcaEventSqlFilter

A Sql filter for RCA events. Only events that match the filter will be shown in the RCA related events tab. Learn more(opens in a new tab).

DIMENSION_EXPLORATION

name description default value

name	description	default value
enumeratorQuery	This is a SQL query that will run on the data source and build enumeration items from that queryExample: “SELECT DISTINCT country, device from pageviews LIMIT 100”. In this case, the enumerator will generate one enumeration item for each country/device combination.	–
enumerationItemIdKeys	List of keys to use to identify the enumeration. The format is the following: `[` `"queryFilters" ]` The keys must be present in the `params` object of each enumeration. The keys will be used to generate the dimension exploration id. The id will be used to identify the enumeration in the detection pipeline.	[‘queryFilters’]

enumeratorQuery

This is a SQL query that will run on the data source and build enumeration items from that queryExample: “SELECT DISTINCT country, device from pageviews LIMIT 100”. In this case, the enumerator will generate one enumeration item for each country/device combination.

–

enumerationItemIdKeys

List of keys to use to identify the enumeration.

The format is the following:

[


"queryFilters"
]

The keys must be present in the params object of each enumeration.

The keys will be used to generate the dimension exploration id.

The id will be used to identify the enumeration in the detection pipeline.

[‘queryFilters’]