monitor_schema.models

Console script for monitor_schema.

Subpackages

• monitor_schema.models.analyzer

Submodules

• monitor_schema.models.column_schema
• monitor_schema.models.commons
• monitor_schema.models.document
• monitor_schema.models.monitor
• monitor_schema.models.segments
• monitor_schema.models.utils

Classes

DatasetMetric	Metrics that are applicable at the dataset level.
SimpleColumnMetric	Simple column metrics that are basically just a single number.
ComplexMetrics	Sketch-based metrics that can only be processed by certain algorithms.
Analyzer	Configuration for running an analysis.
BaselineType	Supported baseline types.
ReferenceProfileId	A baseline based on a static reference profile.
TimeRangeBaseline	A static time range.
TrailingWindowBaseline	A dynamic trailing window.
SingleBatchBaseline	Using current batch.
DriftConfig	An analyzer using stddev for a window of time range.
DiffConfig	Detecting the differences between two numerical metrics.
ComparisonConfig	Compare whether the target against either an expect value or against the baseline.
ExperimentalConfig	Experimental algorithm that is not standardized by the above ones yet.
FixedThresholdsConfig	Fixed threshold analysis.
ColumnListChangeConfig	Compare whether the target is equal to a value or not.
SeasonalConfig	An analyzer using stddev for a window of time range.
StddevConfig	Calculates upper bounds and lower bounds based on stddev from a series of numbers.
DatasetMatrix	Define the matrix of fields and segments to fan out for monitoring.
ColumnMatrix	Define the matrix of columns and segments to fan out for monitoring.
TargetLevel	Which nested level we are targeting.
Metadata	Metadata for a top-level objects such as monitors, analyzers, and schema.
Segment	A segment is a list of tags.
ColumnDiscreteness	Classifying the type.
ColumnDataType	Options for configuring data type for a column.
ColumnSchema	Schema configuration for a column.
WeightConfig	Object that specifies column weights.
SegmentWeightConfig	Object that specifies column weights for a segment.
EntitySchema	Schema definition of an entity.
ImmediateSchedule	Schedule the monitor to run immediately.
CronSchedule	Support for scheduling.
Cadence	Cadence for an analyzer or monitor run.
FixedCadenceSchedule	Support for scheduling based on a predefined cadence.
Metadata	Metadata for a top-level objects such as monitors, analyzers, and schema.
Analyzer	Configuration for running an analysis.
EntitySchema	Schema definition of an entity.
Metadata	Metadata for a top-level objects such as monitors, analyzers, and schema.
Monitor	Customer specified monitor configs.
Granularity	Supported granularity.
Document	The main document that dictates how the monitor should be run. This document is managed by WhyLabs internally.
CronSchedule	Support for scheduling.
FixedCadenceSchedule	Support for scheduling based on a predefined cadence.
ImmediateSchedule	Schedule the monitor to run immediately.
Metadata	Metadata for a top-level objects such as monitors, analyzers, and schema.
GlobalAction	Actions that are configured at the team/organization level.
SendEmail	Action to send an email.
SlackWebhook	Action to send a Slack webhook.
RawWebhook	Action to send a Slack webhook.
AnomalyFilter	Filter the anomalies based on certain criteria. If the alerts are filtered down to 0, the monitor won't fire.
EveryAnomalyMode	Config mode that indicates the monitor will send out individual messages per anomaly.
DigestMode	Config mode that indicates the monitor will send out a digest message.
Monitor	Customer specified monitor configs.
SegmentTag	A single tag key value pair for a segment.
Segment	A segment is a list of tags.

Package Contents

class monitor_schema.models.DatasetMetric

Bases: str, enum.Enum

Metrics that are applicable at the dataset level.

profile_count = 'profile.count'

profile_last_ingestion_time = 'profile.last_ingestion_time'

profile_first_ingestion_time = 'profile.first_ingestion_time'

column_row_count_sum = 'column_row_count_sum'

shape_column_count = 'shape_column_count'

shape_row_count = 'shape_row_count'

input_count = 'input.count'

output_count = 'output.count'

classification_f1 = 'classification.f1'

classification_precision = 'classification.precision'

classification_recall = 'classification.recall'

classification_accuracy = 'classification.accuracy'

classification_fpr = 'classification.fpr'

classification_auroc = 'classification.auroc'

regression_mse = 'regression.mse'

regression_mae = 'regression.mae'

regression_rmse = 'regression.rmse'

class monitor_schema.models.SimpleColumnMetric

Bases: str, enum.Enum

Simple column metrics that are basically just a single number.

count = 'count'

median = 'median'

max = 'max'

min = 'min'

mean = 'mean'

stddev = 'stddev'

variance = 'variance'

unique_upper = 'unique_upper'

unique_upper_ratio = 'unique_upper_ratio'

unique_est = 'unique_est'

unique_est_ratio = 'unique_est_ratio'

unique_lower = 'unique_lower'

unique_lower_ratio = 'unique_lower_ratio'

count_bool = 'count_bool'

count_bool_ratio = 'count_bool_ratio'

count_integral = 'count_integral'

count_integral_ratio = 'count_integral_ratio'

count_fractional = 'count_fractional'

count_fractional_ratio = 'count_fractional_ratio'

count_string = 'count_string'

count_string_ratio = 'count_string_ratio'

count_null = 'count_null'

count_null_ratio = 'count_null_ratio'

inferred_data_type = 'inferred_data_type'

quantile_p5 = 'quantile_5'

quantile_p75 = 'quantile_75'

quantile_p25 = 'quantile_25'

quantile_p90 = 'quantile_90'

quantile_p95 = 'quantile_95'

quantile_p99 = 'quantile_99'

class monitor_schema.models.ComplexMetrics

Bases: str, enum.Enum

Sketch-based metrics that can only be processed by certain algorithms.

histogram = 'histogram'

frequent_items = 'frequent_items'

unique_sketch = 'unique_sketch'

column_list = 'column_list'

class monitor_schema.models.Analyzer

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Configuration for running an analysis.

An analysis targets a metric (note that a metric could be a complex object) for one or multiple fields in one or multiple segments. The output is a list of ‘anomalies’ that might show issues with data.

metadata: monitor_schema.models.commons.Metadata | None

id: str

displayName: str | None

tags: Optional[List[constr(min_length=3, max_length=256, regex='[0-9a-zA-Z\\-_]')]]

targetSize: int | None

schedule: monitor_schema.models.commons.CronSchedule | monitor_schema.models.commons.FixedCadenceSchedule | None

disabled: bool | None

disableTargetRollup: bool | None

targetMatrix: monitor_schema.models.analyzer.targets.ColumnMatrix | monitor_schema.models.analyzer.targets.DatasetMatrix | None

dataReadinessDuration: str | None

batchCoolDownPeriod: str | None

backfillGracePeriodDuration: str | None

config: monitor_schema.models.analyzer.algorithms.ConjunctionConfig | monitor_schema.models.analyzer.algorithms.DisjunctionConfig | monitor_schema.models.analyzer.algorithms.DiffConfig | monitor_schema.models.analyzer.algorithms.ComparisonConfig | monitor_schema.models.analyzer.algorithms.ListComparisonConfig | monitor_schema.models.analyzer.algorithms.FrequentStringComparisonConfig | monitor_schema.models.analyzer.algorithms.ColumnListChangeConfig | monitor_schema.models.analyzer.algorithms.FixedThresholdsConfig | monitor_schema.models.analyzer.algorithms.StddevConfig | monitor_schema.models.analyzer.algorithms.DriftConfig | monitor_schema.models.analyzer.algorithms.ExperimentalConfig | monitor_schema.models.analyzer.algorithms.SeasonalConfig

class Config

Updates JSON schema anyOf to oneOf.

static schema_extra(schema: Dict[str, Any], model: pydantic.BaseModel) → None

Update specific fields here (for Union type, specifically).

class monitor_schema.models.BaselineType

Bases: str, enum.Enum

Supported baseline types.

BatchTimestamp = 'BatchTimestamp'

Reference = 'Reference'

TrailingWindow = 'TrailingWindow'

TimeRange = 'TimeRange'

CurrentBatch = 'CurrentBatch'

class monitor_schema.models.ReferenceProfileId

Bases: _Baseline

A baseline based on a static reference profile.

A typical use case is to use a “gold” dataset and upload its profile to WhyLabs. This can be a training dataset as well for an ML model.

type: Literal[BaselineType]

profileId: str

class monitor_schema.models.TimeRangeBaseline

Bases: _SegmentBaseline

A static time range.

Instead of using a single profile or a trailing window, user can lock in a “good” period.

type: Literal[BaselineType]

range: monitor_schema.models.commons.TimeRange

class monitor_schema.models.TrailingWindowBaseline

Bases: _SegmentBaseline

A dynamic trailing window.

This is useful if you don’t have a static baseline to monitor against. This is the default mode for most monitors.

type: Literal[BaselineType]

size: int

offset: int | None

exclusionRanges: List[monitor_schema.models.commons.TimeRange] | None

class monitor_schema.models.SingleBatchBaseline

Bases: _SegmentBaseline

Using current batch.

This is used when you want to use one batch to monitor another batch in a different metric entity.

type: Literal[BaselineType]

offset: int | None

datasetId: str

class monitor_schema.models.DriftConfig

Bases: AlgorithmConfig

An analyzer using stddev for a window of time range.

This analysis will detect whether the data drifts or not. By default, we use hellinger distance with a threshold of 0.7.

type: Literal[AlgorithmType]

algorithm: Literal['hellinger', 'jensenshannon', 'kl_divergence', 'psi']

metric: Literal[ComplexMetrics, ComplexMetrics]

threshold: float

minBatchSize: int | None

baseline: monitor_schema.models.analyzer.baseline.TrailingWindowBaseline | monitor_schema.models.analyzer.baseline.ReferenceProfileId | monitor_schema.models.analyzer.baseline.TimeRangeBaseline | monitor_schema.models.analyzer.baseline.SingleBatchBaseline

class monitor_schema.models.DiffConfig

Bases: AlgorithmConfig

Detecting the differences between two numerical metrics.

type: Literal[AlgorithmType]

mode: DiffMode

thresholdType: ThresholdType | None

threshold: float

baseline: monitor_schema.models.analyzer.baseline.TrailingWindowBaseline | monitor_schema.models.analyzer.baseline.ReferenceProfileId | monitor_schema.models.analyzer.baseline.TimeRangeBaseline | monitor_schema.models.analyzer.baseline.SingleBatchBaseline

class monitor_schema.models.ComparisonConfig

Bases: AlgorithmConfig

Compare whether the target against either an expect value or against the baseline.

This is useful to detect data type change, for instance.

type: Literal[AlgorithmType]

operator: ComparisonOperator

expected: ExpectedValue | None

baseline: monitor_schema.models.analyzer.baseline.TrailingWindowBaseline | monitor_schema.models.analyzer.baseline.ReferenceProfileId | monitor_schema.models.analyzer.baseline.TimeRangeBaseline | monitor_schema.models.analyzer.baseline.SingleBatchBaseline | None

class monitor_schema.models.ExperimentalConfig

Bases: AlgorithmConfig

Experimental algorithm that is not standardized by the above ones yet.

type: Literal[AlgorithmType]

implementation: str

baseline: monitor_schema.models.analyzer.baseline.TrailingWindowBaseline | monitor_schema.models.analyzer.baseline.ReferenceProfileId | monitor_schema.models.analyzer.baseline.TimeRangeBaseline | monitor_schema.models.analyzer.baseline.SingleBatchBaseline

stub: AlgorithmType | None

class monitor_schema.models.FixedThresholdsConfig

Bases: AlgorithmConfig

Fixed threshold analysis.

If user fails to set both upper bound and lower bound, this algorithm becomes a no-op. WhyLabs might enforce the present of either fields in the future.

type: Literal[AlgorithmType]

upper: float | None

lower: float | None

class monitor_schema.models.ColumnListChangeConfig

Bases: AlgorithmConfig

Compare whether the target is equal to a value or not.

This is useful to detect data type change, for instance.

type: Literal[AlgorithmType]

mode: Literal['ON_ADD_AND_REMOVE', 'ON_ADD', 'ON_REMOVE'] = 'ON_ADD_AND_REMOVE'

metric: Literal[ComplexMetrics]

exclude: List[monitor_schema.models.utils.COLUMN_NAME_TYPE] | None

baseline: monitor_schema.models.analyzer.baseline.TrailingWindowBaseline | monitor_schema.models.analyzer.baseline.ReferenceProfileId | monitor_schema.models.analyzer.baseline.TimeRangeBaseline | monitor_schema.models.analyzer.baseline.SingleBatchBaseline

class monitor_schema.models.SeasonalConfig

Bases: _ThresholdBaseConfig

An analyzer using stddev for a window of time range.

This will fall back to Poisson distribution if there is only 1 value in the baseline.

This only works with TrailingWindow baseline (TODO: add backend validation)

type: Literal[AlgorithmType]

algorithm: Literal['arima']

minBatchSize: int | None

alpha: float | None

baseline: monitor_schema.models.analyzer.baseline.TrailingWindowBaseline

stddevTimeRanges: List[monitor_schema.models.commons.TimeRange] | None

stddevMaxBatchSize: int | None

stddevFactor: float | None

class monitor_schema.models.StddevConfig

Bases: _ThresholdBaseConfig

Calculates upper bounds and lower bounds based on stddev from a series of numbers.

An analyzer using stddev for a window of time range.

This calculation will fall back to Poisson distribution if there is only 1 value in the baseline. For 2 values, we use the formula sqrt((x_i - avg(x))^2 / n - 1)

type: Literal[AlgorithmType]

factor: float | None

minBatchSize: int | None

baseline: monitor_schema.models.analyzer.baseline.TrailingWindowBaseline | monitor_schema.models.analyzer.baseline.TimeRangeBaseline | monitor_schema.models.analyzer.baseline.ReferenceProfileId

class monitor_schema.models.DatasetMatrix

Bases: _BaseMatrix

Define the matrix of fields and segments to fan out for monitoring.

.

type: Literal[TargetLevel]

class monitor_schema.models.ColumnMatrix

Bases: _BaseMatrix

Define the matrix of columns and segments to fan out for monitoring.

type: Literal[TargetLevel]

include: List[ColumnGroups | monitor_schema.models.utils.COLUMN_NAME_TYPE] | None

exclude: List[ColumnGroups | monitor_schema.models.utils.COLUMN_NAME_TYPE] | None

profileId: str | None

class monitor_schema.models.TargetLevel

Bases: str, enum.Enum

Which nested level we are targeting.

dataset = 'dataset'

column = 'column'

class monitor_schema.models.Metadata

Bases: NoExtrasBaseModel

Metadata for a top-level objects such as monitors, analyzers, and schema.

This object is managed by WhyLabs. Any user-provided values will be ignored on WhyLabs side.

version: int

schemaVersion: int | None

updatedTimestamp: int

author: str

description: str | None

class monitor_schema.models.Segment

Bases: monitor_schema.models.commons.NoExtrasBaseModel

A segment is a list of tags.

We normalize these in the backend.

tags: List[SegmentTag]

class monitor_schema.models.ColumnDiscreteness

Bases: str, enum.Enum

Classifying the type.

discrete = 'discrete'

continuous = 'continuous'

class monitor_schema.models.ColumnDataType

Bases: str, enum.Enum

Options for configuring data type for a column.

integral = 'integral'

fractional = 'fractional'

boolean = 'bool'

string = 'string'

unknown = 'unknown'

null = 'null'

class monitor_schema.models.ColumnSchema

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Schema configuration for a column.

Should be generated by WhyLabs originally but can be overridden by users.

discreteness: ColumnDiscreteness

dataType: ColumnDataType

classifier: str | None

class monitor_schema.models.WeightConfig

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Object that specifies column weights.

• By default, the weight of a column is None (unspecified)

• If the weight is unspecified, the column is EXCLUDED when you perform a filter/sort by weight

• For sorting, unweighted column take the LEAST PRECEDENCE, meaning that weight column have higher priorities

• They are not hierarchical: if a segment weight config is specified and a column does not have a weight in that

config, we will not use any hierarchy to resolve the value. It will be None - Order of unweighted column is undefined.

weights: Dict[monitor_schema.models.utils.COLUMN_NAME_TYPE, float]

class monitor_schema.models.SegmentWeightConfig

Bases: WeightConfig

Object that specifies column weights for a segment.

segment: monitor_schema.models.segments.Segment | None

class monitor_schema.models.EntitySchema

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Schema definition of an entity.

metadata: monitor_schema.models.commons.Metadata | None

columns: Dict[monitor_schema.models.utils.COLUMN_NAME_TYPE, ColumnSchema]

class monitor_schema.models.ImmediateSchedule

Bases: NoExtrasBaseModel

Schedule the monitor to run immediately.

type: Literal['immediate']

class monitor_schema.models.CronSchedule

Bases: NoExtrasBaseModel

Support for scheduling.

type: Literal['cron']

cron: str

exclusionRanges: List[TimeRange] | None

class monitor_schema.models.Cadence

Bases: str, enum.Enum

Cadence for an analyzer or monitor run.

hourly = 'hourly'

daily = 'daily'

weekly = 'weekly'

monthly = 'monthly'

class monitor_schema.models.FixedCadenceSchedule

Bases: NoExtrasBaseModel

Support for scheduling based on a predefined cadence.

type: Literal['fixed']

cadence: Literal[Cadence, Cadence, Cadence, Cadence]

exclusionRanges: List[TimeRange] | None

class monitor_schema.models.Metadata

Bases: NoExtrasBaseModel

Metadata for a top-level objects such as monitors, analyzers, and schema.

This object is managed by WhyLabs. Any user-provided values will be ignored on WhyLabs side.

version: int

schemaVersion: int | None

updatedTimestamp: int

author: str

description: str | None

class monitor_schema.models.Analyzer

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Configuration for running an analysis.

An analysis targets a metric (note that a metric could be a complex object) for one or multiple fields in one or multiple segments. The output is a list of ‘anomalies’ that might show issues with data.

metadata: monitor_schema.models.commons.Metadata | None

id: str

displayName: str | None

tags: Optional[List[constr(min_length=3, max_length=256, regex='[0-9a-zA-Z\\-_]')]]

targetSize: int | None

schedule: monitor_schema.models.commons.CronSchedule | monitor_schema.models.commons.FixedCadenceSchedule | None

disabled: bool | None

disableTargetRollup: bool | None

targetMatrix: monitor_schema.models.analyzer.targets.ColumnMatrix | monitor_schema.models.analyzer.targets.DatasetMatrix | None

dataReadinessDuration: str | None

batchCoolDownPeriod: str | None

backfillGracePeriodDuration: str | None

config: monitor_schema.models.analyzer.algorithms.ConjunctionConfig | monitor_schema.models.analyzer.algorithms.DisjunctionConfig | monitor_schema.models.analyzer.algorithms.DiffConfig | monitor_schema.models.analyzer.algorithms.ComparisonConfig | monitor_schema.models.analyzer.algorithms.ListComparisonConfig | monitor_schema.models.analyzer.algorithms.FrequentStringComparisonConfig | monitor_schema.models.analyzer.algorithms.ColumnListChangeConfig | monitor_schema.models.analyzer.algorithms.FixedThresholdsConfig | monitor_schema.models.analyzer.algorithms.StddevConfig | monitor_schema.models.analyzer.algorithms.DriftConfig | monitor_schema.models.analyzer.algorithms.ExperimentalConfig | monitor_schema.models.analyzer.algorithms.SeasonalConfig

class Config

Updates JSON schema anyOf to oneOf.

static schema_extra(schema: Dict[str, Any], model: pydantic.BaseModel) → None

Update specific fields here (for Union type, specifically).

class monitor_schema.models.EntitySchema

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Schema definition of an entity.

metadata: monitor_schema.models.commons.Metadata | None

columns: Dict[monitor_schema.models.utils.COLUMN_NAME_TYPE, ColumnSchema]

class monitor_schema.models.Metadata

Bases: NoExtrasBaseModel

Metadata for a top-level objects such as monitors, analyzers, and schema.

This object is managed by WhyLabs. Any user-provided values will be ignored on WhyLabs side.

version: int

schemaVersion: int | None

updatedTimestamp: int

author: str

description: str | None

class monitor_schema.models.Monitor

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Customer specified monitor configs.

metadata: monitor_schema.models.commons.Metadata | None

id: str

displayName: str | None

tags: Optional[List[constr(min_length=3, max_length=256, regex='[0-9a-zA-Z\\-_]')]]

analyzerIds: List[constr(regex='^[A-Za-z0-9_\\-]+$')]

schedule: monitor_schema.models.commons.FixedCadenceSchedule | monitor_schema.models.commons.CronSchedule | monitor_schema.models.commons.ImmediateSchedule

disabled: bool | None

severity: int | None

mode: EveryAnomalyMode | DigestMode

actions: List[GlobalAction | SendEmail | SlackWebhook | RawWebhook]

class Config

Updates JSON schema anyOf to oneOf.

static schema_extra(schema: Dict[str, Any], model: pydantic.BaseModel) → None

Update specific fields here (for Union type, specifically).

class monitor_schema.models.Granularity

Bases: str, enum.Enum

Supported granularity.

hourly = 'hourly'

daily = 'daily'

weekly = 'weekly'

monthly = 'monthly'

class monitor_schema.models.Document

Bases: monitor_schema.models.commons.NoExtrasBaseModel

The main document that dictates how the monitor should be run. This document is managed by WhyLabs internally.

id: uuid.UUID | None

schemaVersion: Literal[1]

metadata: monitor_schema.models.commons.Metadata | None

orgId: str

datasetId: str

granularity: Granularity

allowPartialTargetBatches: bool | None

entitySchema: monitor_schema.models.column_schema.EntitySchema | None

weightConfig: monitor_schema.models.column_schema.EntityWeights | None

analyzers: List[monitor_schema.models.analyzer.Analyzer]

monitors: List[monitor_schema.models.monitor.Monitor]

class monitor_schema.models.CronSchedule

Bases: NoExtrasBaseModel

Support for scheduling.

type: Literal['cron']

cron: str

exclusionRanges: List[TimeRange] | None

class monitor_schema.models.FixedCadenceSchedule

Bases: NoExtrasBaseModel

Support for scheduling based on a predefined cadence.

type: Literal['fixed']

cadence: Literal[Cadence, Cadence, Cadence, Cadence]

exclusionRanges: List[TimeRange] | None

class monitor_schema.models.ImmediateSchedule

Bases: NoExtrasBaseModel

Schedule the monitor to run immediately.

type: Literal['immediate']

class monitor_schema.models.Metadata

Bases: NoExtrasBaseModel

Metadata for a top-level objects such as monitors, analyzers, and schema.

This object is managed by WhyLabs. Any user-provided values will be ignored on WhyLabs side.

version: int

schemaVersion: int | None

updatedTimestamp: int

author: str

description: str | None

class monitor_schema.models.GlobalAction

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Actions that are configured at the team/organization level.

type: Literal['global']

target: str

class monitor_schema.models.SendEmail

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Action to send an email.

type: Literal['email']

target: str

class monitor_schema.models.SlackWebhook

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Action to send a Slack webhook.

type: Literal['slack']

target: pydantic.HttpUrl

class monitor_schema.models.RawWebhook

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Action to send a Slack webhook.

type: Literal['raw']

target: pydantic.HttpUrl

class monitor_schema.models.AnomalyFilter

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Filter the anomalies based on certain criteria. If the alerts are filtered down to 0, the monitor won’t fire.

includeColumns: List[monitor_schema.models.utils.COLUMN_NAME_TYPE] | None

excludeColumns: List[monitor_schema.models.utils.COLUMN_NAME_TYPE] | None

minWeight: float | None

maxWeight: float | None

minRankByWeight: int | None

maxRankByWeight: int | None

minTotalWeight: float | None

maxTotalWeight: float | None

minAlertCount: int | None

maxAlertCount: int | None

includeMetrics: List[monitor_schema.models.utils.METRIC_NAME_STR] | None

class monitor_schema.models.EveryAnomalyMode

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Config mode that indicates the monitor will send out individual messages per anomaly.

type: Literal['EVERY_ANOMALY']

filter: AnomalyFilter | None

class monitor_schema.models.DigestMode

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Config mode that indicates the monitor will send out a digest message.

type: Literal['DIGEST']

filter: AnomalyFilter | None

creationTimeOffset: str | None

datasetTimestampOffset: str | None

groupBy: List[DigestModeGrouping] | None

class monitor_schema.models.Monitor

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Customer specified monitor configs.

metadata: monitor_schema.models.commons.Metadata | None

id: str

displayName: str | None

tags: Optional[List[constr(min_length=3, max_length=256, regex='[0-9a-zA-Z\\-_]')]]

analyzerIds: List[constr(regex='^[A-Za-z0-9_\\-]+$')]

schedule: monitor_schema.models.commons.FixedCadenceSchedule | monitor_schema.models.commons.CronSchedule | monitor_schema.models.commons.ImmediateSchedule

disabled: bool | None

severity: int | None

mode: EveryAnomalyMode | DigestMode

actions: List[GlobalAction | SendEmail | SlackWebhook | RawWebhook]

class Config

Updates JSON schema anyOf to oneOf.

static schema_extra(schema: Dict[str, Any], model: pydantic.BaseModel) → None

Update specific fields here (for Union type, specifically).

class monitor_schema.models.SegmentTag

Bases: monitor_schema.models.commons.NoExtrasBaseModel

A single tag key value pair for a segment.

key: str

value: str

class monitor_schema.models.Segment

Bases: monitor_schema.models.commons.NoExtrasBaseModel

A segment is a list of tags.

We normalize these in the backend.

tags: List[SegmentTag]