monitor_schema.cli

Console script for monitor_schema.

Classes

Analyzer	Configuration for running an analysis.
AnomalyFilter	Filter the anomalies based on certain criteria. If the alerts are filtered down to 0, the monitor won't fire.
BaselineType	Supported baseline types.
Cadence	Cadence for an analyzer or monitor run.
ColumnDataType	Options for configuring data type for a column.
ColumnDiscreteness	Classifying the type.
ColumnMatrix	Define the matrix of columns and segments to fan out for monitoring.
ColumnSchema	Schema configuration for a column.
DatasetMatrix	Define the matrix of fields and segments to fan out for monitoring.
DigestMode	Config mode that indicates the monitor will send out a digest message.
Document	The main document that dictates how the monitor should be run. This document is managed by WhyLabs internally.
DriftConfig	An analyzer using stddev for a window of time range.
EntitySchema	Schema definition of an entity.
EveryAnomalyMode	Config mode that indicates the monitor will send out individual messages per anomaly.
FixedCadenceSchedule	Support for scheduling based on a predefined cadence.
GlobalAction	Actions that are configured at the team/organization level.
Granularity	Supported granularity.
Monitor	Customer specified monitor configs.
Segment	A segment is a list of tags.
SendEmail	Action to send an email.
SlackWebhook	Action to send a Slack webhook.
StddevConfig	Calculates upper bounds and lower bounds based on stddev from a series of numbers.
TargetLevel	Which nested level we are targeting.
TrailingWindowBaseline	A dynamic trailing window.

Functions

main(→ None)	Generates schema and example document JSON.
_dump_json_yaml(→ None)

Module Contents

class monitor_schema.cli.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.cli.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.cli.BaselineType

Bases: str, enum.Enum

Supported baseline types.

BatchTimestamp = 'BatchTimestamp'

Reference = 'Reference'

TrailingWindow = 'TrailingWindow'

TimeRange = 'TimeRange'

CurrentBatch = 'CurrentBatch'

class monitor_schema.cli.Cadence

Bases: str, enum.Enum

Cadence for an analyzer or monitor run.

hourly = 'hourly'

daily = 'daily'

weekly = 'weekly'

monthly = 'monthly'

class monitor_schema.cli.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.cli.ColumnDiscreteness

Bases: str, enum.Enum

Classifying the type.

discrete = 'discrete'

continuous = 'continuous'

class monitor_schema.cli.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.cli.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.cli.DatasetMatrix

Bases: _BaseMatrix

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

.

type: Literal[TargetLevel]

class monitor_schema.cli.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.cli.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.cli.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.cli.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.cli.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.cli.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.cli.GlobalAction

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Actions that are configured at the team/organization level.

type: Literal['global']

target: str

class monitor_schema.cli.Granularity

Bases: str, enum.Enum

Supported granularity.

hourly = 'hourly'

daily = 'daily'

weekly = 'weekly'

monthly = 'monthly'

class monitor_schema.cli.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.cli.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.cli.SendEmail

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Action to send an email.

type: Literal['email']

target: str

class monitor_schema.cli.SlackWebhook

Bases: monitor_schema.models.commons.NoExtrasBaseModel

Action to send a Slack webhook.

type: Literal['slack']

target: pydantic.HttpUrl

class monitor_schema.cli.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.cli.TargetLevel

Bases: str, enum.Enum

Which nested level we are targeting.

dataset = 'dataset'

column = 'column'

class monitor_schema.cli.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

monitor_schema.cli.main() → None

Generates schema and example document JSON.

monitor_schema.cli._dump_json_yaml(file_name: str, json_content: str) → None