flow_constraints

flow_constraints ¤

FlowConstraintEvents ¤

FlowConstraintEvents(
    dataframe: DataFrame,
    *,
    time_column: str = "systime",
    event_uuid: str = "prod:flow"
)

Bases: Base

Production: Flow Constraints

blocked_events: upstream running while downstream not consuming.
starved_events: downstream idle due to lack of upstream supply.

blocked_events ¤

blocked_events(
    *,
    roles: Dict[str, str],
    tolerance: str = "200ms",
    tolerance_before: Optional[str] = None,
    tolerance_after: Optional[str] = None,
    min_duration: str = "0s"
) -> pd.DataFrame

Blocked: upstream_run=True while downstream_run=False.

Parameters:

Name	Type	Description	Default
`roles`	`Dict[str, str]`	Dictionary mapping role names to UUIDs. Expected keys: 'upstream_run', 'downstream_run'	required
`tolerance`	`str`	Default tolerance for time alignment (used if directional tolerances not provided)	`'200ms'`
`tolerance_before`	`Optional[str]`	Tolerance for looking backward in time during alignment	`None`
`tolerance_after`	`Optional[str]`	Tolerance for looking forward in time during alignment	`None`
`min_duration`	`str`	Minimum duration for an event to be included	`'0s'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, uuid, source_uuid, is_delta, type,
`DataFrame`	time_alignment_quality, duration, severity

Example

roles = {'upstream_run': 'uuid1', 'downstream_run': 'uuid2'}

starved_events ¤

starved_events(
    *,
    roles: Dict[str, str],
    tolerance: str = "200ms",
    tolerance_before: Optional[str] = None,
    tolerance_after: Optional[str] = None,
    min_duration: str = "0s"
) -> pd.DataFrame

Starved: downstream_run=True while upstream_run=False.

Parameters:

Name	Type	Description	Default
`roles`	`Dict[str, str]`	Dictionary mapping role names to UUIDs. Expected keys: 'upstream_run', 'downstream_run'	required
`tolerance`	`str`	Default tolerance for time alignment (used if directional tolerances not provided)	`'200ms'`
`tolerance_before`	`Optional[str]`	Tolerance for looking backward in time during alignment	`None`
`tolerance_after`	`Optional[str]`	Tolerance for looking forward in time during alignment	`None`
`min_duration`	`str`	Minimum duration for an event to be included	`'0s'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, uuid, source_uuid, is_delta, type,
`DataFrame`	time_alignment_quality, duration, severity

Example

roles = {'upstream_run': 'uuid1', 'downstream_run': 'uuid2'}

flow_constraint_analytics ¤

flow_constraint_analytics(
    *,
    roles: Dict[str, str],
    tolerance: str = "200ms",
    tolerance_before: Optional[str] = None,
    tolerance_after: Optional[str] = None,
    min_duration: str = "0s",
    minor_threshold: str = "5s",
    moderate_threshold: str = "30s"
) -> Dict[str, Any]

Generate comprehensive analytics for flow constraints (blockages and starvations).

Parameters:

Name	Type	Description	Default
`roles`	`Dict[str, str]`	Dictionary mapping role names to UUIDs. Expected keys: 'upstream_run', 'downstream_run'	required
`tolerance`	`str`	Default tolerance for time alignment (used if directional tolerances not provided)	`'200ms'`
`tolerance_before`	`Optional[str]`	Tolerance for looking backward in time during alignment	`None`
`tolerance_after`	`Optional[str]`	Tolerance for looking forward in time during alignment	`None`
`min_duration`	`str`	Minimum duration for an event to be included	`'0s'`
`minor_threshold`	`str`	Duration threshold for minor severity classification	`'5s'`
`moderate_threshold`	`str`	Duration threshold for moderate severity classification	`'30s'`

Returns:

Type	Description
`Dict[str, Any]`	Dictionary containing analytics for both blocked and starved events:
`Dict[str, Any]`	blocked_events: DataFrame of blocked events
`Dict[str, Any]`	starved_events: DataFrame of starved events
`Dict[str, Any]`	summary: Dictionary with statistics including: blocked_count: Total number of blocked events starved_count: Total number of starved events blocked_total_duration: Total duration of blocked events starved_total_duration: Total duration of starved events blocked_avg_duration: Average duration of blocked events starved_avg_duration: Average duration of starved events blocked_severity_breakdown: Count by severity level starved_severity_breakdown: Count by severity level overall_alignment_quality: Average alignment quality across both types

Example

roles = {'upstream_run': 'uuid1', 'downstream_run': 'uuid2'} analytics = flow.flow_constraint_analytics(roles=roles) print(analytics['summary']['blocked_count'])