operational_log_patterns

sqllocks_spindle.simulation.operational_log_patterns

Operational logs / observability patterns — trace/span IDs, latency spikes, outage storms.

Generate realistic application log and observability event streams with distributed tracing, latency distributions, error bursts, and outage simulation.

Usage::

from sqllocks_spindle.simulation.operational_log_patterns import (
    OperationalLogSimulator, OperationalLogConfig,
)

cfg = OperationalLogConfig(service_count=5, duration_hours=24)
result = OperationalLogSimulator(config=cfg).run()

Classes

OperationalLogConfig dataclass

Configuration for operational log simulation.

Parameters:

Name	Type	Description	Default
service_count	int	Number of services (uses first N from default list).	5
services	list[dict[str, Any]]	Override service definitions.	list()
duration_hours	float	Time span of the simulation.	24.0
start_time	str	Simulation start (ISO format).	'2024-01-01T00:00:00'
events_per_hour	float	Base event rate per service per hour.	100.0
latency_mean_ms	float	Mean request latency (log-normal distribution).	50.0
latency_std_ms	float	Std dev for log-normal latency.	30.0
latency_spike_enabled	bool	Inject latency spikes.	True
latency_spike_probability	float	Per-hour chance of a latency spike.	0.05
latency_spike_multiplier	float	Multiplier for latency during spikes.	10.0
latency_spike_duration_minutes	float	Duration of spike window.	15.0
outage_enabled	bool	Inject outage storms.	True
outage_probability	float	Per-hour chance of an outage starting.	0.02
outage_duration_minutes	float	How long the outage lasts.	30.0
outage_error_rate	float	Fraction of requests that fail during outage.	0.8
trace_enabled	bool	Generate distributed trace/span IDs.	True
trace_depth_mean	float	Mean number of spans per trace.	3.5
error_burst_enabled	bool	Inject error bursts.	True
error_burst_probability	float	Per-hour chance of error burst.	0.03
error_burst_count	int	Errors per burst.	50
seed	int	Random seed.	42

OperationalLogResult dataclass

Result of an operational log simulation.

Attributes:

Name	Type	Description
logs	DataFrame	All log events sorted by timestamp.
traces	DataFrame	Distributed trace records (trace_id, spans).
service_health	DataFrame	Per-service health summary.
stats	dict[str, Any]	Aggregate statistics.

OperationalLogSimulator

Generate synthetic operational / observability log data.

Produces structured log events with distributed tracing, latency distributions, error bursts, and outage simulation.

Methods:

run()

Execute the operational log simulation.