mirror of
https://github.com/PostHog/posthog.git
synced 2024-11-22 08:15:44 +01:00
86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
from datetime import datetime, timedelta
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from zoneinfo import ZoneInfo
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from celery import shared_task
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from ee.api.sentry_stats import get_stats_for_timerange
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from posthog.models.feature_flag import FeatureFlag
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from posthog.models.filters.filter import Filter
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from posthog.models.team import Team
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from posthog.queries.trends.trends import Trends
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def check_flags_to_rollback():
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flags_with_threshold = FeatureFlag.objects.exclude(rollback_conditions__isnull=True).exclude(
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rollback_conditions__exact=[]
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)
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for feature_flag in flags_with_threshold:
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check_feature_flag_rollback_conditions(feature_flag_id=feature_flag.pk)
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@shared_task(ignore_result=True, max_retries=2)
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def check_feature_flag_rollback_conditions(feature_flag_id: int) -> None:
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flag: FeatureFlag = FeatureFlag.objects.get(pk=feature_flag_id)
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if any(check_condition(condition, flag) for condition in flag.rollback_conditions):
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flag.performed_rollback = True
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flag.active = False
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flag.save()
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def calculate_rolling_average(threshold_metric: dict, team: Team, timezone: str) -> float:
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curr = datetime.now(tz=ZoneInfo(timezone))
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rolling_average_days = 7
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filter = Filter(
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data={
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**threshold_metric,
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"date_from": (curr - timedelta(days=rolling_average_days)).strftime("%Y-%m-%d %H:%M:%S.%f"),
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"date_to": curr.strftime("%Y-%m-%d %H:%M:%S.%f"),
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},
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team=team,
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)
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trends_query = Trends()
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result = trends_query.run(filter, team)
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if not len(result):
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return False
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data = result[0]["data"]
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return sum(data) / rolling_average_days
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def check_condition(rollback_condition: dict, feature_flag: FeatureFlag) -> bool:
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if rollback_condition["threshold_type"] == "sentry":
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created_date = feature_flag.created_at
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base_start_date = created_date.strftime("%Y-%m-%dT%H:%M:%S")
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base_end_date = (created_date + timedelta(days=1)).strftime("%Y-%m-%dT%H:%M:%S")
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current_time = datetime.utcnow()
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target_end_date = current_time.strftime("%Y-%m-%dT%H:%M:%S")
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target_start_date = (current_time - timedelta(days=1)).strftime("%Y-%m-%dT%H:%M:%S")
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base, target = get_stats_for_timerange(base_start_date, base_end_date, target_start_date, target_end_date)
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if rollback_condition["operator"] == "lt":
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return target < float(rollback_condition["threshold"]) * base
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else:
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return target > float(rollback_condition["threshold"]) * base
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elif rollback_condition["threshold_type"] == "insight":
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rolling_average = calculate_rolling_average(
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rollback_condition["threshold_metric"],
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feature_flag.team,
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feature_flag.team.timezone,
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)
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if rollback_condition["operator"] == "lt":
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return rolling_average < rollback_condition["threshold"]
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else:
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return rolling_average > rollback_condition["threshold"]
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return False
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