feat(decision): added decision business logic
This commit is contained in:
@@ -0,0 +1,256 @@
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import hashlib
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import logging
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import uuid
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from datetime import timedelta
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from decimal import Decimal
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from django.core.cache import cache
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from django.utils import timezone
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from prometheus_client import Counter
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from apps.events.models import Decision
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from apps.events.services import decision_create
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from apps.experiments.models import Experiment, ExperimentStatus, Variant
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from apps.experiments.selectors import active_experiment_for_flag
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from apps.flags.models import FeatureFlag
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from apps.flags.selectors import feature_flag_get_by_key
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from libs.dsl import evaluate
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from libs.dsl.exceptions import EvaluationError, LexerError, ParserError
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logger = logging.getLogger("lotty")
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DECIDE_REQUESTS = Counter(
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"lotty_decide_requests_total",
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"Total number of flag decision requests",
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["reason"],
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)
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FLAG_CACHE_TTL = 300
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EXPERIMENT_CACHE_TTL = 60
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MAX_CONCURRENT_EXPERIMENTS = 3
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COOLDOWN_DAYS = 7
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def _hash_subject(subject_id: str, experiment_id: str, salt: str) -> float:
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hash_input = f"{subject_id}:{experiment_id}:{salt}".encode()
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hash_bytes = hashlib.sha256(hash_input).digest()
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hash_int = int.from_bytes(hash_bytes[:8], byteorder="big")
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return (hash_int % 10000) / Decimal(100)
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def _select_variant(
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variants: list[Variant], hash_value: float
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) -> Variant | None:
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cumulative = Decimal(0)
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for variant in sorted(variants, key=lambda v: v.name):
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cumulative += variant.weight
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if hash_value < cumulative:
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return variant
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return variants[-1] if variants else None
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def _persist_decision(result: dict, subject_id: str) -> None:
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decision_create(
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decision_id=result["decision_id"],
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flag_key=result["flag"],
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subject_id=subject_id,
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experiment_id=result.get("experiment_id"),
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variant_id=result.get("variant_id"),
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value=str(result["value"]) if result["value"] is not None else "",
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reason=result["reason"],
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)
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def _cached_flag_get(flag_key: str) -> FeatureFlag | None:
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cache_key = f"flag:{flag_key}"
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cached = cache.get(cache_key)
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if cached is not None:
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return cached if cached != "__none__" else None
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flag = feature_flag_get_by_key(flag_key)
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cache.set(cache_key, flag or "__none__", FLAG_CACHE_TTL)
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return flag
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def _cached_active_experiment(flag_pk):
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cache_key = f"active_exp:{flag_pk}"
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cached = cache.get(cache_key)
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if cached is not None:
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return cached if cached != "__none__" else None
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experiment = active_experiment_for_flag(flag_pk)
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cache.set(
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cache_key,
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experiment or "__none__",
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EXPERIMENT_CACHE_TTL,
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)
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return experiment
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def _check_targeting(
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targeting_rules: str,
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subject_attributes: dict,
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) -> bool:
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if not targeting_rules or not targeting_rules.strip():
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return True
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try:
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return evaluate(targeting_rules, subject_attributes)
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except (EvaluationError, LexerError, ParserError):
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logger.warning(
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"targeting_rules_evaluation_error",
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extra={"rules": targeting_rules},
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)
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return False
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def _check_participation_limits(
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subject_id: str,
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experiment_pk: object,
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) -> bool:
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active_count = (
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Decision.objects.filter(
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subject_id=subject_id,
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reason="experiment_assigned",
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experiment_id__isnull=False,
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)
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.exclude(experiment_id=experiment_pk)
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.values("experiment_id")
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.distinct()
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.count()
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)
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if active_count >= MAX_CONCURRENT_EXPERIMENTS:
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return False
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cutoff = timezone.now() - timedelta(days=COOLDOWN_DAYS)
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recent_completed = (
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Decision.objects.filter(
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subject_id=subject_id,
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reason="experiment_assigned",
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experiment_id__isnull=False,
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created_at__gte=cutoff,
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)
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.filter(
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experiment_id__in=Experiment.objects.filter(
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status__in=(
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ExperimentStatus.COMPLETED,
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ExperimentStatus.ARCHIVED,
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),
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).values("pk"),
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)
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.exclude(experiment_id=experiment_pk)
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.values("experiment_id")
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.distinct()
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.exists()
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)
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return not recent_completed
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def decide_for_flag(
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flag_key: str,
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subject_id: str,
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subject_attributes: dict,
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) -> dict:
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flag = _cached_flag_get(flag_key)
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if not flag:
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DECIDE_REQUESTS.labels(reason="flag_not_found").inc()
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result = {
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"flag": flag_key,
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"value": None,
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"decision_id": str(uuid.uuid4()),
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"experiment_id": None,
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"variant_id": None,
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"reason": "flag_not_found",
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}
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_persist_decision(result, subject_id)
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return result
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experiment = _cached_active_experiment(flag.pk)
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if not experiment or experiment.status != ExperimentStatus.RUNNING:
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DECIDE_REQUESTS.labels(reason="no_active_experiment").inc()
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result = {
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"flag": flag_key,
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"value": flag.default_value,
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"decision_id": str(uuid.uuid4()),
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"experiment_id": None,
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"variant_id": None,
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"reason": "no_active_experiment",
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}
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_persist_decision(result, subject_id)
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return result
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if not _check_targeting(experiment.targeting_rules, subject_attributes):
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DECIDE_REQUESTS.labels(reason="targeting_mismatch").inc()
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result = {
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"flag": flag_key,
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"value": flag.default_value,
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"decision_id": str(uuid.uuid4()),
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"experiment_id": str(experiment.pk),
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"variant_id": None,
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"reason": "targeting_mismatch",
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}
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_persist_decision(result, subject_id)
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return result
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if not _check_participation_limits(subject_id, experiment.pk):
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DECIDE_REQUESTS.labels(reason="participation_limit").inc()
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result = {
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"flag": flag_key,
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"value": flag.default_value,
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"decision_id": str(uuid.uuid4()),
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"experiment_id": str(experiment.pk),
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"variant_id": None,
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"reason": "participation_limit",
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}
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_persist_decision(result, subject_id)
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return result
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allocation_hash = _hash_subject(
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subject_id,
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str(experiment.pk),
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"allocation",
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)
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if allocation_hash >= experiment.traffic_allocation:
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DECIDE_REQUESTS.labels(reason="outside_traffic_allocation").inc()
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result = {
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"flag": flag_key,
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"value": flag.default_value,
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"decision_id": str(uuid.uuid4()),
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"experiment_id": str(experiment.pk),
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"variant_id": None,
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"reason": "outside_traffic_allocation",
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}
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_persist_decision(result, subject_id)
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return result
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variants = list(experiment.variants.all())
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if not variants:
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DECIDE_REQUESTS.labels(reason="no_variants").inc()
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result = {
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"flag": flag_key,
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"value": flag.default_value,
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"decision_id": str(uuid.uuid4()),
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"experiment_id": str(experiment.pk),
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"variant_id": None,
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"reason": "no_variants",
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}
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_persist_decision(result, subject_id)
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return result
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variant_hash = _hash_subject(
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subject_id,
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str(experiment.pk),
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"variant",
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)
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total_weight = sum(v.weight for v in variants)
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normalized_hash = variant_hash * total_weight / Decimal("100")
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selected = _select_variant(variants, normalized_hash)
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DECIDE_REQUESTS.labels(reason="experiment_assigned").inc()
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result = {
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"flag": flag_key,
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"value": selected.value if selected else flag.default_value,
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"decision_id": str(uuid.uuid4()),
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"experiment_id": str(experiment.pk),
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"variant_id": str(selected.pk) if selected else None,
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"reason": "experiment_assigned",
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}
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_persist_decision(result, subject_id)
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return result
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