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chore(): refactored reports queries

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This commit is contained in:
ITQ
2026-02-24 17:57:27 +03:00
parent cda60bb057
commit e5a224bc3e
1 changed files with 145 additions and 76 deletions
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src/backend/apps/reports/services.py
+145 -76
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@@ -2,6 +2,22 @@ from datetime import datetime
from decimal import Decimal
from uuid import UUID
from django.db import connection
from django.db.models import (
Aggregate,
Avg,
Case,
Count,
F,
FloatField,
QuerySet,
Subquery,
Value,
When,
)
from django.db.models.fields.json import KeyTextTransform
from django.db.models.functions import Cast
from apps.events.models import Event, Exposure
from apps.experiments.models import Experiment
from apps.metrics.models import (
@@ -11,12 +27,29 @@ from apps.metrics.models import (
)
def _exposure_decision_ids(
class PercentileCont(Aggregate):
function = "PERCENTILE_CONT"
template = (
"%(function)s(%(percentile)s) "
"WITHIN GROUP (ORDER BY %(expressions)s)"
)
allow_distinct = False
output_field = FloatField()
def __init__(self, expression, percentile, **extra):
super().__init__(
expression,
percentile=percentile,
**extra,
)
def _exposure_queryset(
experiment_id: UUID,
variant_id: UUID,
start_date: datetime | None = None,
end_date: datetime | None = None,
) -> list[str]:
) -> QuerySet[Exposure]:
qs = Exposure.objects.filter(
experiment_id=experiment_id,
variant_id=variant_id,
@@ -25,17 +58,24 @@ def _exposure_decision_ids(
qs = qs.filter(timestamp__gte=start_date)
if end_date:
qs = qs.filter(timestamp__lt=end_date)
return list(qs.values_list("decision_id", flat=True))
return qs
def _count_events(
decision_ids: list[str],
def _exposure_decision_ids_subquery(
exposure_qs: QuerySet[Exposure],
):
return Subquery(exposure_qs.values("decision_id"))
def _events_queryset(
*,
exposure_qs: QuerySet[Exposure],
event_type_name: str,
start_date: datetime | None = None,
end_date: datetime | None = None,
) -> int:
) -> QuerySet[Event]:
qs = Event.objects.filter(
decision_id__in=decision_ids,
decision_id__in=_exposure_decision_ids_subquery(exposure_qs),
event_type__name=event_type_name,
is_attributed=True,
)
@@ -43,72 +83,101 @@ def _count_events(
qs = qs.filter(timestamp__gte=start_date)
if end_date:
qs = qs.filter(timestamp__lt=end_date)
return qs
def _numeric_property_expression(property_field: str):
if connection.vendor == "postgresql":
key_text = KeyTextTransform(property_field, "properties")
pattern = r"^-?(?:\d+(?:\.\d+)?|\.\d+)$"
return Case(
When(
**{
f"properties__{property_field}__regex": pattern
},
then=Cast(key_text, FloatField()),
),
default=Value(None),
output_field=FloatField(),
)
return Cast(F(f"properties__{property_field}"), FloatField())
def _count_events(
*,
exposure_qs: QuerySet[Exposure],
event_type_name: str,
start_date: datetime | None = None,
end_date: datetime | None = None,
) -> int:
qs = _events_queryset(
exposure_qs=exposure_qs,
event_type_name=event_type_name,
start_date=start_date,
end_date=end_date,
)
return qs.count()
def _average_property(
decision_ids: list[str],
*,
exposure_qs: QuerySet[Exposure],
event_type_name: str,
property_field: str,
start_date: datetime | None = None,
end_date: datetime | None = None,
) -> Decimal | None:
qs = Event.objects.filter(
decision_id__in=decision_ids,
event_type__name=event_type_name,
is_attributed=True,
qs = _events_queryset(
exposure_qs=exposure_qs,
event_type_name=event_type_name,
start_date=start_date,
end_date=end_date,
).annotate(
numeric_value=_numeric_property_expression(property_field),
)
if start_date:
qs = qs.filter(timestamp__gte=start_date)
if end_date:
qs = qs.filter(timestamp__lt=end_date)
values = []
for props in qs.values_list("properties", flat=True):
if isinstance(props, dict) and property_field in props:
try:
values.append(float(props[property_field]))
except (TypeError, ValueError):
continue
if not values:
value = qs.aggregate(value=Avg("numeric_value"))["value"]
if value is None:
return None
return Decimal(str(sum(values) / len(values)))
return Decimal(str(value))
def _percentile_property(
decision_ids: list[str],
*,
exposure_qs: QuerySet[Exposure],
event_type_name: str,
property_field: str,
percentile: int,
start_date: datetime | None = None,
end_date: datetime | None = None,
) -> Decimal | None:
qs = Event.objects.filter(
decision_id__in=decision_ids,
event_type__name=event_type_name,
is_attributed=True,
)
if start_date:
qs = qs.filter(timestamp__gte=start_date)
if end_date:
qs = qs.filter(timestamp__lt=end_date)
qs = _events_queryset(
exposure_qs=exposure_qs,
event_type_name=event_type_name,
start_date=start_date,
end_date=end_date,
).annotate(
numeric_value=_numeric_property_expression(property_field),
).exclude(numeric_value__isnull=True)
if connection.vendor == "postgresql":
value = qs.aggregate(
value=PercentileCont(
"numeric_value",
Decimal(percentile) / Decimal(100),
)
)["value"]
if value is None:
return None
return Decimal(str(value))
values = []
for props in qs.values_list("properties", flat=True):
if isinstance(props, dict) and property_field in props:
try:
values.append(float(props[property_field]))
except (TypeError, ValueError):
continue
if not values:
total = qs.aggregate(total=Count("pk"))["total"]
if not total:
return None
values.sort()
idx = int(len(values) * percentile / 100)
idx = min(idx, len(values) - 1)
return Decimal(str(values[idx]))
idx = min(int(total * percentile / 100), total - 1)
value = qs.order_by("numeric_value").values_list(
"numeric_value",
flat=True,
)[idx]
return Decimal(str(value))
def calculate_metric_value(
@@ -121,14 +190,14 @@ def calculate_metric_value(
event_end_date: datetime | None = None,
) -> Decimal | None:
rule = metric.calculation_rule
decision_ids = _exposure_decision_ids(
exposure_qs = _exposure_queryset(
experiment_id,
variant_id,
start_date,
end_date,
)
if not decision_ids:
if not exposure_qs.exists():
return None
ev_start = event_start_date or start_date
@@ -138,16 +207,16 @@ def calculate_metric_value(
if metric_type == MetricType.RATIO:
numerator = _count_events(
decision_ids,
rule["numerator_event"],
ev_start,
ev_end,
exposure_qs=exposure_qs,
event_type_name=rule["numerator_event"],
start_date=ev_start,
end_date=ev_end,
)
denominator = _count_events(
decision_ids,
rule["denominator_event"],
ev_start,
ev_end,
exposure_qs=exposure_qs,
event_type_name=rule["denominator_event"],
start_date=ev_start,
end_date=ev_end,
)
if denominator == 0:
return None
@@ -155,30 +224,30 @@ def calculate_metric_value(
if metric_type == MetricType.COUNT:
count = _count_events(
decision_ids,
rule["event"],
ev_start,
ev_end,
exposure_qs=exposure_qs,
event_type_name=rule["event"],
start_date=ev_start,
end_date=ev_end,
)
return Decimal(str(count))
if metric_type == MetricType.AVERAGE:
return _average_property(
decision_ids,
rule["event"],
rule["property"],
ev_start,
ev_end,
exposure_qs=exposure_qs,
event_type_name=rule["event"],
property_field=rule["property"],
start_date=ev_start,
end_date=ev_end,
)
if metric_type == MetricType.PERCENTILE:
return _percentile_property(
decision_ids,
rule["event"],
rule["property"],
rule.get("percentile", 95),
ev_start,
ev_end,
exposure_qs=exposure_qs,
event_type_name=rule["event"],
property_field=rule["property"],
percentile=rule.get("percentile", 95),
start_date=ev_start,
end_date=ev_end,
)
return None