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shruthilayajandrewshie-sentry
shruthilayaj
authored and
andrewshie-sentry
committed
feat(explore): Add support for apdex and user misery (#94919)
Adds support for apdex and user misery. Note that the is_transaction filter is built into the formulas.
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‎src/sentry/search/eap/spans/formulas.py

Lines changed: 263 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -36,7 +36,11 @@
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)
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from sentry.search.eap.types import SearchResolverConfig
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from sentry.search.eap.utils import literal_validator
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from sentry.search.events.constants import WEB_VITALS_PERFORMANCE_SCORE_WEIGHTS
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from sentry.search.events.constants import (
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MISERY_ALPHA,
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MISERY_BETA,
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WEB_VITALS_PERFORMANCE_SCORE_WEIGHTS,
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)
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from sentry.snuba import spans_rpc
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from sentry.snuba.referrer import Referrer
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@@ -795,6 +799,234 @@ def eps(_: ResolvedArguments, settings: ResolverSettings) -> Column.BinaryFormul
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)
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def apdex(args: ResolvedArguments, settings: ResolverSettings) -> Column.BinaryFormula:
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"""
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Calculate Apdex score based on response time field and threshold.
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Apdex = (Satisfactory + Tolerable/2) / Total Requests
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Where:
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- Satisfactory: response time ≤ T
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- Tolerable: response time > T and ≤ 4T
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- Frustrated: response time > 4T
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"""
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extrapolation_mode = settings["extrapolation_mode"]
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# Get the response time field and threshold
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response_time_field = cast(AttributeKey, args[0])
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threshold = cast(float, args[1])
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# Calculate 4T for tolerable range
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tolerable_threshold = threshold * 4
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# Satisfactory requests: response time ≤ T and is_transaction = True
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satisfactory = Column(
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conditional_aggregation=AttributeConditionalAggregation(
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aggregate=Function.FUNCTION_COUNT,
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key=response_time_field,
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filter=TraceItemFilter(
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and_filter=AndFilter(
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filters=[
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TraceItemFilter(
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comparison_filter=ComparisonFilter(
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key=response_time_field,
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op=ComparisonFilter.OP_LESS_THAN_OR_EQUALS,
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value=AttributeValue(val_double=threshold),
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)
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),
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TraceItemFilter(
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comparison_filter=ComparisonFilter(
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key=AttributeKey(
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type=AttributeKey.TYPE_BOOLEAN, name="sentry.is_segment"
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),
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op=ComparisonFilter.OP_EQUALS,
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value=AttributeValue(val_bool=True),
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)
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),
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]
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)
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),
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extrapolation_mode=extrapolation_mode,
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)
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)
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# Tolerable requests: response time > T and ≤ 4T and is_transaction = True
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tolerable = Column(
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conditional_aggregation=AttributeConditionalAggregation(
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aggregate=Function.FUNCTION_COUNT,
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key=response_time_field,
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filter=TraceItemFilter(
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and_filter=AndFilter(
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filters=[
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TraceItemFilter(
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comparison_filter=ComparisonFilter(
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key=response_time_field,
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op=ComparisonFilter.OP_GREATER_THAN,
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value=AttributeValue(val_double=threshold),
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)
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),
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TraceItemFilter(
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comparison_filter=ComparisonFilter(
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key=response_time_field,
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op=ComparisonFilter.OP_LESS_THAN_OR_EQUALS,
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value=AttributeValue(val_double=tolerable_threshold),
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)
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),
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TraceItemFilter(
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comparison_filter=ComparisonFilter(
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key=AttributeKey(
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type=AttributeKey.TYPE_BOOLEAN, name="sentry.is_segment"
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),
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op=ComparisonFilter.OP_EQUALS,
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value=AttributeValue(val_bool=True),
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)
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),
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]
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)
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),
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extrapolation_mode=extrapolation_mode,
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)
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)
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# Total requests: count of all requests with the response time field and is_transaction = True
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total = Column(
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conditional_aggregation=AttributeConditionalAggregation(
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aggregate=Function.FUNCTION_COUNT,
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key=response_time_field,
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filter=TraceItemFilter(
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comparison_filter=ComparisonFilter(
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key=AttributeKey(type=AttributeKey.TYPE_BOOLEAN, name="sentry.is_segment"),
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op=ComparisonFilter.OP_EQUALS,
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value=AttributeValue(val_bool=True),
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)
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),
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extrapolation_mode=extrapolation_mode,
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)
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)
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# Calculate (Satisfactory + Tolerable/2) / Total
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numerator = Column(
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formula=Column.BinaryFormula(
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left=satisfactory,
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op=Column.BinaryFormula.OP_ADD,
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right=Column(
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formula=Column.BinaryFormula(
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left=tolerable,
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op=Column.BinaryFormula.OP_DIVIDE,
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right=Column(literal=LiteralValue(val_double=2.0)),
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)
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),
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)
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)
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return Column.BinaryFormula(
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left=numerator,
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op=Column.BinaryFormula.OP_DIVIDE,
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right=total,
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)
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def user_misery(args: ResolvedArguments, settings: ResolverSettings) -> Column.BinaryFormula:
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"""
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Calculate User Misery score based on response time field and threshold.
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User Misery = (miserable_users + α) / (total_unique_users + α + β)
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Where:
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- miserable_users: unique users with response time > 4T
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- total_unique_users: total unique users with response time field
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- α (MISERY_ALPHA) = 5.8875
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- β (MISERY_BETA) = 111.8625
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"""
939+
extrapolation_mode = settings["extrapolation_mode"]
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# Get the response time field and threshold
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response_time_field = cast(AttributeKey, args[0])
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threshold = cast(float, args[1])
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# Calculate 4T for miserable threshold
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miserable_threshold = threshold * 4
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# Count miserable users: unique users with response time > 4T and is_transaction = True
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miserable_users = Column(
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conditional_aggregation=AttributeConditionalAggregation(
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aggregate=Function.FUNCTION_UNIQ,
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key=AttributeKey(type=AttributeKey.TYPE_STRING, name="sentry.user"),
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filter=TraceItemFilter(
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and_filter=AndFilter(
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filters=[
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TraceItemFilter(
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comparison_filter=ComparisonFilter(
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key=response_time_field,
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op=ComparisonFilter.OP_GREATER_THAN,
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value=AttributeValue(val_double=miserable_threshold),
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)
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),
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TraceItemFilter(
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comparison_filter=ComparisonFilter(
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key=AttributeKey(
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type=AttributeKey.TYPE_BOOLEAN, name="sentry.is_segment"
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),
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op=ComparisonFilter.OP_EQUALS,
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value=AttributeValue(val_bool=True),
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)
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),
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]
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)
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),
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extrapolation_mode=extrapolation_mode,
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)
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)
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# Count total unique users: unique users with response time field and is_transaction = True
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total_unique_users = Column(
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conditional_aggregation=AttributeConditionalAggregation(
982+
aggregate=Function.FUNCTION_UNIQ,
983+
key=AttributeKey(type=AttributeKey.TYPE_STRING, name="sentry.user"),
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filter=TraceItemFilter(
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and_filter=AndFilter(
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filters=[
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TraceItemFilter(
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exists_filter=ExistsFilter(key=response_time_field),
989+
),
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TraceItemFilter(
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comparison_filter=ComparisonFilter(
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key=AttributeKey(
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type=AttributeKey.TYPE_BOOLEAN, name="sentry.is_segment"
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),
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op=ComparisonFilter.OP_EQUALS,
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value=AttributeValue(val_bool=True),
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)
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),
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]
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)
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),
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extrapolation_mode=extrapolation_mode,
1003+
)
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)
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# Calculate (miserable_users + α) / (total_unique_users + α + β)
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numerator = Column(
1008+
formula=Column.BinaryFormula(
1009+
left=miserable_users,
1010+
op=Column.BinaryFormula.OP_ADD,
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right=Column(literal=LiteralValue(val_double=MISERY_ALPHA)),
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)
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)
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denominator = Column(
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formula=Column.BinaryFormula(
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left=total_unique_users,
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op=Column.BinaryFormula.OP_ADD,
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right=Column(literal=LiteralValue(val_double=MISERY_ALPHA + MISERY_BETA)),
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)
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)
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return Column.BinaryFormula(
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left=numerator,
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op=Column.BinaryFormula.OP_DIVIDE,
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right=denominator,
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)
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7981030
SPAN_FORMULA_DEFINITIONS = {
7991031
"http_response_rate": FormulaDefinition(
8001032
default_search_type="percentage",
@@ -987,4 +1219,34 @@ def eps(_: ResolvedArguments, settings: ResolverSettings) -> Column.BinaryFormul
9871219
"eps": FormulaDefinition(
9881220
default_search_type="rate", arguments=[], formula_resolver=eps, is_aggregate=True
9891221
),
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"apdex": FormulaDefinition(
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default_search_type="number",
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infer_search_type_from_arguments=False,
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arguments=[
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AttributeArgumentDefinition(
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attribute_types={
1228+
"duration",
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*constants.DURATION_TYPE,
1230+
},
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),
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ValueArgumentDefinition(argument_types={"number"}),
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],
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formula_resolver=apdex,
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is_aggregate=True,
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),
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"user_misery": FormulaDefinition(
1238+
default_search_type="number",
1239+
infer_search_type_from_arguments=False,
1240+
arguments=[
1241+
AttributeArgumentDefinition(
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attribute_types={
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"duration",
1244+
*constants.DURATION_TYPE,
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},
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),
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ValueArgumentDefinition(argument_types={"number"}),
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],
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formula_resolver=user_misery,
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is_aggregate=True,
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),
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}

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