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merged 5 commits into from
Jul 7, 2025
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feat(explore): Add support for apdex and user misery #94919

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cf4679a
check in progress
shruthilayaj Jul 3, 2025
0f94930
test
shruthilayaj Jul 4, 2025
31e61ef
add user misery
shruthilayaj Jul 4, 2025
8d5a551
add existance of response time field to count unique
shruthilayaj Jul 4, 2025
9b796cc
cast to float
shruthilayaj Jul 4, 2025
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264 changes: 263 additions & 1 deletion src/sentry/search/eap/spans/formulas.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,11 @@
)
from sentry.search.eap.types import SearchResolverConfig
from sentry.search.eap.utils import literal_validator
from sentry.search.events.constants import WEB_VITALS_PERFORMANCE_SCORE_WEIGHTS
from sentry.search.events.constants import (
MISERY_ALPHA,
MISERY_BETA,
WEB_VITALS_PERFORMANCE_SCORE_WEIGHTS,
)
from sentry.snuba import spans_rpc
from sentry.snuba.referrer import Referrer

Expand Down Expand Up @@ -795,6 +799,234 @@ def eps(_: ResolvedArguments, settings: ResolverSettings) -> Column.BinaryFormul
)


def apdex(args: ResolvedArguments, settings: ResolverSettings) -> Column.BinaryFormula:
"""
Calculate Apdex score based on response time field and threshold.

Apdex = (Satisfactory + Tolerable/2) / Total Requests
Where:
- Satisfactory: response time ≤ T
- Tolerable: response time > T and ≤ 4T
- Frustrated: response time > 4T
"""
extrapolation_mode = settings["extrapolation_mode"]

# Get the response time field and threshold
response_time_field = cast(AttributeKey, args[0])
threshold = cast(float, args[1])

# Calculate 4T for tolerable range
tolerable_threshold = threshold * 4

# Satisfactory requests: response time ≤ T and is_transaction = True
satisfactory = Column(
conditional_aggregation=AttributeConditionalAggregation(
aggregate=Function.FUNCTION_COUNT,
key=response_time_field,
filter=TraceItemFilter(
and_filter=AndFilter(
filters=[
TraceItemFilter(
comparison_filter=ComparisonFilter(
key=response_time_field,
op=ComparisonFilter.OP_LESS_THAN_OR_EQUALS,
value=AttributeValue(val_double=threshold),
)
),
TraceItemFilter(
comparison_filter=ComparisonFilter(
key=AttributeKey(
type=AttributeKey.TYPE_BOOLEAN, name="sentry.is_segment"
),
op=ComparisonFilter.OP_EQUALS,
value=AttributeValue(val_bool=True),
)
),
]
)
),
extrapolation_mode=extrapolation_mode,
)
)

# Tolerable requests: response time > T and ≤ 4T and is_transaction = True
tolerable = Column(
conditional_aggregation=AttributeConditionalAggregation(
aggregate=Function.FUNCTION_COUNT,
key=response_time_field,
filter=TraceItemFilter(
and_filter=AndFilter(
filters=[
TraceItemFilter(
comparison_filter=ComparisonFilter(
key=response_time_field,
op=ComparisonFilter.OP_GREATER_THAN,
value=AttributeValue(val_double=threshold),
)
),
TraceItemFilter(
comparison_filter=ComparisonFilter(
key=response_time_field,
op=ComparisonFilter.OP_LESS_THAN_OR_EQUALS,
value=AttributeValue(val_double=tolerable_threshold),
)
),
TraceItemFilter(
comparison_filter=ComparisonFilter(
key=AttributeKey(
type=AttributeKey.TYPE_BOOLEAN, name="sentry.is_segment"
),
op=ComparisonFilter.OP_EQUALS,
value=AttributeValue(val_bool=True),
)
),
]
)
),
extrapolation_mode=extrapolation_mode,
)
)

# Total requests: count of all requests with the response time field and is_transaction = True
total = Column(
conditional_aggregation=AttributeConditionalAggregation(
aggregate=Function.FUNCTION_COUNT,
key=response_time_field,
filter=TraceItemFilter(
comparison_filter=ComparisonFilter(
key=AttributeKey(type=AttributeKey.TYPE_BOOLEAN, name="sentry.is_segment"),
op=ComparisonFilter.OP_EQUALS,
value=AttributeValue(val_bool=True),
)
),
extrapolation_mode=extrapolation_mode,
)
)

# Calculate (Satisfactory + Tolerable/2) / Total
numerator = Column(
formula=Column.BinaryFormula(
left=satisfactory,
op=Column.BinaryFormula.OP_ADD,
right=Column(
formula=Column.BinaryFormula(
left=tolerable,
op=Column.BinaryFormula.OP_DIVIDE,
right=Column(literal=LiteralValue(val_double=2.0)),
)
),
)
)

return Column.BinaryFormula(
left=numerator,
op=Column.BinaryFormula.OP_DIVIDE,
right=total,
)


def user_misery(args: ResolvedArguments, settings: ResolverSettings) -> Column.BinaryFormula:
"""
Calculate User Misery score based on response time field and threshold.

User Misery = (miserable_users + α) / (total_unique_users + α + β)
Where:
- miserable_users: unique users with response time > 4T
- total_unique_users: total unique users with response time field
- α (MISERY_ALPHA) = 5.8875
- β (MISERY_BETA) = 111.8625
"""
extrapolation_mode = settings["extrapolation_mode"]

# Get the response time field and threshold
response_time_field = cast(AttributeKey, args[0])
threshold = cast(float, args[1])

# Calculate 4T for miserable threshold
miserable_threshold = threshold * 4

# Count miserable users: unique users with response time > 4T and is_transaction = True
miserable_users = Column(
conditional_aggregation=AttributeConditionalAggregation(
aggregate=Function.FUNCTION_UNIQ,
key=AttributeKey(type=AttributeKey.TYPE_STRING, name="sentry.user"),
filter=TraceItemFilter(
and_filter=AndFilter(
filters=[
TraceItemFilter(
comparison_filter=ComparisonFilter(
key=response_time_field,
op=ComparisonFilter.OP_GREATER_THAN,
value=AttributeValue(val_double=miserable_threshold),
)
),
TraceItemFilter(
comparison_filter=ComparisonFilter(
key=AttributeKey(
type=AttributeKey.TYPE_BOOLEAN, name="sentry.is_segment"
),
op=ComparisonFilter.OP_EQUALS,
value=AttributeValue(val_bool=True),
)
),
]
)
),
extrapolation_mode=extrapolation_mode,
)
)

# Count total unique users: unique users with response time field and is_transaction = True
total_unique_users = Column(
conditional_aggregation=AttributeConditionalAggregation(
aggregate=Function.FUNCTION_UNIQ,
key=AttributeKey(type=AttributeKey.TYPE_STRING, name="sentry.user"),
filter=TraceItemFilter(
and_filter=AndFilter(
filters=[
TraceItemFilter(
exists_filter=ExistsFilter(key=response_time_field),
),
TraceItemFilter(
comparison_filter=ComparisonFilter(
key=AttributeKey(
type=AttributeKey.TYPE_BOOLEAN, name="sentry.is_segment"
),
op=ComparisonFilter.OP_EQUALS,
value=AttributeValue(val_bool=True),
)
),
]
)
),
extrapolation_mode=extrapolation_mode,
)
)

# Calculate (miserable_users + α) / (total_unique_users + α + β)
numerator = Column(
formula=Column.BinaryFormula(
left=miserable_users,
op=Column.BinaryFormula.OP_ADD,
right=Column(literal=LiteralValue(val_double=MISERY_ALPHA)),
)
)

denominator = Column(
formula=Column.BinaryFormula(
left=total_unique_users,
op=Column.BinaryFormula.OP_ADD,
right=Column(literal=LiteralValue(val_double=MISERY_ALPHA + MISERY_BETA)),
)
)

return Column.BinaryFormula(
left=numerator,
op=Column.BinaryFormula.OP_DIVIDE,
right=denominator,
)


SPAN_FORMULA_DEFINITIONS = {
"http_response_rate": FormulaDefinition(
default_search_type="percentage",
Expand Down Expand Up @@ -987,4 +1219,34 @@ def eps(_: ResolvedArguments, settings: ResolverSettings) -> Column.BinaryFormul
"eps": FormulaDefinition(
default_search_type="rate", arguments=[], formula_resolver=eps, is_aggregate=True
),
"apdex": FormulaDefinition(
default_search_type="number",
infer_search_type_from_arguments=False,
arguments=[
AttributeArgumentDefinition(
attribute_types={
"duration",
*constants.DURATION_TYPE,
},
),
ValueArgumentDefinition(argument_types={"number"}),
],
formula_resolver=apdex,
is_aggregate=True,
),
"user_misery": FormulaDefinition(
default_search_type="number",
infer_search_type_from_arguments=False,
arguments=[
AttributeArgumentDefinition(
attribute_types={
"duration",
*constants.DURATION_TYPE,
},
),
ValueArgumentDefinition(argument_types={"number"}),
],
formula_resolver=user_misery,
is_aggregate=True,
),
}
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