from datetime import datetime, timedelta, timezone from typing import Literal, Mapping import pytest # from django.utils import timezone from snuba_sdk import ( Column, Condition, Direction, Metric, MetricsQuery, MetricsScope, Op, Request, Rollup, Timeseries, ) from sentry.sentry_metrics.use_case_id_registry import UseCaseID from sentry.snuba.metrics.naming_layer import TransactionMRI from sentry.snuba.metrics_layer.query import run_query from sentry.testutils.cases import BaseMetricsTestCase, TestCase pytestmark = pytest.mark.sentry_metrics class SnQLTest(TestCase, BaseMetricsTestCase): def ts(self, dt: datetime) -> int: return int(dt.timestamp()) def setUp(self): super().setUp() self.metrics: Mapping[str, Literal["counter", "set", "distribution"]] = { TransactionMRI.DURATION.value: "distribution", TransactionMRI.USER.value: "set", TransactionMRI.COUNT_PER_ROOT_PROJECT.value: "counter", } self.now = datetime.now(tz=timezone.utc) self.hour_ago = self.now - timedelta(hours=1) self.org_id = self.project.organization_id # Store a data point every 10 seconds for an hour for mri, metric_type in self.metrics.items(): assert metric_type in {"counter", "distribution", "set"} for i in range(360): self.store_metric( self.org_id, self.project.id, metric_type, mri, { "transaction": f"transaction_{i % 2}", "status_code": "500" if i % 10 == 0 else "200", "device": "BlackBerry" if i % 3 == 0 else "Nokia", }, self.ts(self.hour_ago + timedelta(minutes=1 * i)), i, UseCaseID.TRANSACTIONS, ) def test_basic(self) -> None: query = MetricsQuery( query=Timeseries( metric=Metric( "transaction.duration", TransactionMRI.DURATION.value, ), aggregate="max", ), start=self.hour_ago, end=self.now, rollup=Rollup(interval=60, granularity=60), scope=MetricsScope( org_ids=[self.org_id], project_ids=[self.project.id], use_case_id=UseCaseID.TRANSACTIONS.value, ), ) request = Request( dataset="generic_metrics", app_id="tests", query=query, tenant_ids={"referrer": "metrics.testing.test", "organization_id": self.org_id}, ) result = run_query(request) assert len(result["data"]) == 61 rows = result["data"] for i in range(61): assert rows[i]["aggregate_value"] == i assert ( rows[i]["time"] == ( self.hour_ago.replace(second=0, microsecond=0) + timedelta(minutes=1 * i) ).isoformat() ) def test_groupby(self) -> None: query = MetricsQuery( query=Timeseries( metric=Metric( "transaction.duration", TransactionMRI.DURATION.value, ), aggregate="quantiles", aggregate_params=[0.5, 0.99], groupby=[Column("transaction")], ), start=self.hour_ago, end=self.now, rollup=Rollup(interval=60, granularity=60), scope=MetricsScope( org_ids=[self.org_id], project_ids=[self.project.id], use_case_id=UseCaseID.TRANSACTIONS.value, ), ) request = Request( dataset="generic_metrics", app_id="tests", query=query, tenant_ids={"referrer": "metrics.testing.test", "organization_id": self.org_id}, ) result = run_query(request) assert len(result["data"]) == 61 rows = result["data"] for i in range(61): assert rows[i]["aggregate_value"] == [i, i] assert rows[i]["transaction"] == f"transaction_{i % 2}" assert ( rows[i]["time"] == ( self.hour_ago.replace(second=0, microsecond=0) + timedelta(minutes=1 * i) ).isoformat() ) def test_filters(self) -> None: query = MetricsQuery( query=Timeseries( metric=Metric( "transaction.duration", TransactionMRI.DURATION.value, ), aggregate="quantiles", aggregate_params=[0.5], filters=[Condition(Column("status_code"), Op.EQ, "500")], ), filters=[Condition(Column("device"), Op.EQ, "BlackBerry")], start=self.hour_ago, end=self.now, rollup=Rollup(interval=60, granularity=60), scope=MetricsScope( org_ids=[self.org_id], project_ids=[self.project.id], use_case_id=UseCaseID.TRANSACTIONS.value, ), ) request = Request( dataset="generic_metrics", app_id="tests", query=query, tenant_ids={"referrer": "metrics.testing.test", "organization_id": self.org_id}, ) result = run_query(request) assert len(result["data"]) == 3 rows = result["data"] for i in range(3): # 500 status codes on Blackberry are sparse assert rows[i]["aggregate_value"] == [i * 30] assert ( rows[i]["time"] == ( self.hour_ago.replace(second=0, microsecond=0) + timedelta(minutes=30 * i) ).isoformat() ) def test_complex(self) -> None: query = MetricsQuery( query=Timeseries( metric=Metric( "transaction.duration", TransactionMRI.DURATION.value, ), aggregate="quantiles", aggregate_params=[0.5], filters=[Condition(Column("status_code"), Op.EQ, "500")], groupby=[Column("transaction")], ), filters=[Condition(Column("device"), Op.EQ, "BlackBerry")], start=self.hour_ago, end=self.now, rollup=Rollup(interval=60, granularity=60), scope=MetricsScope( org_ids=[self.org_id], project_ids=[self.project.id], use_case_id=UseCaseID.TRANSACTIONS.value, ), ) request = Request( dataset="generic_metrics", app_id="tests", query=query, tenant_ids={"referrer": "metrics.testing.test", "organization_id": self.org_id}, ) result = run_query(request) assert len(result["data"]) == 3 rows = result["data"] for i in range(3): # 500 status codes on BB are sparse assert rows[i]["aggregate_value"] == [i * 30] assert rows[i]["transaction"] == "transaction_0" assert ( rows[i]["time"] == ( self.hour_ago.replace(second=0, microsecond=0) + timedelta(minutes=30 * i) ).isoformat() ) def test_totals(self) -> None: query = MetricsQuery( query=Timeseries( metric=Metric( "transaction.duration", TransactionMRI.DURATION.value, ), aggregate="max", filters=[Condition(Column("status_code"), Op.EQ, "200")], groupby=[Column("transaction")], ), start=self.hour_ago, end=self.now, rollup=Rollup(totals=True, granularity=60, orderby=Direction.ASC), scope=MetricsScope( org_ids=[self.org_id], project_ids=[self.project.id], use_case_id=UseCaseID.TRANSACTIONS.value, ), ) request = Request( dataset="generic_metrics", app_id="tests", query=query, tenant_ids={"referrer": "metrics.testing.test", "organization_id": self.org_id}, ) result = run_query(request) assert len(result["data"]) == 2 rows = result["data"] assert rows[0]["aggregate_value"] == 58 assert rows[0]["transaction"] == "transaction_0" assert rows[1]["aggregate_value"] == 59 assert rows[1]["transaction"] == "transaction_1"