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373 lines
14 KiB
Python
373 lines
14 KiB
Python
# isort: skip_file
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# Needs to be first to set up django environment
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from .helpers import *
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from datetime import timedelta
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from typing import List, Tuple
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from ee.clickhouse.materialized_columns import backfill_materialized_columns, get_materialized_columns, materialize
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from ee.clickhouse.queries.clickhouse_stickiness import ClickhouseStickiness
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from ee.clickhouse.queries.funnels.funnel_correlation import FunnelCorrelation
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from ee.clickhouse.queries.trends.clickhouse_trends import ClickhouseTrends
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from ee.clickhouse.queries.session_recordings.clickhouse_session_recording_list import ClickhouseSessionRecordingList
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from posthog.models import Action, ActionStep, Cohort, Team, Organization
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from posthog.models.filters.session_recordings_filter import SessionRecordingsFilter
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from posthog.models.filters.stickiness_filter import StickinessFilter
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from posthog.models.filters.filter import Filter
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from posthog.models.property import PropertyName, TableWithProperties
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from posthog.constants import FunnelCorrelationType
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MATERIALIZED_PROPERTIES: List[Tuple[TableWithProperties, PropertyName]] = [
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("events", "$host"),
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("events", "$current_url"),
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("events", "$event_type"),
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("person", "email"),
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("person", "$browser"),
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]
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DATE_RANGE = {"date_from": "2021-01-01", "date_to": "2021-10-01", "interval": "week"}
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SHORT_DATE_RANGE = {"date_from": "2021-07-01", "date_to": "2021-10-01", "interval": "week"}
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SESSIONS_DATE_RANGE = {"date_from": "2021-11-17", "date_to": "2021-11-22"}
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class QuerySuite:
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timeout = 3000.0 # Timeout for the whole suite
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version = "v001" # Version. Incrementing this will invalidate previous results
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team: Team
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cohort: Cohort
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@benchmark_clickhouse
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def track_trends_no_filter(self):
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filter = Filter(data={"events": [{"id": "$pageview"}], **DATE_RANGE})
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ClickhouseTrends().run(filter, self.team)
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@benchmark_clickhouse
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def track_trends_event_property_filter(self):
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filter = Filter(
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data={
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"events": [{"id": "$pageview"}],
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"properties": [
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{
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"key": "$host",
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"operator": "is_not",
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"value": [
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"localhost:8000",
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"localhost:5000",
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"127.0.0.1:8000",
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"127.0.0.1:3000",
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"localhost:3000",
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],
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}
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],
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**SHORT_DATE_RANGE,
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}
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)
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with no_materialized_columns():
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ClickhouseTrends().run(filter, self.team)
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@benchmark_clickhouse
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def track_trends_event_property_filter_materialized(self):
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filter = Filter(
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data={
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"events": [{"id": "$pageview"}],
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"properties": [
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{
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"key": "$host",
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"operator": "is_not",
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"value": [
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"localhost:8000",
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"localhost:5000",
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"127.0.0.1:8000",
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"127.0.0.1:3000",
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"localhost:3000",
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],
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}
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],
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**DATE_RANGE,
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}
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)
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ClickhouseTrends().run(filter, self.team)
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@benchmark_clickhouse
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def track_trends_person_property_filter(self):
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filter = Filter(
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data={
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"events": [{"id": "$pageview"}],
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"properties": [{"key": "email", "operator": "icontains", "value": ".com", "type": "person"}],
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**DATE_RANGE,
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}
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)
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with no_materialized_columns():
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ClickhouseTrends().run(filter, self.team)
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@benchmark_clickhouse
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def track_trends_person_property_filter_materialized(self):
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filter = Filter(
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data={
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"events": [{"id": "$pageview"}],
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"properties": [{"key": "email", "operator": "icontains", "value": ".com", "type": "person"}],
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**DATE_RANGE,
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}
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)
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ClickhouseTrends().run(filter, self.team)
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@benchmark_clickhouse
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def track_trends_filter_by_cohort_precalculated(self):
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self.cohort.last_calculation = now()
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self.cohort.save()
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filter = Filter(
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data={
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"events": [{"id": "$pageview"}],
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"properties": [{"key": "id", "value": self.cohort.pk, "type": "cohort"}],
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**DATE_RANGE,
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},
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team=self.team,
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)
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ClickhouseTrends().run(filter, self.team)
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@benchmark_clickhouse
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def track_trends_filter_by_cohort(self):
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self.cohort.last_calculation = None
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self.cohort.save()
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filter = Filter(
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data={
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"events": [{"id": "$pageview"}],
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"properties": [{"key": "id", "value": self.cohort.pk, "type": "cohort"}],
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**DATE_RANGE,
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},
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team=self.team,
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)
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with no_materialized_columns():
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ClickhouseTrends().run(filter, self.team)
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@benchmark_clickhouse
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def track_trends_filter_by_cohort_materialized(self):
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self.cohort.last_calculation = None
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self.cohort.save()
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filter = Filter(
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data={
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"events": [{"id": "$pageview"}],
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"properties": [{"key": "id", "value": self.cohort.pk, "type": "cohort"}],
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**DATE_RANGE,
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},
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team=self.team,
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)
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ClickhouseTrends().run(filter, self.team)
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@benchmark_clickhouse
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def track_trends_filter_by_action_current_url_materialized(self):
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action = Action.objects.create(team=self.team, name="docs view")
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ActionStep.objects.create(
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action=action, event="$pageview", url="docs", url_matching="contains",
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)
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filter = Filter(data={"actions": [{"id": action.id}], **DATE_RANGE}, team=self.team)
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ClickhouseTrends().run(filter, self.team)
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@benchmark_clickhouse
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def track_trends_filter_by_action_current_url(self):
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action = Action.objects.create(team=self.team, name="docs view")
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ActionStep.objects.create(
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action=action, event="$pageview", url="docs", url_matching="contains",
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)
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filter = Filter(data={"actions": [{"id": action.id}], **DATE_RANGE}, team=self.team)
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with no_materialized_columns():
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ClickhouseTrends().run(filter, self.team)
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@benchmark_clickhouse
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def track_trends_filter_by_action_with_person_filters_materialized(self):
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action = Action.objects.create(team=self.team, name=".com-users page views")
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ActionStep.objects.create(
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action=action,
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event="$pageview",
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properties=[{"key": "email", "operator": "icontains", "value": ".com", "type": "person"}],
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)
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filter = Filter(data={"actions": [{"id": action.id}], **DATE_RANGE}, team=self.team)
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ClickhouseTrends().run(filter, self.team)
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@benchmark_clickhouse
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def track_trends_filter_by_action_with_person_filters(self):
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action = Action.objects.create(team=self.team, name=".com-users page views")
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ActionStep.objects.create(
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action=action,
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event="$pageview",
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properties=[{"key": "email", "operator": "icontains", "value": ".com", "type": "person"}],
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)
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filter = Filter(data={"actions": [{"id": action.id}], **DATE_RANGE}, team=self.team)
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with no_materialized_columns():
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ClickhouseTrends().run(filter, self.team)
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@benchmark_clickhouse
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def track_correlations_by_events(self):
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filter = Filter(
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data={"events": [{"id": "user signed up"}, {"id": "insight analyzed"}], **SHORT_DATE_RANGE,}, team=self.team
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)
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FunnelCorrelation(filter, self.team).run()
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@benchmark_clickhouse
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def track_correlations_by_properties_materialized(self):
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filter = Filter(
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data={
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"events": [{"id": "user signed up"}, {"id": "insight analyzed"}],
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**SHORT_DATE_RANGE,
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"funnel_correlation_type": FunnelCorrelationType.PROPERTIES,
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"funnel_correlation_names": ["$browser"],
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},
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team=self.team,
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)
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FunnelCorrelation(filter, self.team).run()
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@benchmark_clickhouse
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def track_correlations_by_properties(self):
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filter = Filter(
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data={
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"events": [{"id": "user signed up"}, {"id": "insight analyzed"}],
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**SHORT_DATE_RANGE,
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"funnel_correlation_type": FunnelCorrelationType.PROPERTIES,
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"funnel_correlation_names": ["$browser"],
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},
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team=self.team,
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)
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with no_materialized_columns():
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FunnelCorrelation(filter, self.team).run()
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@benchmark_clickhouse
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def track_correlations_by_event_properties(self):
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filter = Filter(
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data={
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"events": [{"id": "user signed up"}, {"id": "insight analyzed"}],
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**SHORT_DATE_RANGE,
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"funnel_correlation_type": FunnelCorrelationType.EVENT_WITH_PROPERTIES,
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"funnel_correlation_event_names": ["$autocapture"],
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},
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team=self.team,
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)
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with no_materialized_columns():
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FunnelCorrelation(filter, self.team).run()
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@benchmark_clickhouse
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def track_correlations_by_event_properties_materialized(self):
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filter = Filter(
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data={
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"events": [{"id": "user signed up"}, {"id": "insight analyzed"}],
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**SHORT_DATE_RANGE,
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"funnel_correlation_type": FunnelCorrelationType.EVENT_WITH_PROPERTIES,
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"funnel_correlation_event_names": ["$autocapture"],
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},
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team=self.team,
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)
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FunnelCorrelation(filter, self.team).run()
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@benchmark_clickhouse
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def track_stickiness(self):
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filter = StickinessFilter(
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data={
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"insight": "STICKINESS",
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"events": [{"id": "$pageview"}],
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"shown_as": "Stickiness",
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"display": "ActionsLineGraph",
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**DATE_RANGE,
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},
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team=self.team,
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)
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ClickhouseStickiness().run(filter, self.team)
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@benchmark_clickhouse
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def track_stickiness_filter_by_person_property(self):
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filter = StickinessFilter(
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data={
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"insight": "STICKINESS",
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"events": [{"id": "$pageview"}],
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"shown_as": "Stickiness",
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"display": "ActionsLineGraph",
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"properties": [{"key": "email", "operator": "icontains", "value": ".com", "type": "person"}],
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**DATE_RANGE,
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},
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team=self.team,
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)
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with no_materialized_columns():
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ClickhouseStickiness().run(filter, self.team)
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@benchmark_clickhouse
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def track_stickiness_filter_by_person_property_materialized(self):
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filter = StickinessFilter(
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data={
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"insight": "STICKINESS",
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"events": [{"id": "$pageview"}],
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"shown_as": "Stickiness",
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"display": "ActionsLineGraph",
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"properties": [{"key": "email", "operator": "icontains", "value": ".com", "type": "person"}],
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**DATE_RANGE,
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},
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team=self.team,
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)
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ClickhouseStickiness().run(filter, self.team)
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@benchmark_clickhouse
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def track_session_recordings_list(self):
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filter = SessionRecordingsFilter(data=SESSIONS_DATE_RANGE, team=self.team,)
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ClickhouseSessionRecordingList(filter, self.team.pk).run()
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@benchmark_clickhouse
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def track_session_recordings_list_event_filter(self):
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filter = SessionRecordingsFilter(data={"events": [{"id": "$pageview"}], **SESSIONS_DATE_RANGE}, team=self.team,)
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ClickhouseSessionRecordingList(filter, self.team.pk).run()
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@benchmark_clickhouse
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def track_session_recordings_list_person_property_filter(self):
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filter = SessionRecordingsFilter(
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data={
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"events": [
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{
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"id": "$pageview",
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"properties": [{"key": "email", "operator": "icontains", "value": ".com", "type": "person"}],
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}
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],
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**SESSIONS_DATE_RANGE,
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},
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team=self.team,
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)
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ClickhouseSessionRecordingList(filter, self.team.pk).run()
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def setup(self):
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for table, property in MATERIALIZED_PROPERTIES:
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if property not in get_materialized_columns(table):
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materialize(table, property)
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backfill_materialized_columns(table, [property], backfill_period=timedelta(days=1_000))
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# :TRICKY: Data in benchmark servers has ID=2
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team = Team.objects.filter(id=2).first()
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if team is None:
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organization = Organization.objects.create()
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team = Team.objects.create(id=2, organization=organization, name="The Bakery")
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self.team = team
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cohort = Cohort.objects.filter(name="benchmarking cohort").first()
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if cohort is None:
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cohort = Cohort.objects.create(
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team_id=2,
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name="benchmarking cohort",
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groups=[{"properties": [{"key": "email", "operator": "icontains", "value": ".com", "type": "person"}]}],
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)
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cohort.calculate_people_ch()
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self.cohort = cohort
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