def test_firefox_ios_klar_app_id_propagation():
exp = Experiment("slug", "2019-01-01", 8, app_id="my_cool_app")
tl = TimeLimits.for_ts(
first_enrollment_date="2019-01-01",
last_date_full_data="2019-03-01",
time_series_period="weekly",
num_dates_enrollment=8,
)
sds = SegmentDataSource(
name="cool_data_source",
from_expr="`moz-fx-data-shared-prod`.{dataset}.cool_table",
default_dataset="org_mozilla_ios_klar",
)
segment = Segment(
name="cool_segment",
select_expr="COUNT(*)",
data_source=sds,
)
enrollments_sql = exp.build_enrollments_query(
time_limits=tl,
segment_list=[segment],
enrollments_query_type="glean-event",
)
sql_lint(enrollments_sql)
metrics_sql = exp.build_metrics_query(
metric_list=[
m for m in mozanalysis.metrics.klar_ios.__dict__.values()
if isinstance(m, Metric)
],
time_limits=tl,
enrollments_table="enrollments",
)
sql_lint(metrics_sql)
assert "org_mozilla_ios_klar" not in enrollments_sql
assert "my_cool_app" in enrollments_sql
sql_lint(metrics_sql)
def test_with_segments(self, client, project_id, static_dataset,
temporary_dataset):
experiment = Experiment(
experimenter_slug="test-experiment",
type="rollout",
status="Live",
start_date=dt.datetime(2020, 3, 30, tzinfo=pytz.utc),
end_date=dt.datetime(2020, 6, 1, tzinfo=pytz.utc),
proposed_enrollment=7,
branches=[
Branch(slug="branch1", ratio=0.5),
Branch(slug="branch2", ratio=0.5)
],
reference_branch="branch2",
features=[],
normandy_slug="test-experiment",
)
config = AnalysisSpec().resolve(experiment)
test_clients_daily = DataSource(
name="clients_daily",
from_expr=f"`{project_id}.test_data.clients_daily`",
)
test_active_hours = Metric(
name="active_hours",
data_source=test_clients_daily,
select_expr=agg_sum("active_hours_sum"),
)
test_clients_last_seen = SegmentDataSource(
"clients_last_seen", f"`{project_id}.test_data.clients_last_seen`")
regular_user_v3 = Segment(
"regular_user_v3",
test_clients_last_seen,
"COALESCE(LOGICAL_OR(is_regular_user_v3), FALSE)",
)
config.experiment.segments = [regular_user_v3]
config.metrics = {
AnalysisPeriod.WEEK: [Summary(test_active_hours, BootstrapMean())]
}
self.analysis_mock_run(config, static_dataset, temporary_dataset,
project_id)
query_job = client.client.query(f"""
SELECT
*
FROM `{project_id}.{temporary_dataset}.test_experiment_week_1`
ORDER BY enrollment_date DESC
""")
expected_metrics_results = [
{
"client_id": "bbbb",
"branch": "branch2",
"enrollment_date": datetime.date(2020, 4, 3),
"num_enrollment_events": 1,
"analysis_window_start": 0,
"analysis_window_end": 6,
"regular_user_v3": True,
},
{
"client_id": "aaaa",
"branch": "branch1",
"enrollment_date": datetime.date(2020, 4, 2),
"num_enrollment_events": 1,
"analysis_window_start": 0,
"analysis_window_end": 6,
"regular_user_v3": False,
},
]
for i, row in enumerate(query_job.result()):
for k, v in expected_metrics_results[i].items():
assert row[k] == v
assert (client.client.get_table(
f"{project_id}.{temporary_dataset}.test_experiment_weekly")
is not None)
assert (client.client.get_table(
f"{project_id}.{temporary_dataset}.statistics_test_experiment_week_1"
) is not None)
stats = client.client.list_rows(
f"{project_id}.{temporary_dataset}.statistics_test_experiment_week_1"
).to_dataframe()
count_by_branch = stats.query(
"segment == 'all' and statistic == 'count'").set_index("branch")
assert count_by_branch.loc["branch1", "point"] == 1.0
assert count_by_branch.loc["branch2", "point"] == 1.0
assert len(stats.query("segment == 'regular_user_v3'")) > 0
assert (client.client.get_table(
f"{project_id}.{temporary_dataset}.statistics_test_experiment_weekly"
) is not None)
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
from mozanalysis.segments import Segment, SegmentDataSource
clients_last_seen = SegmentDataSource(
name='clients_last_seen',
from_expr="`moz-fx-data-shared-prod.telemetry.clients_last_seen`",
window_start=0,
window_end=0,
)
regular_users_v3 = Segment(
name='regular_users_v3',
data_source=clients_last_seen,
select_expr="""MAX(
BIT_COUNT(COALESCE(days_seen_bits, 0) & 0x0FFFFFFE) >= 14
)""",
)
new_or_resurrected_v3 = Segment(
name='new_or_resurrected_v3',
data_source=clients_last_seen,
select_expr="""MAX(
BIT_COUNT(COALESCE(days_seen_bits, 0) & 0x0FFFFFFE) = 0
)""",
)
from mozanalysis.metrics import agg_any
from mozanalysis.segments import Segment, SegmentDataSource
clients_last_seen = SegmentDataSource(
name="clients_last_seen",
from_expr="`moz-fx-data-shared-prod.telemetry.clients_last_seen`",
window_start=0,
window_end=0,
)
regular_users_v3 = Segment(
name="regular_users_v3",
data_source=clients_last_seen,
select_expr=agg_any("is_regular_user_v3"),
friendly_name="Regular users (v3)",
description=dedent("""\
Clients who used Firefox on at least 14 of the 27 days prior to enrolling.
This segment is characterized by high retention.
"""),
)
new_or_resurrected_v3 = Segment(
name="new_or_resurrected_v3",
data_source=clients_last_seen,
select_expr="LOGICAL_OR(COALESCE(is_new_or_resurrected_v3, TRUE))",
friendly_name="New or resurrected users (v3)",
description=dedent("""\
Clients who used Firefox on none of the 27 days prior to enrolling.
"""),
)
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
from mozanalysis.metrics import agg_any
from mozanalysis.segments import Segment, SegmentDataSource
clients_last_seen = SegmentDataSource(
name='clients_last_seen',
from_expr="`moz-fx-data-shared-prod.telemetry.clients_last_seen`",
window_start=0,
window_end=0,
)
regular_users_v3 = Segment(
name='regular_users_v3',
data_source=clients_last_seen,
select_expr=agg_any('is_regular_user_v3'),
)
new_or_resurrected_v3 = Segment(
name='new_or_resurrected_v3',
data_source=clients_last_seen,
select_expr="LOGICAL_OR(COALESCE(is_new_or_resurrected_v3, TRUE))",
)
weekday_regular_v1 = Segment(
name='weekday_regular_v1',
data_source=clients_last_seen,
select_expr=agg_any('is_weekday_regular_v1'),
)