def test_segments_megaquery_not_detectably_malformed():
exp = Experiment("slug", "2019-01-01", 8)
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,
)
enrollments_sql = exp.build_enrollments_query(
time_limits=tl,
segment_list=[s for s in msd.__dict__.values() if isinstance(s, msd.Segment)],
enrollments_query_type="normandy",
)
sql_lint(enrollments_sql)
metrics_sql = exp.build_metrics_query(
metric_list=[m for m in mad.__dict__.values() if isinstance(m, mad.Metric)],
time_limits=tl,
enrollments_table="enrollments",
)
sql_lint(metrics_sql)
def test_exposure_signal_query_custom_windows():
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,
)
enrollment_sql = exp.build_enrollments_query(
time_limits=tl,
enrollments_query_type="glean-event",
exposure_signal=ExposureSignal(
name="exposures",
data_source=mozanalysis.metrics.fenix.baseline,
select_expr="metrics.counter.events_total_uri_count > 0",
friendly_name="URI visited exposure",
description="Exposed when URI visited",
window_start=1,
window_end=3,
),
)
sql_lint(enrollment_sql)
assert "exposures" in enrollment_sql
assert "metrics.counter.events_total_uri_count > 0" in enrollment_sql
assert "DATE_ADD('2019-01-01', INTERVAL 1 DAY)" in enrollment_sql
assert "DATE_ADD('2019-01-01', INTERVAL 3 DAY)" in enrollment_sql
def test_sql_not_detectably_malformed(included_segments,
included_segment_datasources):
for _name, s in included_segments:
sql_lint(s.select_expr)
for _name, sds in included_segment_datasources:
sql_lint(sds.from_expr_for(None))
def test_metrics_query_based_on_exposure():
exp = Experiment("slug", "2019-01-01", 8)
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,
)
enrollments_sql = exp.build_enrollments_query(
time_limits=tl, enrollments_query_type="fenix-fallback")
sql_lint(enrollments_sql)
metrics_sql = exp.build_metrics_query(
metric_list=[
m for m in mozanalysis.metrics.fenix.__dict__.values()
if isinstance(m, Metric)
],
time_limits=tl,
enrollments_table="enrollments",
analysis_basis=AnalysisBasis.EXPOSURES,
)
sql_lint(metrics_sql)
assert "e.exposure_date" in metrics_sql
def test_sql_not_detectably_malformed():
for m in mmd.__dict__.values():
if isinstance(m, mmd.Metric):
sql_lint(m.select_expr.format(experiment_slug='slug'))
for ds in mmd.__dict__.values():
if isinstance(ds, mmd.DataSource):
sql_lint(ds.from_expr)
def test_sql_not_detectably_malformed():
for m in msd.__dict__.values():
if isinstance(m, msd.Segment):
sql_lint(m.select_expr)
for ds in msd.__dict__.values():
if isinstance(ds, msd.SegmentDataSource):
sql_lint(ds.from_expr)
def test_megaquery_not_detectably_malformed():
exp = Experiment('slug', '2019-01-01', 8)
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
)
sql = exp.build_query(
metric_list=[m for m in mad.__dict__.values() if isinstance(m, mad.Metric)],
time_limits=tl,
enrollments_query_type='normandy',
)
sql_lint(sql)
def test_query_not_detectably_malformed():
exp = Experiment('slug', '2019-01-01', 8)
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
)
sql = exp.build_query(
metric_list=[],
time_limits=tl,
enrollments_query_type='normandy',
)
sql_lint(sql)
def test_exposure_query():
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,
)
enrollment_sql = exp.build_enrollments_query(
time_limits=tl,
enrollments_query_type="glean-event",
)
sql_lint(enrollment_sql)
assert "exposures" in enrollment_sql
def test_query_not_detectably_malformed_fenix_fallback():
exp = Experiment("slug", "2019-01-01", 8)
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,
)
enrollments_sql = exp.build_enrollments_query(
time_limits=tl, enrollments_query_type="fenix-fallback")
sql_lint(enrollments_sql)
metrics_sql = exp.build_metrics_query(
metric_list=[],
time_limits=tl,
enrollments_table="enrollments",
)
sql_lint(metrics_sql)
def test_metrics_query_with_exposure_signal_custom_windows():
exp = Experiment("slug", "2019-01-01", 8)
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,
)
enrollments_sql = exp.build_enrollments_query(
time_limits=tl, enrollments_query_type="fenix-fallback")
sql_lint(enrollments_sql)
metrics_sql = exp.build_metrics_query(
metric_list=[
m for m in mozanalysis.metrics.fenix.__dict__.values()
if isinstance(m, Metric)
],
time_limits=tl,
enrollments_table="enrollments",
analysis_basis=AnalysisBasis.EXPOSURES,
exposure_signal=ExposureSignal(
name="exposures",
data_source=mozanalysis.metrics.fenix.baseline,
select_expr="metrics.counter.events_total_uri_count > 0",
friendly_name="URI visited exposure",
description="Exposed when URI visited",
window_start=1,
window_end=3,
),
)
sql_lint(metrics_sql)
assert "DATE_ADD('2019-01-01', INTERVAL 1 DAY)" in metrics_sql
assert "DATE_ADD('2019-01-01', INTERVAL 3 DAY)" in metrics_sql
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_sql_not_detectably_malformed(included_metrics, included_datasources):
for _, m in included_metrics:
sql_lint(m.select_expr.format(experiment_slug="slug"))
for _, ds in included_datasources:
sql_lint(ds.from_expr_for(None))
