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https://github.com/apache/superset.git
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More tests
This commit is contained in:
Beto Dealmeida
@@ -32,6 +32,7 @@ from superset.semantic_layers.mapper import (
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_get_time_bounds,
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_get_time_filter,
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map_query_object,
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validate_query_object,
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)
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from superset.semantic_layers.types import (
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AdhocExpression,
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@@ -53,6 +54,9 @@ from superset.semantic_layers.types import (
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)
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from superset.utils.core import FilterOperator
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# Alias for convenience
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Feature = SemanticViewFeature
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class MockSemanticViewImplementation:
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"""
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@@ -143,31 +147,29 @@ def mock_datasource() -> Mock:
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return datasource
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def test_convert_time_grain_time() -> None:
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@pytest.mark.parametrize(
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"input_grain, expected_grain",
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[
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("PT1S", TimeGrain.PT1S),
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("PT1M", TimeGrain.PT1M),
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("PT1H", TimeGrain.PT1H),
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("P1D", DateGrain.P1D),
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("P1W", DateGrain.P1W),
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("P1M", DateGrain.P1M),
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("P1Y", DateGrain.P1Y),
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("P3M", DateGrain.P3M),
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("INVALID", None),
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("", None),
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],
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)
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def test_convert_date_time_grain(
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input_grain: str,
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expected_grain: TimeGrain | DateGrain,
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) -> None:
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"""
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Test conversion of time grains (hour, minute, second).
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"""
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assert _convert_time_grain("PT1S") == TimeGrain.PT1S
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assert _convert_time_grain("PT1M") == TimeGrain.PT1M
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assert _convert_time_grain("PT1H") == TimeGrain.PT1H
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def test_convert_time_grain_date() -> None:
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"""
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Test conversion of date grains (day, week, month, year).
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"""
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assert _convert_time_grain("P1D") == DateGrain.P1D
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assert _convert_time_grain("P1W") == DateGrain.P1W
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assert _convert_time_grain("P1M") == DateGrain.P1M
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assert _convert_time_grain("P1Y") == DateGrain.P1Y
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def test_convert_time_grain_invalid() -> None:
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"""
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Test that invalid grain returns None.
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"""
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assert _convert_time_grain("INVALID") is None
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assert _convert_time_grain("") is None
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assert _convert_time_grain(input_grain) == expected_grain
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def test_get_filters_from_extras_empty() -> None:
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@@ -335,30 +337,6 @@ def test_convert_query_object_filter_temporal_range() -> None:
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assert result is None
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def test_convert_query_object_filter_equals(mock_datasource):
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"""
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Test conversion of EQUALS filter.
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"""
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all_dimensions = {
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dim.name: dim for dim in mock_datasource.implementation.dimensions
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}
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filter_ = {
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"op": FilterOperator.EQUALS.value,
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"col": "region",
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"val": "US",
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}
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result = _convert_query_object_filter(filter_, all_dimensions)
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assert result == Filter(
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type=PredicateType.WHERE,
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column=all_dimensions["region"],
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operator=Operator.EQUALS,
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value="US",
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)
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def test_convert_query_object_filter_in(mock_datasource):
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"""
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Test conversion of IN filter.
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@@ -1007,147 +985,184 @@ def test_map_query_object_with_time_offsets(mock_datasource):
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}
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def test_map_query_object_with_group_limit(mock_datasource):
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def test_convert_query_object_filter_unknown_operator(mock_datasource):
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"""
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Test mapping with group limit.
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Test filter with unknown operator returns None.
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"""
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query_object = QueryObject(
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datasource=mock_datasource,
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from_dttm=datetime(2025, 10, 15),
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to_dttm=datetime(2025, 10, 22),
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metrics=["total_sales"],
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columns=["category", "region"],
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series_columns=["category"],
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series_limit=10,
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series_limit_metric="total_sales",
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granularity="order_date",
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)
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result = map_query_object(query_object)
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assert len(result) == 1
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semantic_query = result[0]
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print(semantic_query.group_limit.dimensions)
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assert semantic_query.group_limit == GroupLimit(
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top=10,
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dimensions=[
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Dimension(
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id="products.category",
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name="category",
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type=STRING,
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definition="category",
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description="Product category",
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grain=None,
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)
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],
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metric=Metric(
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id="orders.total_sales",
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name="total_sales",
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type=NUMBER,
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definition="SUM(amount)",
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description="Total sales",
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),
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direction=OrderDirection.DESC,
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group_others=False,
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filters=None,
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)
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def test_map_query_object_with_order(mock_datasource):
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"""
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Test mapping with order by (testing _get_order directly).
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"""
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all_metrics = {
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metric.name: metric for metric in mock_datasource.implementation.metrics
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}
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all_dimensions = {
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dim.name: dim for dim in mock_datasource.implementation.dimensions
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}
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query_object = QueryObject(
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datasource=mock_datasource,
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from_dttm=datetime(2025, 10, 15),
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to_dttm=datetime(2025, 10, 22),
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metrics=["total_sales"],
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columns=["order_date", "category"],
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granularity="order_date",
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orderby=[("total_sales", False)],
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)
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# Test _get_order_from_query_object directly to avoid validation
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order = _get_order_from_query_object(query_object, all_metrics, all_dimensions)
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assert order == [(all_metrics["total_sales"], OrderDirection.DESC)]
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def test_map_query_object_filters_all_sources(mock_datasource):
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"""
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Test that filters from all sources are combined.
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"""
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mock_datasource.fetch_values_predicate = "tenant_id = 123"
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query_object = QueryObject(
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datasource=mock_datasource,
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from_dttm=datetime(2025, 10, 15),
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to_dttm=datetime(2025, 10, 22),
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metrics=["total_sales"],
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columns=["order_date", "category"],
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granularity="order_date",
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apply_fetch_values_predicate=True,
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extras={
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"where": "customer_id > 100",
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"having": "SUM(amount) > 1000",
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},
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filter=[
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{
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"op": FilterOperator.EQUALS.value,
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"col": "region",
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"val": "US",
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}
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],
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)
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result = map_query_object(query_object)
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assert len(result) == 1
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semantic_query = result[0]
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filters = semantic_query.filters
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# Should have at least:
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# - 2 time filters (>= and <)
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# - 1 fetch values filter
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# - 2 extras filters (where and having)
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# May also have region filter if it gets converted
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assert filters == {
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Filter(
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type=PredicateType.WHERE,
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column=Dimension(
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id="orders.order_date",
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name="order_date",
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type=STRING,
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definition="order_date",
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description="Order date",
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grain=None,
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),
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operator=Operator.LESS_THAN,
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value=datetime(2025, 10, 22, 0, 0),
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),
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AdhocFilter(type=PredicateType.WHERE, definition="tenant_id = 123"),
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Filter(
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type=PredicateType.WHERE,
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column=Dimension(
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id="orders.order_date",
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name="order_date",
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type=STRING,
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definition="order_date",
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description="Order date",
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grain=None,
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),
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operator=Operator.GREATER_THAN_OR_EQUAL,
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value=datetime(2025, 10, 15, 0, 0),
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),
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AdhocFilter(type=PredicateType.HAVING, definition="SUM(amount) > 1000"),
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AdhocFilter(type=PredicateType.WHERE, definition="customer_id > 100"),
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filter_ = {
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"op": "UNKNOWN_OPERATOR",
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"col": "category",
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"val": "Electronics",
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}
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result = _convert_query_object_filter(filter_, all_dimensions)
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assert result is None
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def test_validate_query_object_undefined_metric_error(mock_datasource):
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"""
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Test validation error for undefined metrics.
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"""
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query_object = QueryObject(
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datasource=mock_datasource,
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metrics=["undefined_metric"],
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columns=["order_date"],
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)
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with pytest.raises(ValueError, match="All metrics must be defined"):
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validate_query_object(query_object, mock_datasource.implementation)
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def test_validate_query_object_undefined_dimension_error(mock_datasource):
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"""
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Test validation error for undefined dimensions.
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"""
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query_object = QueryObject(
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datasource=mock_datasource,
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metrics=["total_sales"],
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columns=["undefined_dimension"],
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)
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with pytest.raises(ValueError, match="All dimensions must be defined"):
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validate_query_object(query_object, mock_datasource.implementation)
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def test_validate_query_object_time_grain_without_column_error(mock_datasource):
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"""
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Test validation error when time grain provided without time column.
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"""
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query_object = QueryObject(
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datasource=mock_datasource,
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metrics=["total_sales"],
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columns=["order_date", "category"],
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granularity=None, # No time column
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extras={"time_grain_sqla": "P1D"},
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)
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with pytest.raises(ValueError, match="time column must be specified"):
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validate_query_object(query_object, mock_datasource.implementation)
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def test_validate_query_object_unsupported_time_grain_error(mock_datasource):
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"""
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Test validation error for unsupported time grain.
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"""
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query_object = QueryObject(
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datasource=mock_datasource,
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metrics=["total_sales"],
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columns=["order_date", "category"],
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granularity="order_date",
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extras={"time_grain_sqla": "P1Y"}, # Year grain not supported
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)
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with pytest.raises(
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ValueError,
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match=(
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"The time grain is not supported for the time column in the Semantic View."
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),
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):
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validate_query_object(query_object, mock_datasource.implementation)
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def test_validate_query_object_group_limit_not_supported_error():
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"""
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Test validation error when group limit not supported.
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"""
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mock_datasource = Mock()
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time_dim = Dimension("order_date", "order_date", STRING, "order_date", "Date")
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category_dim = Dimension("category", "category", STRING, "category", "Category")
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sales_metric = Metric("total_sales", "total_sales", NUMBER, "SUM(amount)", "Sales")
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mock_datasource.implementation.dimensions = {time_dim, category_dim}
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mock_datasource.implementation.metrics = {sales_metric}
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mock_datasource.implementation.features = frozenset() # No GROUP_LIMIT feature
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query_object = QueryObject(
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datasource=mock_datasource,
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metrics=["total_sales"],
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columns=["order_date", "category"],
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series_columns=["category"],
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series_limit=10,
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)
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with pytest.raises(ValueError, match="Group limit is not supported"):
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validate_query_object(query_object, mock_datasource.implementation)
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def test_validate_query_object_undefined_series_column_error(mock_datasource):
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"""
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Test validation error for undefined series columns.
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"""
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query_object = QueryObject(
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datasource=mock_datasource,
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metrics=["total_sales"],
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columns=["order_date", "category"],
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series_columns=["undefined_column"],
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series_limit=10,
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)
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with pytest.raises(ValueError, match="All series columns must be defined"):
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validate_query_object(query_object, mock_datasource.implementation)
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@pytest.mark.parametrize(
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"filter_op, expected_operator",
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[
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("==", Operator.EQUALS),
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("!=", Operator.NOT_EQUALS),
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("<", Operator.LESS_THAN),
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(">", Operator.GREATER_THAN),
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("<=", Operator.LESS_THAN_OR_EQUAL),
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(">=", Operator.GREATER_THAN_OR_EQUAL),
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],
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)
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def test_convert_query_object_filter(
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filter_op: str,
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expected_operator: Operator,
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) -> None:
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"""
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Test filter with different operators.
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"""
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all_dimensions = {
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"category": Dimension("category", "category", STRING, "category", "Category")
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}
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filter_ = {
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"op": filter_op,
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"col": "category",
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"val": "Electronics",
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}
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result = _convert_query_object_filter(filter_, all_dimensions)
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assert result == Filter(
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type=PredicateType.WHERE,
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column=all_dimensions["category"],
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operator=expected_operator,
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value="Electronics",
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)
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def test_convert_query_object_filter_like():
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"""Test filter with LIKE operator."""
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all_dimensions = {"name": Dimension("name", "name", STRING, "name", "Name")}
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filter_ = {
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"op": "LIKE",
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"col": "name",
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"val": "%test%",
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}
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result = _convert_query_object_filter(filter_, all_dimensions)
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assert result == Filter(
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type=PredicateType.WHERE,
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column=all_dimensions["name"],
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operator=Operator.LIKE,
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value="%test%",
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)
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