Add test time spines for sub-daily granularity (#1358)

Add test time spines for sub-daily granularity
I added as much data as seemed reasonable to include manually, and
figure we can focus tests to those time ranges. And we always have the
flexibility to add more data later.
I included one time spine per sub-daily grain so that we could be sure
to test the syntax for each grain against each engine that supports it.
Snapshot changes are separated into isolated commits for easier review.
They only include the following changes:
- Node ID changes due to new time spine source nodes
- Time spine table alias changes due to new time spine source nodes

.changes/unreleased/Features-20240727-081106.yaml

@@ -0,0 +1,6 @@
+kind: Features
+body: Enable sub-daily queries without metrics.
+time: 2024-07-27T08:11:06.357653-07:00
+custom:
+  Author: courtneyholcomb
+  Issue: "1359"

metricflow-semantics/metricflow_semantics/model/semantics/linkable_spec_resolver.py

@@ -40,6 +40,7 @@
 from metricflow_semantics.model.semantics.linkable_element_set import LinkableElementSet
 from metricflow_semantics.model.semantics.semantic_model_join_evaluator import SemanticModelJoinEvaluator
 from metricflow_semantics.specs.time_dimension_spec import DEFAULT_TIME_GRANULARITY
+from metricflow_semantics.time.time_spine_source import TimeSpineSource
 
 if TYPE_CHECKING:
     from metricflow_semantics.model.semantics.semantic_model_lookup import SemanticModelLookup
@@ -124,6 +125,7 @@ def __init__(
         # Sort semantic models by name for consistency in building derived objects.
         self._semantic_models = sorted(self._semantic_manifest.semantic_models, key=lambda x: x.name)
         self._join_evaluator = SemanticModelJoinEvaluator(semantic_model_lookup)
+        self._time_spine_sources = TimeSpineSource.create_from_manifest(self._semantic_manifest)
 
         assert max_entity_links >= 0
         self._max_entity_links = max_entity_links
@@ -454,6 +456,7 @@ def _get_metric_time_elements(self, measure_reference: Optional[MeasureReference
         on what aggregation time dimension was used to define the measure.
         """
         measure_semantic_model: Optional[SemanticModel] = None
+        defined_granularity: Optional[TimeGranularity] = None
         if measure_reference:
             measure_semantic_model = self._get_semantic_model_for_measure(measure_reference)
             measure_agg_time_dimension_reference = measure_semantic_model.checked_agg_time_dimension_for_measure(
@@ -463,15 +466,20 @@ def _get_metric_time_elements(self, measure_reference: Optional[MeasureReference
                 semantic_model=measure_semantic_model,
                 time_dimension_reference=measure_agg_time_dimension_reference,
             )
+            possible_metric_time_granularities = tuple(
+                time_granularity
+                for time_granularity in TimeGranularity
+                if defined_granularity.is_smaller_than_or_equal(time_granularity)
+            )
         else:
-            defined_granularity = DEFAULT_TIME_GRANULARITY
-
-        # It's possible to aggregate measures to coarser time granularities (except with cumulative metrics).
-        possible_metric_time_granularities = tuple(
-            time_granularity
-            for time_granularity in TimeGranularity
-            if defined_granularity.is_smaller_than_or_equal(time_granularity)
-        )
+            # If querying metric_time without metrics, will query from time spines.
+            # Defaults to DAY granularity if available in time spines, else smallest available granularity.
+            min_time_spine_granularity = min(self._time_spine_sources.keys())
+            possible_metric_time_granularities = tuple(
+                time_granularity
+                for time_granularity in TimeGranularity
+                if min_time_spine_granularity.is_smaller_than_or_equal(time_granularity)
+            )
 
         # For each of the possible time granularities, create a LinkableDimension.
         path_key_to_linkable_dimensions: Dict[ElementPathKey, List[LinkableDimension]] = defaultdict(list)

metricflow-semantics/metricflow_semantics/specs/patterns/metric_time_default_granularity.py

@@ -10,6 +10,7 @@
 from metricflow_semantics.specs.patterns.spec_pattern import SpecPattern
 from metricflow_semantics.specs.spec_set import group_specs_by_type
 from metricflow_semantics.specs.time_dimension_spec import (
+    DEFAULT_TIME_GRANULARITY,
     TimeDimensionSpec,
     TimeDimensionSpecComparisonKey,
     TimeDimensionSpecField,
@@ -47,10 +48,13 @@ def __init__(self, max_metric_default_time_granularity: Optional[TimeGranularity
     def match(self, candidate_specs: Sequence[InstanceSpec]) -> Sequence[InstanceSpec]:
         spec_set = group_specs_by_type(candidate_specs)
 
-        # If there are no metrics or metric_time specs in the query, skip this filter.
-        if not (self._max_metric_default_time_granularity and spec_set.metric_time_specs):
+        # If there are no metric_time specs in the query, skip this filter.
+        if not spec_set.metric_time_specs:
             return candidate_specs
 
+        # If there are metrics in the query, use max metric default. For no-metric queries, use standard default.
+        default_granularity = self._max_metric_default_time_granularity or DEFAULT_TIME_GRANULARITY
+
         spec_key_to_grains: Dict[TimeDimensionSpecComparisonKey, Set[TimeGranularity]] = defaultdict(set)
         spec_key_to_specs: Dict[TimeDimensionSpecComparisonKey, Tuple[TimeDimensionSpec, ...]] = defaultdict(tuple)
         for metric_time_spec in spec_set.metric_time_specs:
@@ -60,10 +64,8 @@ def match(self, candidate_specs: Sequence[InstanceSpec]) -> Sequence[InstanceSpe
 
         matched_metric_time_specs: Tuple[TimeDimensionSpec, ...] = ()
         for spec_key, time_grains in spec_key_to_grains.items():
-            if self._max_metric_default_time_granularity in time_grains:
-                matched_metric_time_specs += (
-                    spec_key_to_specs[spec_key][0].with_grain(self._max_metric_default_time_granularity),
-                )
+            if default_granularity in time_grains:
+                matched_metric_time_specs += (spec_key_to_specs[spec_key][0].with_grain(default_granularity),)
             else:
                 # If default_granularity is not in the available options, then time granularity was specified in the request
                 # and a default is not needed here. Pass all options through for this spec key.

metricflow-semantics/metricflow_semantics/specs/time_dimension_spec.py

@@ -101,7 +101,12 @@ def without_first_entity_link(self) -> TimeDimensionSpec:  # noqa: D102
 
     @property
     def without_entity_links(self) -> TimeDimensionSpec:  # noqa: D102
-        return TimeDimensionSpec.from_name(self.element_name)
+        return TimeDimensionSpec(
+            element_name=self.element_name,
+            time_granularity=self.time_granularity,
+            date_part=self.date_part,
+            entity_links=(),
+        )
 
     @staticmethod
     def from_name(name: str) -> TimeDimensionSpec:  # noqa: D102

metricflow-semantics/metricflow_semantics/test_helpers/semantic_manifest_yamls/shared/project_configuration.yaml

@@ -5,3 +5,40 @@ project_configuration:
     - location: $source_schema.mf_time_spine
       column_name: ds
       grain: day
+  time_spines:
+    - node_relation:
+        alias: mf_time_spine_nanosecond
+        schema_name: $source_schema
+      primary_column:
+        name: ts
+        time_granularity: nanosecond
+    - node_relation:
+        alias: mf_time_spine_microsecond
+        schema_name: $source_schema
+      primary_column:
+        name: ts
+        time_granularity: microsecond
+    - node_relation:
+        alias: mf_time_spine_millisecond
+        schema_name: $source_schema
+      primary_column:
+        name: ts
+        time_granularity: millisecond
+    - node_relation:
+        alias: mf_time_spine_second
+        schema_name: $source_schema
+      primary_column:
+        name: ts
+        time_granularity: second
+    - node_relation:
+        alias: mf_time_spine_minute
+        schema_name: $source_schema
+      primary_column:
+        name: ts
+        time_granularity: minute
+    - node_relation:
+        alias: mf_time_spine_hour
+        schema_name: $source_schema
+      primary_column:
+        name: ts
+        time_granularity: hour

metricflow-semantics/metricflow_semantics/test_helpers/semantic_manifest_yamls/simple_manifest/metrics.yaml

@@ -783,22 +783,3 @@ metric:
       name: listings
   filter: "{{ Metric('views', ['listing']) }} > 10"
   time_granularity: week
----
-metric:
-  name: bookings_before_dec_20_2019
-  description: Bookings up to the end of 2022
-  type: simple
-  type_params:
-    measure: bookings
-  filter: "{{ TimeDimension('metric_time') }} < '2012-12-20'"
----
-metric:
-  name: bookings_between_dec_18_2019_and_dec_20_2019
-  description: Bookings starting in 2020. Used to test a metric with different types of ambiguous filters in on its input metric.
-  type: derived
-  type_params:
-    expr: bookings_before_dec_20_2019
-    metrics:
-      - name: bookings_before_dec_20_2019
-        filter: "{{ TimeDimension('metric_time') }} > '2019-12-18'"
-  time_granularity: week

metricflow-semantics/metricflow_semantics/test_helpers/semantic_manifest_yamls/simple_manifest/semantic_models/user_sm_source.yaml

@@ -23,6 +23,23 @@ semantic_model:
         time_granularity: day
     - name: home_state
       type: categorical
+    - name: last_profile_edit_ts
+      type: time
+      type_params:
+        time_granularity: millisecond
+    - name: bio_added_ts
+      type: time
+      type_params:
+        time_granularity: second
+    - name: last_login_ts
+      type: time
+      type_params:
+        time_granularity: minute
+    - name: archived_at
+      type: time
+      type_params:
+        time_granularity: hour
+
 
   entities:
     - name: user

metricflow-semantics/tests_metricflow_semantics/snapshots/test_available_group_by_items.py/LinkableSpecSet/test_available_group_by_items__no_metrics__set0.txt

@@ -6,13 +6,55 @@
   "TimeDimension('metric_time', 'day', date_part_name='month')",
   "TimeDimension('metric_time', 'day', date_part_name='quarter')",
   "TimeDimension('metric_time', 'day', date_part_name='year')",
+  "TimeDimension('metric_time', 'hour')",
+  "TimeDimension('metric_time', 'hour', date_part_name='day')",
+  "TimeDimension('metric_time', 'hour', date_part_name='dow')",
+  "TimeDimension('metric_time', 'hour', date_part_name='doy')",
+  "TimeDimension('metric_time', 'hour', date_part_name='month')",
+  "TimeDimension('metric_time', 'hour', date_part_name='quarter')",
+  "TimeDimension('metric_time', 'hour', date_part_name='year')",
+  "TimeDimension('metric_time', 'microsecond')",
+  "TimeDimension('metric_time', 'microsecond', date_part_name='day')",
+  "TimeDimension('metric_time', 'microsecond', date_part_name='dow')",
+  "TimeDimension('metric_time', 'microsecond', date_part_name='doy')",
+  "TimeDimension('metric_time', 'microsecond', date_part_name='month')",
+  "TimeDimension('metric_time', 'microsecond', date_part_name='quarter')",
+  "TimeDimension('metric_time', 'microsecond', date_part_name='year')",
+  "TimeDimension('metric_time', 'millisecond')",
+  "TimeDimension('metric_time', 'millisecond', date_part_name='day')",
+  "TimeDimension('metric_time', 'millisecond', date_part_name='dow')",
+  "TimeDimension('metric_time', 'millisecond', date_part_name='doy')",
+  "TimeDimension('metric_time', 'millisecond', date_part_name='month')",
+  "TimeDimension('metric_time', 'millisecond', date_part_name='quarter')",
+  "TimeDimension('metric_time', 'millisecond', date_part_name='year')",
+  "TimeDimension('metric_time', 'minute')",
+  "TimeDimension('metric_time', 'minute', date_part_name='day')",
+  "TimeDimension('metric_time', 'minute', date_part_name='dow')",
+  "TimeDimension('metric_time', 'minute', date_part_name='doy')",
+  "TimeDimension('metric_time', 'minute', date_part_name='month')",
+  "TimeDimension('metric_time', 'minute', date_part_name='quarter')",
+  "TimeDimension('metric_time', 'minute', date_part_name='year')",
   "TimeDimension('metric_time', 'month')",
   "TimeDimension('metric_time', 'month', date_part_name='month')",
   "TimeDimension('metric_time', 'month', date_part_name='quarter')",
   "TimeDimension('metric_time', 'month', date_part_name='year')",
+  "TimeDimension('metric_time', 'nanosecond')",
+  "TimeDimension('metric_time', 'nanosecond', date_part_name='day')",
+  "TimeDimension('metric_time', 'nanosecond', date_part_name='dow')",
+  "TimeDimension('metric_time', 'nanosecond', date_part_name='doy')",
+  "TimeDimension('metric_time', 'nanosecond', date_part_name='month')",
+  "TimeDimension('metric_time', 'nanosecond', date_part_name='quarter')",
+  "TimeDimension('metric_time', 'nanosecond', date_part_name='year')",
   "TimeDimension('metric_time', 'quarter')",
   "TimeDimension('metric_time', 'quarter', date_part_name='quarter')",
   "TimeDimension('metric_time', 'quarter', date_part_name='year')",
+  "TimeDimension('metric_time', 'second')",
+  "TimeDimension('metric_time', 'second', date_part_name='day')",
+  "TimeDimension('metric_time', 'second', date_part_name='dow')",
+  "TimeDimension('metric_time', 'second', date_part_name='doy')",
+  "TimeDimension('metric_time', 'second', date_part_name='month')",
+  "TimeDimension('metric_time', 'second', date_part_name='quarter')",
+  "TimeDimension('metric_time', 'second', date_part_name='year')",
   "TimeDimension('metric_time', 'week')",
   "TimeDimension('metric_time', 'week', date_part_name='month')",
   "TimeDimension('metric_time', 'week', date_part_name='quarter')",