Cache additional methods in MetricLookup / SemanticModelLookup (#…

…1438)

Similar to the previous PRs, this PR caches a few additional methods. I
did a little bit of memory profiling using a large manifest, and total
memory allocation did not change significantly. I believe this is
because these additional caches reference objects that are already
stored in `ValidLinkableSpecResolver` or the cached objects have low
overhead.

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

@@ -54,6 +54,14 @@ def __init__(
             max_entity_links=MAX_JOIN_HOPS,
         )
 
+        # Cache for `get_min_queryable_time_granularity()`
+        self._metric_reference_to_min_metric_time_grain: Dict[MetricReference, TimeGranularity] = {}
+
+        # Cache for `get_valid_agg_time_dimensions_for_metric()`.
+        self._metric_reference_to_valid_agg_time_dimension_specs: Dict[
+            MetricReference, Sequence[TimeDimensionSpec]
+        ] = {}
+
     @functools.lru_cache
     def linkable_elements_for_measure(
         self,
@@ -183,6 +191,18 @@ def get_valid_agg_time_dimensions_for_metric(
         self, metric_reference: MetricReference
     ) -> Sequence[TimeDimensionSpec]:
         """Get the agg time dimension specs that can be used in place of metric time for this metric, if applicable."""
+        result = self._metric_reference_to_valid_agg_time_dimension_specs.get(metric_reference)
+        if result is not None:
+            return result
+
+        result = self._get_valid_agg_time_dimensions_for_metric(metric_reference)
+        self._metric_reference_to_valid_agg_time_dimension_specs[metric_reference] = result
+
+        return result
+
+    def _get_valid_agg_time_dimensions_for_metric(
+        self, metric_reference: MetricReference
+    ) -> Sequence[TimeDimensionSpec]:
         agg_time_dimension_specs = self._get_agg_time_dimension_specs_for_metric(metric_reference)
         distinct_agg_time_dimension_identifiers = set(
             [(spec.reference, spec.entity_links) for spec in agg_time_dimension_specs]
@@ -204,6 +224,15 @@ def get_min_queryable_time_granularity(self, metric_reference: MetricReference)
 
         Maps to the largest granularity defined for any of the metric's agg_time_dimensions.
         """
+        result = self._metric_reference_to_min_metric_time_grain.get(metric_reference)
+        if result is not None:
+            return result
+
+        result = self._get_min_queryable_time_granularity(metric_reference)
+        self._metric_reference_to_min_metric_time_grain[metric_reference] = result
+        return result
+
+    def _get_min_queryable_time_granularity(self, metric_reference: MetricReference) -> TimeGranularity:
         agg_time_dimension_specs = self._get_agg_time_dimension_specs_for_metric(metric_reference)
         assert (
             agg_time_dimension_specs

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

@@ -67,6 +67,9 @@ def __init__(self, model: SemanticManifest, custom_granularities: Dict[str, Expa
         # Cache for defined time granularity.
         self._time_dimension_to_defined_time_granularity: Dict[TimeDimensionReference, TimeGranularity] = {}
 
+        # Cache for agg. time dimension for measure.
+        self._measure_reference_to_agg_time_dimension_specs: Dict[MeasureReference, Sequence[TimeDimensionSpec]] = {}
+
     def get_dimension_references(self) -> Sequence[DimensionReference]:
         """Retrieve all dimension references from the collection of semantic models."""
         return tuple(self._dimension_index.keys())
@@ -364,6 +367,17 @@ def get_agg_time_dimension_specs_for_measure(
         self, measure_reference: MeasureReference
     ) -> Sequence[TimeDimensionSpec]:
         """Get the agg time dimension specs that can be used in place of metric time for this measure."""
+        result = self._measure_reference_to_agg_time_dimension_specs.get(measure_reference)
+        if result is not None:
+            return result
+
+        result = self._get_agg_time_dimension_specs_for_measure(measure_reference)
+        self._measure_reference_to_agg_time_dimension_specs[measure_reference] = result
+        return result
+
+    def _get_agg_time_dimension_specs_for_measure(
+        self, measure_reference: MeasureReference
+    ) -> Sequence[TimeDimensionSpec]:
         agg_time_dimension = self.get_agg_time_dimension_for_measure(measure_reference)
         # A measure's agg_time_dimension is required to be in the same semantic model as the measure,
         # so we can assume the same semantic model for both measure and dimension.