Include Time Spine Nodes in Dataflow Plan #1548
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Dec 11, 2024
metricflow/dataset/sql_dataset.py
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| @@ -144,7 +145,7 @@ def instances_for_time_dimensions( | |||
| return instances_to_return | |||
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| def instance_for_time_dimension(self, time_dimension_spec: TimeDimensionSpec) -> TimeDimensionInstance: | |||
| """Given the name of the time dimension, return the instance associated with it in the data set.""" | |||
| """Given ta time dimension spec, return the instance associated with it in the data set.""" | |||
metricflow/dataset/sql_dataset.py
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| for instance in instances: | ||
| if instance.spec == spec: | ||
| return instance | ||
| raise RuntimeError(f"Did not find instance matching spec in dataset. Spec: {spec}\nInstances: {instances}") |
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For better formatting, you can do:
# Easier autocomplete.
raise RuntimeError(str(LazyFormat("Did not find instance matching spec in dataset.", spec=spec, instances=instances))
# Or call formatting function.
raise RuntimeError(mf_pformat_dict("Did not find instance matching spec in dataset.", {"spec": spec, "instances": instances))
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Ah good call! I need to change this habit.
| class AliasSpecsNode(DataflowPlanNode, ABC): | ||
| """Change the columns matching the key specs to match the value specs.""" | ||
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| change_specs: Sequence[Tuple[InstanceSpec, InstanceSpec]] |
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Since this class is frozen, it's preferable to keep the field types immutable as well. i.e.
change_specs: Tuple[...]
Also helpful to have a dataclass for Tuple[InstanceSpec, InstanceSpec] so that you don't have to remember what index is what.
… for time spines The metric time nodes are used for resolving metric_time without metrics. The read SQL nodes will be used for time spine joins.
This will allow us to stop building time spine nodes on the fly in the dataflow to SQL logic.
Also fixes a small issue where we were prematurely filtering out columns in that node. This is what you see in the snapshot changes. It should not impact optimized queries.
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Previously, time spine nodes were built on the fly in the dataflow to SQL logic. This came with several limitations, including:
This PR adds logic to build time spine nodes in the dataflow plan, instead.