AllDimensionsQuantumGraphBuilder

final class lsst.pipe.base.all_dimensions_quantum_graph_builder.AllDimensionsQuantumGraphBuilder(pipeline_graph: PipelineGraph, butler: Butler, *, where: str, dataset_query_constraint: DatasetQueryConstraintVariant = <class 'lsst.pipe.base._datasetQueryConstraints._ALL'>, bind: Mapping[str, Any] | None = None, **kwargs: Any)

Bases: QuantumGraphBuilder

An implementation of QuantumGraphBuilder that uses a single large query for data IDs covering all dimensions in the pipeline.

Parameters:

pipeline_graphpipeline_graph.PipelineGraph

Pipeline to build a QuantumGraph from, as a graph. Will be resolved in-place with the given butler (any existing resolution is ignored).

butlerlsst.daf.butler.Butler

Client for the data repository. Should be read-only.

wherestr

Butler expression language constraint to apply to all data IDs.

dataset_query_constraintDatasetQueryConstraintVariant, optional

Specification of which overall-input datasets should be used to constrain the initial data ID queries. Not including an important constraint can result in catastrophically large query results that take too long to process, while including too many makes the query much more complex, increasing the chances that the database will choose a bad (sometimes catastrophically bad) query plan.

bindMapping, optional

Variable substitutions for the where expression.

**kwargs

Additional keyword arguments forwarded to QuantumGraphBuilder.

Notes

This is a general-purpose algorithm that delegates the problem of determining which “end” of the pipeline is more constrained (beginning by input collection contents vs. end by the where string) to the database query planner, which usually does a good job.

This algorithm suffers from a serious limitation, which we refer to as the “tract slicing” problem from its most common variant: the where string and general data ID intersection rules apply to all data IDs in the graph. For example, if a tract constraint is present in the where string or an overall-input dataset, then it is impossible for any data ID that does not overlap that tract to be present anywhere in the pipeline, such as a {visit, detector} combination where the visit overlaps the tract even if the detector does not.

Attributes Summary

universe

Definitions of all data dimensions.

Methods Summary

build([metadata])	Build the quantum graph.
process_subgraph(subgraph)	Build the rough structure for an independent subset of the QuantumGraph and query for relevant existing datasets.

Attributes Documentation

universe

Definitions of all data dimensions.

Methods Documentation

build(metadata: Mapping[str, Any] | None = None) → QuantumGraph

Build the quantum graph.

Parameters:

metadataMapping, optional

Flexible metadata to add to the quantum graph.

Returns:

quantum_graphQuantumGraph

DAG describing processing to be performed.

Notes

External code is expected to construct a QuantumGraphBuilder and then call this method exactly once. See class documentation for details on what it does.

process_subgraph(subgraph: PipelineGraph) → QuantumGraphSkeleton

Build the rough structure for an independent subset of the QuantumGraph and query for relevant existing datasets.

Parameters:

subgraphpipeline_graph.PipelineGraph

Subset of the pipeline graph that should be processed by this call. This is always resolved and topologically sorted. It should not be modified.

Returns:

skeletonquantum_graph_skeleton.QuantumGraphSkeleton

Class representing an initial quantum graph. See quantum_graph_skeleton.QuantumGraphSkeleton docs for details. After this is returned, the object may be modified in-place in unspecified ways.

Notes

In addition to returning a quantum_graph_skeleton.QuantumGraphSkeleton, this method should populate the existing_datasets structure by querying for all relevant datasets with non-empty data IDs (those with empty data IDs will already be present). In particular:

• inputs must always be populated with all overall-input datasets (but not prerequisites), by querying input_collections;

• outputs_for_skip must be populated with any intermediate our output datasets present in skip_existing_in (it can be ignored if skip_existing_in is empty);

• outputs_in_the_way must be populated with any intermediate or output datasets present in output_run, if output_run_exists (it can be ignored if output_run_exists is False). Note that the presence of such datasets is not automatically an error, even if clobber is `False, as these may be quanta that will be skipped.

• inputs must be populated with all prerequisite-input datasets that were included in the skeleton, by querying input_collections (not all prerequisite inputs need to be included in the skeleton, but the base class can only use per-quantum queries to find them, and that can be slow when there are many quanta).

Dataset types should never be components and should always use the “common” storage class definition in pipeline_graph.DatasetTypeNode (which is the data repository definition when the dataset type is registered).