QuantumBackedButler

class lsst.daf.butler.QuantumBackedButler(predicted_inputs: Iterable[UUID], predicted_outputs: Iterable[UUID], dimensions: DimensionUniverse, datastore: Datastore, storageClasses: StorageClassFactory, dataset_types: Mapping[str, DatasetType] | None = None)

Bases: LimitedButler

An implementation of LimitedButler intended to back execution of a single Quantum.

Parameters:

predicted_inputsIterable [DatasetId]

Dataset IDs for datasets that can can be read from this butler.

predicted_outputsIterable [DatasetId]

Dataset IDs for datasets that can be stored in this butler.

dimensionsDimensionUniverse

Object managing all dimension definitions.

datastoreDatastore

Datastore to use for all dataset I/O and existence checks.

storageClassesStorageClassFactory

Object managing all storage class definitions.

dataset_typesMapping [str, DatasetType]

The registry dataset type definitions, indexed by name.

Notes

Most callers should use the initialize classmethod to construct new instances instead of calling the constructor directly.

QuantumBackedButler uses a SQLite database internally, in order to reuse existing DatastoreRegistryBridge and OpaqueTableStorage implementations that rely SQLAlchemy. If implementations are added in the future that don’t rely on SQLAlchemy, it should be possible to swap them in by overriding the type arguments to initialize (though at present, QuantumBackedButler would still create at least an in-memory SQLite database that would then go unused).`

We imagine QuantumBackedButler being used during (at least) batch execution to capture Datastore records and save them to per-quantum files, which are also a convenient place to store provenance for eventual upload to a SQL-backed Registry (once Registry has tables to store provenance, that is). These per-quantum files can be written in two ways:

• The SQLite file used internally by QuantumBackedButler can be used directly but customizing the filename argument to initialize, and then transferring that file to the object store after execution completes (or fails; a try/finally pattern probably makes sense here).

• A JSON or YAML file can be written by calling extract_provenance_data, and using pydantic methods to write the returned QuantumProvenanceData to a file.

Note that at present, the SQLite file only contains datastore records, not provenance, but that should be easy to address (if desired) after we actually design a Registry schema for provenance. I also suspect that we’ll want to explicitly close the SQLite file somehow before trying to transfer it. But I’m guessing we’d prefer to write the per-quantum files as JSON anyway.

Attributes Summary

dimensions

Structure managing all dimensions recognized by this data repository (DimensionUniverse).

Methods Summary

extract_provenance_data()	Extract provenance information and datastore records from this butler.
from_predicted(config, predicted_inputs, ...)	Construct a new QuantumBackedButler from sets of input and output dataset IDs.
get(ref, /, *[, parameters, storageClass])	Retrieve a stored dataset.
getDeferred(ref, /, *[, parameters, ...])	Create a DeferredDatasetHandle which can later retrieve a dataset, after an immediate registry lookup.
initialize(config, quantum, dimensions[, ...])	Construct a new QuantumBackedButler from repository configuration and helper types.
isWriteable()	Return True if this Butler supports write operations.
markInputUnused(ref)	Indicate that a predicted input was not actually used when processing a Quantum.
pruneDatasets(refs, *[, disassociate, ...])	Remove one or more datasets from a collection and/or storage.
put(obj, ref, /)	Store a dataset that already has a UUID and RUN collection.
stored(ref)	Indicate whether the dataset's artifacts are present in the Datastore.
stored_many(refs)	Check the datastore for artifact existence of multiple datasets at once.

Attributes Documentation

dimensions

Methods Documentation

extract_provenance_data() → QuantumProvenanceData

Extract provenance information and datastore records from this butler.

Returns:

provenanceQuantumProvenanceData

A serializable struct containing input/output dataset IDs and datastore records. This assumes all dataset IDs are UUIDs (just to make it easier for pydantic to reason about the struct’s types); the rest of this class makes no such assumption, but the approach to processing in which it’s useful effectively requires UUIDs anyway.

Notes

QuantumBackedButler records this provenance information when its methods are used, which mostly saves PipelineTask authors from having to worry about while still recording very detailed information. But it has two small weaknesses:

• Calling getDeferred or get is enough to mark a dataset as an “actual input”, which may mark some datasets that aren’t actually used. We rely on task authors to use markInputUnused to address this.

• We assume that the execution system will call stored on all predicted inputs prior to execution, in order to populate the “available inputs” set. This is what I envision ‘SingleQuantumExecutor doing after we update it to use this class, but it feels fragile for this class to make such a strong assumption about how it will be used, even if I can’t think of any other executor behavior that would make sense.

classmethod from_predicted(config: ~lsst.daf.butler._config.Config | str | ~urllib.parse.ParseResult | ~lsst.resources._resourcePath.ResourcePath | ~pathlib.Path, predicted_inputs: ~collections.abc.Iterable[~uuid.UUID], predicted_outputs: ~collections.abc.Iterable[~uuid.UUID], dimensions: ~lsst.daf.butler.dimensions._universe.DimensionUniverse, datastore_records: ~collections.abc.Mapping[str, ~lsst.daf.butler.datastore.record_data.DatastoreRecordData], filename: str = ':memory:', OpaqueManagerClass: type[lsst.daf.butler.registry.interfaces._opaque.OpaqueTableStorageManager] = <class 'lsst.daf.butler.registry.opaque.ByNameOpaqueTableStorageManager'>, BridgeManagerClass: type[lsst.daf.butler.registry.interfaces._bridge.DatastoreRegistryBridgeManager] = <class 'lsst.daf.butler.registry.bridge.monolithic.MonolithicDatastoreRegistryBridgeManager'>, search_paths: list[str] | None = None, dataset_types: ~collections.abc.Mapping[str, ~lsst.daf.butler._dataset_type.DatasetType] | None = None) → QuantumBackedButler

Construct a new QuantumBackedButler from sets of input and output dataset IDs.

Parameters:

configConfig or ResourcePathExpression

A butler repository root, configuration filename, or configuration instance.

predicted_inputsIterable [DatasetId]

Dataset IDs for datasets that can can be read from this butler.

predicted_outputsIterable [DatasetId]

Dataset IDs for datasets that can be stored in this butler, must be fully resolved.

dimensionsDimensionUniverse

Object managing all dimension definitions.

datastore_recordsdict [str, DatastoreRecordData] or None

Datastore records to import into a datastore.

filenamestr, optional

Name for the SQLite database that will back this butler; defaults to an in-memory database.

OpaqueManagerClasstype, optional

A subclass of OpaqueTableStorageManager to use for datastore opaque records. Default is a SQL-backed implementation.

BridgeManagerClasstype, optional

A subclass of DatastoreRegistryBridgeManager to use for datastore location records. Default is a SQL-backed implementation.

search_pathslist of str, optional

Additional search paths for butler configuration.

dataset_typesMapping [str, DatasetType], optional

Mapping of the dataset type name to its registry definition.

get(ref: DatasetRef, /, *, parameters: dict[str, Any] | None = None, storageClass: StorageClass | str | None = None) → Any

Retrieve a stored dataset.

Parameters:

refDatasetRef

A resolved DatasetRef directly associated with a dataset.

parametersdict

Additional StorageClass-defined options to control reading, typically used to efficiently read only a subset of the dataset.

storageClassStorageClass or str, optional

The storage class to be used to override the Python type returned by this method. By default the returned type matches the dataset type definition for this dataset. Specifying a read StorageClass can force a different type to be returned. This type must be compatible with the original type.

Returns:

objobject

The dataset.

Raises:

AmbiguousDatasetError

Raised if the supplied DatasetRef is unresolved.

Notes

In a LimitedButler the only allowable way to specify a dataset is to use a resolved DatasetRef. Subclasses can support more options.

getDeferred(ref: DatasetRef, /, *, parameters: dict[str, Any] | None = None, storageClass: str | StorageClass | None = None) → DeferredDatasetHandle

Create a DeferredDatasetHandle which can later retrieve a dataset, after an immediate registry lookup.

Parameters:

refDatasetRef

For the default implementation of a LimitedButler, the only acceptable parameter is a resolved DatasetRef.

parametersdict

Additional StorageClass-defined options to control reading, typically used to efficiently read only a subset of the dataset.

storageClassStorageClass or str, optional

The storage class to be used to override the Python type returned by this method. By default the returned type matches the dataset type definition for this dataset. Specifying a read StorageClass can force a different type to be returned. This type must be compatible with the original type.

Returns:

objDeferredDatasetHandle

A handle which can be used to retrieve a dataset at a later time.

Notes

In a LimitedButler the only allowable way to specify a dataset is to use a resolved DatasetRef. Subclasses can support more options.

classmethod initialize(config: ~lsst.daf.butler._config.Config | str | ~urllib.parse.ParseResult | ~lsst.resources._resourcePath.ResourcePath | ~pathlib.Path, quantum: ~lsst.daf.butler._quantum.Quantum, dimensions: ~lsst.daf.butler.dimensions._universe.DimensionUniverse, filename: str = ':memory:', OpaqueManagerClass: type[lsst.daf.butler.registry.interfaces._opaque.OpaqueTableStorageManager] = <class 'lsst.daf.butler.registry.opaque.ByNameOpaqueTableStorageManager'>, BridgeManagerClass: type[lsst.daf.butler.registry.interfaces._bridge.DatastoreRegistryBridgeManager] = <class 'lsst.daf.butler.registry.bridge.monolithic.MonolithicDatastoreRegistryBridgeManager'>, search_paths: list[str] | None = None, dataset_types: ~collections.abc.Mapping[str, ~lsst.daf.butler._dataset_type.DatasetType] | None = None) → QuantumBackedButler

Construct a new QuantumBackedButler from repository configuration and helper types.

Parameters:

configConfig or ResourcePathExpression

A butler repository root, configuration filename, or configuration instance.

quantumQuantum

Object describing the predicted input and output dataset relevant to this butler. This must have resolved DatasetRef instances for all inputs and outputs.

dimensionsDimensionUniverse

Object managing all dimension definitions.

filenamestr, optional

Name for the SQLite database that will back this butler; defaults to an in-memory database.

OpaqueManagerClasstype, optional

A subclass of OpaqueTableStorageManager to use for datastore opaque records. Default is a SQL-backed implementation.

BridgeManagerClasstype, optional

A subclass of DatastoreRegistryBridgeManager to use for datastore location records. Default is a SQL-backed implementation.

search_pathslist of str, optional

Additional search paths for butler configuration.

dataset_typesMapping [str, DatasetType], optional

Mapping of the dataset type name to its registry definition.

isWriteable() → bool

Return True if this Butler supports write operations.

markInputUnused(ref: DatasetRef) → None

Indicate that a predicted input was not actually used when processing a Quantum.

Parameters:

refDatasetRef

Reference to the unused dataset.

Notes

By default, a dataset is considered “actually used” if it is accessed via get or a handle to it is obtained via getDeferred (even if the handle is not used). This method must be called after one of those in order to remove the dataset from the actual input list.

This method does nothing for butlers that do not store provenance information (which is the default implementation provided by the base class).

pruneDatasets(refs: Iterable[DatasetRef], *, disassociate: bool = True, unstore: bool = False, tags: Iterable[str] = (), purge: bool = False) → None

Remove one or more datasets from a collection and/or storage.

Parameters:

refsIterable of DatasetRef

Datasets to prune. These must be “resolved” references (not just a DatasetType and data ID).

disassociatebool, optional

Disassociate pruned datasets from tags, or from all collections if purge=True.

unstorebool, optional

If True (False is default) remove these datasets from all datastores known to this butler. Note that this will make it impossible to retrieve these datasets even via other collections. Datasets that are already not stored are ignored by this option.

tagsIterable [ str ], optional

TAGGED collections to disassociate the datasets from. Ignored if disassociate is False or purge is True.

purgebool, optional

If True (False is default), completely remove the dataset from the Registry. To prevent accidental deletions, purge may only be True if all of the following conditions are met:

• disassociate is True;

• unstore is True.

This mode may remove provenance information from datasets other than those provided, and should be used with extreme care.

Raises:

TypeError

Raised if the butler is read-only, if no collection was provided, or the conditions for purge=True were not met.

put(obj: Any, ref: DatasetRef, /) → DatasetRef

Store a dataset that already has a UUID and RUN collection.

Parameters:

objobject

The dataset.

refDatasetRef

Resolved reference for a not-yet-stored dataset.

Returns:

refDatasetRef

The same as the given, for convenience and symmetry with Butler.put.

Raises:

TypeError

Raised if the butler is read-only.

Notes

Whether this method inserts the given dataset into a Registry is implementation defined (some LimitedButler subclasses do not have a Registry), but it always adds the dataset to a Datastore, and the given ref.id and ref.run are always preserved.

stored(ref: DatasetRef) → bool

Indicate whether the dataset’s artifacts are present in the Datastore.

Parameters:

refDatasetRef

Resolved reference to a dataset.

Returns:

storedbool

Whether the dataset artifact exists in the datastore and can be retrieved.

stored_many(refs: Iterable[DatasetRef]) → dict[lsst.daf.butler._dataset_ref.DatasetRef, bool]

Check the datastore for artifact existence of multiple datasets at once.

Parameters:

refsiterable of DatasetRef

The datasets to be checked.

Returns:

existencedict of [DatasetRef, bool]

Mapping from given dataset refs to boolean indicating artifact existence.