Registry

class lsst.daf.butler.Registry(database: Database, dimensions: DimensionUniverse, *, create: bool = False)

Bases: object

Registry interface.

Parameters:

config : ButlerConfig, RegistryConfig, Config or str

Registry configuration

Attributes Summary

defaultConfigFile

Path to configuration defaults.

Methods Summary

associate(collection, refs)	Add existing Datasets to a collection, implicitly creating the collection if it does not already exist.
attachComponent(name, parent, component)	Attach a component to a dataset.
deleteOpaqueData(tableName, **where)	Remove records from an opaque table.
disassociate(collection, refs)	Remove existing Datasets from a collection.
expandDataId(dataId, Mapping[str, Any], …)	Expand a dimension-based data ID to include additional information.
fetchOpaqueData(tableName, **where)	Retrieve records from an opaque table.
find(collection, datasetType, str], dataId, …)	Lookup a dataset.
fromConfig(config, RegistryConfig, Config, …)	Create Registry subclass instance from config.
getAllCollections()	Get names of all the collections found in this repository.
getAllDatasetTypes()	Get every registered DatasetType.
getDataset(id, datasetType, dataId)	Retrieve a Dataset entry.
getDatasetLocations(ref)	Retrieve datastore locations for a given dataset.
getDatasetType(name)	Get the DatasetType.
insertDatasetLocations(datastoreName, refs)	Record that a datastore holds the given datasets.
insertDatasets(datasetType, str], dataIds, …)	Insert one or more datasets into the Registry
insertDimensionData(element, str], *data, …)	Insert one or more dimension records into the database.
insertOpaqueData(tableName, *data)	Insert records into an opaque table.
makeQueryBuilder(summary)	Return a QueryBuilder instance capable of constructing and managing more complex queries than those obtainable via Registry interfaces.
queryDatasets(datasetType, str, …)	Query for and iterate over dataset references matching user-provided criteria.
queryDimensions(dimensions, str]], …)	Query for and iterate over data IDs matching user-provided criteria.
registerDatasetType(datasetType)	Add a new DatasetType to the Registry.
registerOpaqueTable(tableName, spec)	Add an opaque (to the Registry) table for use by a Datastore or other data repository client.
registerRun(name)	Add a new run if one with the given name does not exist.
removeDataset(ref)	Remove a dataset from the Registry.
removeDatasetLocation(datastoreName, ref)	Remove datastore location associated with this dataset.
transaction()	Return a context manager that represents a transaction.

Attributes Documentation

defaultConfigFile = None

Path to configuration defaults. Relative to $DAF_BUTLER_DIR/config or absolute path. Can be None if no defaults specified.

Methods Documentation

associate(collection: str, refs: List[DatasetRef])

Add existing Datasets to a collection, implicitly creating the collection if it does not already exist.

If a DatasetRef with the same exact dataset_id is already in a collection nothing is changed. If a DatasetRef with the same DatasetType1 and dimension values but with different dataset_id exists in the collection, ValueError is raised.

Parameters:

collection : str

Indicates the collection the Datasets should be associated with.

refs : iterable of DatasetRef

An iterable of DatasetRef instances that already exist in this Registry. All component datasets will be associated with the collection as well.

Raises:

ConflictingDefinitionError

If a Dataset with the given DatasetRef already exists in the given collection.

AmbiguousDatasetError

Raised if any(ref.id is None for ref in refs).

attachComponent(name: str, parent: DatasetRef, component: DatasetRef)

Attach a component to a dataset.

Parameters:

name : str

Name of the component.

parent : DatasetRef

A reference to the parent dataset. Will be updated to reference the component.

component : DatasetRef

A reference to the component dataset.

Raises:

AmbiguousDatasetError

Raised if parent.id or component.id is None.

deleteOpaqueData(tableName: str, **where)

Remove records from an opaque table.

Parameters:

tableName : str

Logical name of the opaque table. Must match the name used in a previous call to registerOpaqueTable.

where

Additional keyword arguments are interpreted as equality constraints that restrict the deleted rows (combined with AND); keyword arguments are column names and values are the values they must have.

disassociate(collection: str, refs: List[DatasetRef])

Remove existing Datasets from a collection.

collection and ref combinations that are not currently associated are silently ignored.

Parameters:

collection : str

The collection the Datasets should no longer be associated with.

refs : list of DatasetRef

A list of DatasetRef instances that already exist in this Registry. All component datasets will also be removed.

Raises:

AmbiguousDatasetError

Raised if any(ref.id is None for ref in refs).

expandDataId(dataId: Union[lsst.daf.butler.core.dimensions.coordinate.DataCoordinate, Mapping[str, Any], None] = None, *, graph: Optional[lsst.daf.butler.core.dimensions.graph.DimensionGraph] = None, records: Optional[Mapping[lsst.daf.butler.core.dimensions.elements.DimensionElement, lsst.daf.butler.core.dimensions.records.DimensionRecord]] = None, **kwds)

Expand a dimension-based data ID to include additional information.

Parameters:

dataId : DataCoordinate or dict, optional

Data ID to be expanded; augmented and overridden by kwds.

graph : DimensionGraph, optional

Set of dimensions for the expanded ID. If None, the dimensions will be inferred from the keys of dataId and kwds. Dimensions that are in dataId or kwds but not in graph are silently ignored, providing a way to extract and expand a subset of a data ID.

records : mapping [DimensionElement, DimensionRecord], optional

Dimension record data to use before querying the database for that data.

**kwds

Additional keywords are treated like additional key-value pairs for dataId, extending and overriding

Returns:

expanded : ExpandedDataCoordinate

A data ID that includes full metadata for all of the dimensions it identifieds.

fetchOpaqueData(tableName: str, **where) → Iterator[dict]

Retrieve records from an opaque table.

Parameters:

tableName : str

Logical name of the opaque table. Must match the name used in a previous call to registerOpaqueTable.

where

Additional keyword arguments are interpreted as equality constraints that restrict the returned rows (combined with AND); keyword arguments are column names and values are the values they must have.

Yields:

row : dict

A dictionary representing a single result row.

find(collection: str, datasetType: Union[lsst.daf.butler.core.datasets.type.DatasetType, str], dataId: Union[lsst.daf.butler.core.dimensions.coordinate.DataCoordinate, Mapping[str, Any], None] = None, **kwds) → Optional[lsst.daf.butler.core.datasets.ref.DatasetRef]

Lookup a dataset.

This can be used to obtain a DatasetRef that permits the dataset to be read from a Datastore.

Parameters:

collection : str

Identifies the collection to search.

datasetType : DatasetType or str

A DatasetType or the name of one.

dataId : dict or DataCoordinate, optional

A dict-like object containing the Dimension links that identify the dataset within a collection.

**kwds

Additional keyword arguments passed to DataCoordinate.standardize to convert dataId to a true DataCoordinate or augment an existing one.

Returns:

ref : DatasetRef

A ref to the Dataset, or None if no matching Dataset was found.

Raises:

LookupError

If one or more data ID keys are missing.

classmethod fromConfig(config: Union[ButlerConfig, RegistryConfig, Config, str], create: bool = False, butlerRoot: Optional[str] = None) → Registry

Create Registry subclass instance from config.

Uses registry.cls from config to determine which subclass to instantiate.

Parameters:

config : ButlerConfig, RegistryConfig, Config or str

Registry configuration

create : bool, optional

Assume empty Registry and create a new one.

butlerRoot : str, optional

Path to the repository root this Registry will manage.

Returns:

registry : Registry (subclass)

A new Registry subclass instance.

getAllCollections()

Get names of all the collections found in this repository.

Returns:

collections : set of str

The collections.

getAllDatasetTypes() → FrozenSet[lsst.daf.butler.core.datasets.type.DatasetType]

Get every registered DatasetType.

Returns:

types : frozenset of DatasetType

Every DatasetType in the registry.

getDataset(id: int, datasetType: Optional[lsst.daf.butler.core.datasets.type.DatasetType] = None, dataId: Optional[lsst.daf.butler.core.dimensions.coordinate.DataCoordinate] = None) → Optional[lsst.daf.butler.core.datasets.ref.DatasetRef]

Retrieve a Dataset entry.

Parameters:

id : int

The unique identifier for the Dataset.

datasetType : DatasetType, optional

The DatasetType of the dataset to retrieve. This is used to short-circuit retrieving the DatasetType, so if provided, the caller is guaranteeing that it is what would have been retrieved.

dataId : DataCoordinate, optional

A Dimension-based identifier for the dataset within a collection, possibly containing additional metadata. This is used to short-circuit retrieving the dataId, so if provided, the caller is guaranteeing that it is what would have been retrieved.

Returns:

ref : DatasetRef

A ref to the Dataset, or None if no matching Dataset was found.

getDatasetLocations(ref: lsst.daf.butler.core.datasets.ref.DatasetRef) → Set[str]

Retrieve datastore locations for a given dataset.

Typically used by Datastore.

Parameters:

ref : DatasetRef

A reference to the dataset for which to retrieve storage information.

Returns:

datastores : set of str

All the matching datastores holding this dataset. Empty set if the dataset does not exist anywhere.

Raises:

AmbiguousDatasetError

Raised if ref.id is None.

getDatasetType(name: str) → lsst.daf.butler.core.datasets.type.DatasetType

Get the DatasetType.

Parameters:

name : str

Name of the type.

Returns:

type : DatasetType

The DatasetType associated with the given name.

Raises:

KeyError

Requested named DatasetType could not be found in registry.

insertDatasetLocations(datastoreName: str, refs: Iterable[DatasetRef])

Record that a datastore holds the given datasets.

Typically used by Datastore.

Parameters:

datastoreName : str

Name of the datastore holding these datasets.

refs : Iterable of DatasetRef

References to the datasets.

Raises:

AmbiguousDatasetError

Raised if any(ref.id is None for ref in refs).

insertDatasets(datasetType: Union[DatasetType, str], dataIds: Iterable[DataId], run: str, *, producer: Optional[Quantum] = None, recursive: bool = False) → List[DatasetRef]

Insert one or more datasets into the Registry

This always adds new datasets; to associate existing datasets with a new collection, use associate.

Parameters:

datasetType : DatasetType or str

A DatasetType or the name of one.

dataIds : Iterable of dict or DataCoordinate

Dimension-based identifiers for the new datasets.

run : str

The name of the run that produced the datasets.

producer : Quantum

Unit of work that produced the datasets. May be None to store no provenance information, but if present the Quantum must already have been added to the Registry.

recursive : bool

If True, recursively add datasets and attach entries for component datasets as well.

Returns:

refs : list of DatasetRef

Resolved DatasetRef instances for all given data IDs (in the same order).

ConflictingDefinitionError

If a dataset with the same dataset type and data ID as one of those given already exists in the given collection.

insertDimensionData(element: Union[lsst.daf.butler.core.dimensions.elements.DimensionElement, str], *data, conform: bool = True)

Insert one or more dimension records into the database.

Parameters:

element : DimensionElement or str

The DimensionElement or name thereof that identifies the table records will be inserted into.

data : dict or DimensionRecord (variadic)

One or more records to insert.

conform : bool, optional

If False (True is default) perform no checking or conversions, and assume that element is a DimensionElement instance and data is a one or more DimensionRecord instances of the appropriate subclass.

insertOpaqueData(tableName: str, *data)

Insert records into an opaque table.

Parameters:

tableName : str

Logical name of the opaque table. Must match the name used in a previous call to registerOpaqueTable.

data

Each additional positional argument is a dictionary that represents a single row to be added.

makeQueryBuilder(summary: lsst.daf.butler.registry.queries._structs.QuerySummary) → lsst.daf.butler.registry.queries._builder.QueryBuilder

Return a QueryBuilder instance capable of constructing and managing more complex queries than those obtainable via Registry interfaces.

This is an advanced SqlRegistry-only interface; downstream code should prefer Registry.queryDimensions and Registry.queryDatasets whenever those are sufficient.

Parameters:

summary: `QuerySummary`

Object describing and categorizing the full set of dimensions that will be included in the query.

Returns:

builder : QueryBuilder

Object that can be used to construct and perform advanced queries.

queryDatasets(datasetType: Union[lsst.daf.butler.core.datasets.type.DatasetType, str, lsst.daf.butler.registry.queries._datasets.Like, ellipsis], *, collections: Union[Sequence[Union[str, lsst.daf.butler.registry.queries._datasets.Like]], ellipsis], dimensions: Optional[Iterable[Union[lsst.daf.butler.core.dimensions.elements.Dimension, str]]] = None, dataId: Union[lsst.daf.butler.core.dimensions.coordinate.DataCoordinate, Mapping[str, Any], None] = None, where: Optional[str] = None, deduplicate: bool = False, expand: bool = True, **kwds) → Iterator[lsst.daf.butler.core.datasets.ref.DatasetRef]

Query for and iterate over dataset references matching user-provided criteria.

Parameters:

datasetType : DatasetType, str, Like, or ...

An expression indicating type(s) of datasets to query for. ... may be used to query for all known DatasetTypes. Multiple explicitly-provided dataset types cannot be queried in a single call to queryDatasets even though wildcard expressions can, because the results would be identical to chaining the iterators produced by multiple calls to queryDatasets.

collections: `~collections.abc.Sequence` of `str` or `Like`, or ``…``

An expression indicating the collections to be searched for datasets. ... may be passed to search all collections.

dimensions : Iterable of Dimension or str

Dimensions to include in the query (in addition to those used to identify the queried dataset type(s)), either to constrain the resulting datasets to those for which a matching dimension exists, or to relate the dataset type’s dimensions to dimensions referenced by the dataId or where arguments.

dataId : dict or DataCoordinate, optional

A data ID whose key-value pairs are used as equality constraints in the query.

where : str, optional

A string expression similar to a SQL WHERE clause. May involve any column of a dimension table or (as a shortcut for the primary key column of a dimension table) dimension name.

deduplicate : bool, optional

If True (False is default), for each result data ID, only yield one DatasetRef of each DatasetType, from the first collection in which a dataset of that dataset type appears (according to the order of collections passed in). Cannot be used if any element in collections is an expression.

expand : bool, optional

If True (default) attach ExpandedDataCoordinate instead of minimal DataCoordinate base-class instances.

kwds

Additional keyword arguments are forwarded to DataCoordinate.standardize when processing the dataId argument (and may be used to provide a constraining data ID even when the dataId argument is None).

Yields:

ref : DatasetRef

Dataset references matching the given query criteria. These are grouped by DatasetType if the query evaluates to multiple dataset types, but order is otherwise unspecified.

Raises:

TypeError

Raised when the arguments are incompatible, such as when a collection wildcard is pass when deduplicate is True.

Notes

When multiple dataset types are queried via a wildcard expression, the results of this operation are equivalent to querying for each dataset type separately in turn, and no information about the relationships between datasets of different types is included. In contexts where that kind of information is important, the recommended pattern is to use queryDimensions to first obtain data IDs (possibly with the desired dataset types and collections passed as constraints to the query), and then use multiple (generally much simpler) calls to queryDatasets with the returned data IDs passed as constraints.

queryDimensions(dimensions: Union[Iterable[Union[lsst.daf.butler.core.dimensions.elements.Dimension, str]], lsst.daf.butler.core.dimensions.elements.Dimension, str], *, dataId: Union[lsst.daf.butler.core.dimensions.coordinate.DataCoordinate, Mapping[str, Any], None] = None, datasets: Optional[Mapping[Union[lsst.daf.butler.core.datasets.type.DatasetType, str, lsst.daf.butler.registry.queries._datasets.Like, ellipsis], Union[Sequence[Union[str, lsst.daf.butler.registry.queries._datasets.Like]], ellipsis]]] = None, where: Optional[str] = None, expand: bool = True, **kwds) → Iterator[lsst.daf.butler.core.dimensions.coordinate.DataCoordinate]

Query for and iterate over data IDs matching user-provided criteria.

Parameters:

dimensions : Dimension or str, or iterable thereof

The dimensions of the data IDs to yield, as either Dimension instances or str. Will be automatically expanded to a complete DimensionGraph.

dataId : dict or DataCoordinate, optional

A data ID whose key-value pairs are used as equality constraints in the query.

datasets : Mapping, optional

Datasets whose existence in the registry constrain the set of data IDs returned. This is a mapping from a dataset type expression (a str name, a true DatasetType instance, a Like pattern for the name, or ... for all DatasetTypes) to a collections expression (a sequence of str or Like patterns, or for all collections).

where : str, optional

A string expression similar to a SQL WHERE clause. May involve any column of a dimension table or (as a shortcut for the primary key column of a dimension table) dimension name.

expand : bool, optional

If True (default) yield ExpandedDataCoordinate instead of minimal DataCoordinate base-class instances.

kwds

Additional keyword arguments are forwarded to DataCoordinate.standardize when processing the dataId argument (and may be used to provide a constraining data ID even when the dataId argument is None).

Yields:

dataId : DataCoordinate

Data IDs matching the given query parameters. Order is unspecified.

registerDatasetType(datasetType: DatasetType) → bool

Add a new DatasetType to the Registry.

It is not an error to register the same DatasetType twice.

Parameters:

datasetType : DatasetType

The DatasetType to be added.

Returns:

inserted : bool

True if datasetType was inserted, False if an identical existing DatsetType was found. Note that in either case the DatasetType is guaranteed to be defined in the Registry consistently with the given definition.

Raises:

ValueError

Raised if the dimensions or storage class are invalid.

ConflictingDefinitionError

Raised if this DatasetType is already registered with a different definition.

registerOpaqueTable(tableName: str, spec: lsst.daf.butler.core.ddl.TableSpec)

Add an opaque (to the Registry) table for use by a Datastore or other data repository client.

Opaque table records can be added via insertOpaqueData, retrieved via fetchOpaqueData, and removed via deleteOpaqueData.

Parameters:

tableName : str

Logical name of the opaque table. This may differ from the actual name used in the database by a prefix and/or suffix.

spec : ddl.TableSpec

Specification for the table to be added.

registerRun(name: str)

Add a new run if one with the given name does not exist.

Parameters:

name : str

The name of the run to create.

Notes

This method cannot be called within transactions, as it needs to be able to perform its own transaction to be concurrent.

removeDataset(ref: DatasetRef)

Remove a dataset from the Registry.

The dataset and all components will be removed unconditionally from all collections, and any associated Quantum records will also be removed. Datastore records will not be deleted; the caller is responsible for ensuring that the dataset has already been removed from all Datastores.

Parameters:

ref : DatasetRef

Reference to the dataset to be removed. Must include a valid id attribute, and should be considered invalidated upon return.

Raises:

AmbiguousDatasetError

Raised if ref.id is None.

OrphanedRecordError

Raised if the dataset is still present in any Datastore.

removeDatasetLocation(datastoreName, ref)

Remove datastore location associated with this dataset.

Typically used by Datastore when a dataset is removed.

Parameters:

datastoreName : str

Name of this Datastore.

ref : DatasetRef

A reference to the dataset for which information is to be removed.

Raises:

AmbiguousDatasetError

Raised if ref.id is None.

transaction()

Return a context manager that represents a transaction.