SqlRegistry

class lsst.daf.butler.registries.sqlRegistry.SqlRegistry(registryConfig, schemaConfig, dimensionConfig, create=False, butlerRoot=None)

Bases: lsst.daf.butler.Registry

Registry backed by a SQL database.

Parameters:

registryConfig : SqlRegistryConfig or str

Load configuration

schemaConfig : SchemaConfig or str

Definition of the schema to use.

dimensionConfig : DimensionConfig or Config or

DimensionGraph configuration.

create : bool

Assume registry is empty and create a new one.

Attributes Summary

defaultConfigFile

Path to configuration defaults.

Methods Summary

addDataset(datasetType, dataId, run[, …])	Adds a Dataset entry to the Registry
addDatasetLocation(ref, datastoreName)	Add datastore name locating a given dataset.
addExecution(execution)	Add a new Execution to the Registry.
addRun(run)	Add a new Run to the Registry.
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(name, **where)	Remove records from an opaque table.
disassociate(collection, refs)	Remove existing Datasets from a collection.
ensureRun(run)	Conditionally add a new Run to the Registry.
expandDataId(dataId, Mapping[str, Any], …)	Expand a dimension-based data ID to include additional information.
fetchOpaqueData(name, **where)	Retrieve records from an opaque table.
find(collection, datasetType[, dataId])	Lookup a dataset.
fromConfig(registryConfig[, schemaConfig, …])	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.
getExecution(id)	Retrieve an Execution.
getRun([id, collection])	Get a Run corresponding to its collection or id
insertDimensionData(element, str], *data, …)	Insert one or more dimension records into the database.
insertOpaqueData(name, *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.
makeRun(collection)	Create a new Run in the Registry and return it.
query(sql, **params)	Execute a SQL SELECT statement directly.
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(name, spec)	Add an opaque (to the Registry) table for use by a Datastore or other data repository client.
removeDataset(ref)	Remove a dataset from the Registry.
removeDatasetLocation(datastoreName, ref)	Remove datastore location associated with this dataset.
setConfigRoot(root, config, full[, overwrite])	Set any filesystem-dependent config options for this Registry to be appropriate for a new empty repository with the given root.
transaction()	Context manager that implements SQL transactions.

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

addDataset(datasetType, dataId, run, producer=None, recursive=False, **kwds)

Adds a Dataset entry to the Registry

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

Parameters:

datasetType : DatasetType or str

A DatasetType or the name of one.

dataId : dict or DataCoordinate

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

run : Run

The Run instance that produced the Dataset. Ignored if producer is passed (producer.run is then used instead). A Run must be provided by one of the two arguments.

producer : Quantum

Unit of work that produced the Dataset. 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 Dataset and attach entries for component Datasets as well.

kwds

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

Returns:

ref : DatasetRef

A newly-created DatasetRef instance.

Raises:

ConflictingDefinitionError

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

Exception

If dataId contains unknown or invalid Dimension entries.

addDatasetLocation(ref, datastoreName)

Add datastore name locating a given dataset.

Typically used by Datastore.

Parameters:

ref : DatasetRef

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

datastoreName : str

Name of the datastore holding this dataset.

Raises:

AmbiguousDatasetError

Raised if ref.id is None.

addExecution(execution)

Add a new Execution to the Registry.

If execution.id is None the Registry will update it to that of the newly inserted entry.

Parameters:

execution : Execution

Instance to add to the Registry. The given Execution must not already be present in the Registry.

Raises:

ConflictingDefinitionError

If execution is already present in the Registry.

addRun(run)

Add a new Run to the Registry.

Parameters:

run : Run

Instance to add to the Registry. The given Run must not already be present in the Registry (or any other). Therefore its id must be None and its collection must not be associated with any existing Run.

Raises:

ConflictingDefinitionError

If a run already exists with this collection.

associate(collection, refs)

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.

attachComponent(name, parent, component)

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(name: str, **where)

Remove records from an opaque table.

Parameters:

name : 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 deketed rows (combined with AND); keyword arguments are column names and values are the values they must have.

disassociate(collection, refs)

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).

ensureRun(run)

Conditionally add a new Run to the Registry.

If the run.id is None or a Run with this id or collection doesn’t exist in the Registry yet, add it. Otherwise, ensure the provided run is identical to the one already in the registry.

Parameters:

run : Run

Instance to add to the Registry.

Raises:

ConflictingDefinitionError

If run already exists, but is not identical.

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.

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

Retrieve records from an opaque table.

Parameters:

name : 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, datasetType, dataId=None, **kwds)

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.

static fromConfig(registryConfig, schemaConfig=None, dimensionConfig=None, create=False, butlerRoot=None)

Create Registry subclass instance from config.

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

Parameters:

registryConfig : ButlerConfig, RegistryConfig, Config or str

Registry configuration

schemaConfig : SchemaConfig, Config or str, optional.

Schema configuration. Can be read from supplied registryConfig if the relevant component is defined and schemaConfig is None.

dimensionConfig : DimensionConfig or Config or

str, optional. DimensionGraph configuration. Can be read from supplied registryConfig if the relevant component is defined and dimensionConfig is None.

create : bool

Assume empty Registry and create a new one.

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()

Get every registered DatasetType.

Returns:

types : frozenset of DatasetType

Every DatasetType in the registry.

getDataset(id, datasetType=None, dataId=None)

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)

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)

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.

getExecution(id)

Retrieve an Execution.

Parameters:

id : int

The unique identifier for the Execution.

getRun(id=None, collection=None)

Get a Run corresponding to its collection or id

Parameters:

id : int, optional

Retrieve the run with the given integer id.

collection : str

If given, lookup by collection name instead.

Returns:

run : Run

The Run instance.

Raises:

ValueError

Must supply one of collection or id.

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(name: str, *data)

Insert records into an opaque table.

Parameters:

name : 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.core.queries.structs.QuerySummary) → lsst.daf.butler.core.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.

makeRun(collection)

Create a new Run in the Registry and return it.

If a run with this collection already exists, return that instead.

Parameters:

collection : str

The collection used to identify all inputs and outputs of the Run.

Returns:

run : Run

A new Run instance.

query(sql, **params)

Execute a SQL SELECT statement directly.

Named parameters are specified in the SQL query string by preceeding them with a colon. Parameter values are provided as additional keyword arguments. For example:

registry.query(“SELECT * FROM instrument WHERE instrument=:name”,

name=”HSC”)

Parameters:

sql : str

SQL query string. Must be a SELECT statement.

**params

Parameter name-value pairs to insert into the query.

Yields:

row : dict

The next row result from executing the query.

queryDatasets(datasetType: Union[lsst.daf.butler.core.datasets.type.DatasetType, str, lsst.daf.butler.core.queries.datasets.Like, ellipsis], *, collections: Union[Sequence[Union[str, lsst.daf.butler.core.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.core.queries.datasets.Like, ellipsis], Union[Sequence[Union[str, lsst.daf.butler.core.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)

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(name: str, spec: lsst.daf.butler.core.schema.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:

name : 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 : TableSpec

Specification for the table to be added.

removeDataset(ref)

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.

classmethod setConfigRoot(root, config, full, overwrite=True)

Set any filesystem-dependent config options for this Registry to be appropriate for a new empty repository with the given root.

Parameters:

root : str

Filesystem path to the root of the data repository.

config : Config

A Config to update. Only the subset understood by this component will be updated. Will not expand defaults.

full : Config

A complete config with all defaults expanded that can be converted to a RegistryConfig. Read-only and will not be modified by this method. Repository-specific options that should not be obtained from defaults when Butler instances are constructed should be copied from full to config.

overwrite : bool, optional

If False, do not modify a value in config if the value already exists. Default is always to overwrite with the provided root.

Notes

If a keyword is explicitly defined in the supplied config it will not be overridden by this method if overwrite is False. This allows explicit values set in external configs to be retained.

transaction()

Context manager that implements SQL transactions.

Will roll back any changes to the SqlRegistry database in case an exception is raised in the enclosed block.

This context manager may be nested.