Datastore¶

class lsst.daf.butler.Datastore(config: Union[Config, str], bridgeManager: DatastoreRegistryBridgeManager, butlerRoot: str = None)¶

Bases: object

Datastore interface.

Parameters:	config : `DatastoreConfig` or `str` Load configuration either from an existing config instance or by referring to a configuration file. bridgeManager : `DatastoreRegistryBridgeManager` Object that manages the interface between `Registry` and datastores. butlerRoot : `str`, optional New datastore root to use to override the configuration value.

Attributes Summary

`containerKey`	Name of the key containing a list of subconfigurations that also need to be merged with defaults and will likely use different Python datastore classes (but all using DatastoreConfig).
`defaultConfigFile`	Path to configuration defaults.
`isEphemeral`	Indicate whether this Datastore is ephemeral or not.
`names`	Names associated with this datastore returned as a list.

Methods Summary

`emptyTrash`(ignore_errors)	Remove all datasets from the trash.
`exists`(datasetRef)	Check if the dataset exists in the datastore.
`export`(refs, *, directory, transfer)	Export datasets for transfer to another data repository.
`fromConfig`(config, bridgeManager, …)	Create datastore from type specified in config file.
`get`(datasetRef, parameters, Any] = None)	Load an `InMemoryDataset` from the store.
`getLookupKeys`()	Return all the lookup keys relevant to this datastore.
`getURI`(datasetRef, predict)	URI to the Dataset.
`getURIs`(datasetRef, predict)	Return URIs associated with dataset.
`ingest`(*datasets, transfer)	Ingest one or more files into the datastore.
`put`(inMemoryDataset, datasetRef)	Write a `InMemoryDataset` with a given `DatasetRef` to the store.
`remove`(datasetRef)	Indicate to the Datastore that a Dataset can be removed.
`setConfigRoot`(root, config, full, overwrite)	Set any filesystem-dependent config options for this Datastore to be appropriate for a new empty repository with the given root.
`transaction`()	Context manager supporting `Datastore` transactions.
`transfer`(inputDatastore, datasetRef)	Retrieve a Dataset from an input `Datastore`, and store the result in this `Datastore`.
`trash`(datasetRef, ignore_errors)	Indicate to the Datastore that a Dataset can be moved to the trash.
`validateConfiguration`(entities, DatasetType, …)	Validate some of the configuration for this datastore.
`validateKey`(lookupKey, entity, DatasetType, …)	Validate a specific look up key with supplied entity.

Attributes Documentation

containerKey = None¶: Name of the key containing a list of subconfigurations that also need to be merged with defaults and will likely use different Python datastore classes (but all using DatastoreConfig). Assumed to be a list of configurations that can be represented in a DatastoreConfig and containing a “cls” definition. None indicates that no containers are expected in this Datastore.

defaultConfigFile = None¶: Path to configuration defaults. Accessed within the config resource or relative to a search path. Can be None if no defaults specified.

isEphemeral = False¶: Indicate whether this Datastore is ephemeral or not. An ephemeral datastore is one where the contents of the datastore will not exist across process restarts. This value can change per-instance.

names¶

Names associated with this datastore returned as a list.

Can be different to name for a chaining datastore.

Methods Documentation

emptyTrash(ignore_errors: bool = True) → None¶

Remove all datasets from the trash.

Parameters:	ignore_errors : `bool`, optional Determine whether errors should be ignored.

Notes

Some Datastores may implement this method as a silent no-op to disable Dataset deletion through standard interfaces.

exists(datasetRef: DatasetRef) → bool¶

Check if the dataset exists in the datastore.

Parameters:	datasetRef : `DatasetRef` Reference to the required dataset.
Returns:	exists : `bool` `True` if the entity exists in the `Datastore`.

export(refs: Iterable[DatasetRef], *, directory: Optional[str] = None, transfer: Optional[str] = None) → Iterable[FileDataset]¶

Export datasets for transfer to another data repository.

Parameters:	refs : iterable of `DatasetRef` Dataset references to be exported. directory : `str`, optional Path to a directory that should contain files corresponding to output datasets. Ignored if `transfer` is `None`. transfer : `str`, optional Mode that should be used to move datasets out of the repository. Valid options are the same as those of the `transfer` argument to `ingest`, and datastores may similarly signal that a transfer mode is not supported by raising `NotImplementedError`.
Returns:	dataset : iterable of `DatasetTransfer` Structs containing information about the exported datasets, in the same order as `refs`.
Raises:	NotImplementedError Raised if the given transfer mode is not supported.

static fromConfig(config: Config, bridgeManager: DatastoreRegistryBridgeManager, butlerRoot: Optional[Union[str, ButlerURI]] = None) → 'Datastore'¶

Create datastore from type specified in config file.

Parameters:	config : `Config` Configuration instance. bridgeManager : `DatastoreRegistryBridgeManager` Object that manages the interface between `Registry` and datastores. butlerRoot : `str`, optional Butler root directory.

get(datasetRef: DatasetRef, parameters: Mapping[str, Any] = None) → Any¶

Load an InMemoryDataset from the store.

Parameters:	datasetRef : `DatasetRef` Reference to the required Dataset. parameters : `dict` `StorageClass`-specific parameters that specify a slice of the Dataset to be loaded.
Returns:	inMemoryDataset : `object` Requested Dataset or slice thereof as an InMemoryDataset.

getLookupKeys() → Set[LookupKey]¶

Return all the lookup keys relevant to this datastore.

Returns:	keys : `set` of `LookupKey` The keys stored internally for looking up information based on `DatasetType` name or `StorageClass`.

getURI(datasetRef: DatasetRef, predict: bool = False) → ButlerURI¶

URI to the Dataset.

Parameters:	datasetRef : `DatasetRef` Reference to the required Dataset. predict : `bool` If `True` attempt to predict the URI for a dataset if it does not exist in datastore.
Returns:	uri : `str` URI string pointing to the Dataset within the datastore. If the Dataset does not exist in the datastore, the URI may be a guess. If the datastore does not have entities that relate well to the concept of a URI the returned URI string will be descriptive. The returned URI is not guaranteed to be obtainable.
Raises:	FileNotFoundError A URI has been requested for a dataset that does not exist and guessing is not allowed.

getURIs(datasetRef: DatasetRef, predict: bool = False) → Tuple[Optional[ButlerURI], Dict[str, ButlerURI]]¶

Return URIs associated with dataset.

Parameters:	ref : `DatasetRef` Reference to the required dataset. predict : `bool`, optional If the datastore does not know about the dataset, should it return a predicted URI or not?
Returns:	primary : `ButlerURI` The URI to the primary artifact associated with this dataset. If the dataset was disassembled within the datastore this may be `None`. components : `dict` URIs to any components associated with the dataset artifact. Can be empty if there are no components.

ingest(*datasets, transfer: Optional[str] = None) → None¶

Ingest one or more files into the datastore.

Parameters:

datasets : FileDataset: Each positional argument is a struct containing information about a file to be ingested, including its path (either absolute or relative to the datastore root, if applicable), a complete DatasetRef (with dataset_id not None), and optionally a formatter class or its fully-qualified string name. If a formatter is not provided, the one the datastore would use for put on that dataset is assumed.
transfer : str, optional: How (and whether) the dataset should be added to the datastore. If None (default), the file must already be in a location appropriate for the datastore (e.g. within its root directory), and will not be modified. Other choices include “move”, “copy”, “link”, “symlink”, “relsymlink”, and “hardlink”. “link” is a special transfer mode that will first try to make a hardlink and if that fails a symlink will be used instead. “relsymlink” creates a relative symlink rather than use an absolute path. Most datastores do not support all transfer modes. “auto” is a special option that will let the data store choose the most natural option for itself.

Raises:

NotImplementedError: Raised if the datastore does not support the given transfer mode (including the case where ingest is not supported at all).
DatasetTypeNotSupportedError: Raised if one or more files to be ingested have a dataset type that is not supported by the datastore.
FileNotFoundError: Raised if one of the given files does not exist.
FileExistsError: Raised if transfer is not None but the (internal) location the file would be moved to is already occupied.

Notes

Subclasses should implement _prepIngest and _finishIngest instead of implementing ingest directly. Datastores that hold and delegate to child datastores may want to call those methods as well.

Subclasses are encouraged to document their supported transfer modes in their class documentation.

put(inMemoryDataset: Any, datasetRef: DatasetRef) → None¶

Write a InMemoryDataset with a given DatasetRef to the store.

Parameters:	inMemoryDataset : `object` The Dataset to store. datasetRef : `DatasetRef` Reference to the associated Dataset.

remove(datasetRef: DatasetRef) → None¶

Indicate to the Datastore that a Dataset can be removed.

Parameters:	datasetRef : `DatasetRef` Reference to the required Dataset.
Raises:	FileNotFoundError When Dataset does not exist.

Notes

Some Datastores may implement this method as a silent no-op to disable Dataset deletion through standard interfaces.

classmethod setConfigRoot(root: str, config: lsst.daf.butler.core.config.Config, full: lsst.daf.butler.core.config.Config, overwrite: bool = True) → None¶

Set any filesystem-dependent config options for this Datastore 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 DatastoreConfig. 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() → Iterator[lsst.daf.butler.core.datastore.DatastoreTransaction]¶

Context manager supporting Datastore transactions.

Transactions can be nested, and are to be used in combination with Registry.transaction.

transfer(inputDatastore: Datastore, datasetRef: DatasetRef) → None¶

Retrieve a Dataset from an input Datastore, and store the result in this Datastore.

Parameters:	inputDatastore : `Datastore` The external `Datastore` from which to retreive the Dataset. datasetRef : `DatasetRef` Reference to the required Dataset.

trash(datasetRef: DatasetRef, ignore_errors: bool = True) → None¶

Indicate to the Datastore that a Dataset can be moved to the trash.

Parameters:	datasetRef : `DatasetRef` Reference to the required Dataset. ignore_errors : `bool`, optional Determine whether errors should be ignored.
Raises:	FileNotFoundError When Dataset does not exist.

Notes

Some Datastores may implement this method as a silent no-op to disable Dataset deletion through standard interfaces.

validateConfiguration(entities: Iterable[Union[DatasetRef, DatasetType, StorageClass]], logFailures: bool = False) → None¶

Validate some of the configuration for this datastore.

Parameters:	entities : iterable of `DatasetRef`, `DatasetType`, or `StorageClass` Entities to test against this configuration. Can be differing types. logFailures : `bool`, optional If `True`, output a log message for every validation error detected.
Raises:	DatastoreValidationError Raised if there is a validation problem with a configuration.

Notes

Which parts of the configuration are validated is at the discretion of each Datastore implementation.

validateKey(lookupKey: LookupKey, entity: Union[DatasetRef, DatasetType, StorageClass]) → None¶

Validate a specific look up key with supplied entity.

Parameters:	lookupKey : `LookupKey` Key to use to retrieve information from the datastore configuration. entity : `DatasetRef`, `DatasetType`, or `StorageClass` Entity to compare with configuration retrieved using the specified lookup key.
Raises:	DatastoreValidationError Raised if there is a problem with the combination of entity and lookup key.

Notes

Bypasses the normal selection priorities by allowing a key that would normally not be selected to be validated.

Navigation

Datastore¶