Datastore

class lsst.daf.butler.Datastore(config, registry, butlerRoot=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.

registry : Registry

Registry to use for storing internal information about the datasets.

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.

Methods Summary

exists(datasetRef)	Check if the dataset exists in the datastore.
export(refs, *, directory, transfer)	Export datasets for transfer to another data repository.
fromConfig(config, registry, butlerRoot)	Create datastore from type specified in config file.
get(datasetRef[, parameters])	Load an InMemoryDataset from the store.
getLookupKeys()	Return all the lookup keys relevant to this datastore.
getUri(datasetRef)	URI to the Dataset.
ingest(path, ref[, formatter, transfer])	Add an on-disk file with the given DatasetRef to the store, possibly transferring it.
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.
validateConfiguration(entities[, logFailures])	Validate some of the configuration for this datastore.
validateKey(lookupKey, entity[, logFailures])	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. Relative to $DAF_BUTLER_DIR/config or absolute 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.

Methods Documentation

exists(datasetRef)

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[DatasetExport]

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: lsst.daf.butler.core.config.Config, registry: lsst.daf.butler.core.registry.Registry, butlerRoot: Optional[str] = None) → Datastore

Create datastore from type specified in config file.

Parameters:

config : Config

Configuration instance.

registry : Registry

Registry to be used by the Datastore for internal data.

butlerRoot : str, optional

Butler root directory.

get(datasetRef, parameters=None)

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()

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)

URI to the Dataset.

Parameters:

datasetRef : DatasetRef

Reference to the required Dataset.

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.

ingest(path, ref, formatter=None, transfer=None)

Add an on-disk file with the given DatasetRef to the store, possibly transferring it.

The caller is responsible for ensuring that the given (or predicted) Formatter is consistent with how the file was written; ingest will in general silently ignore incorrect formatters (as it cannot efficiently verify their correctness), deferring errors until get is first called on the ingested dataset.

Datastores are not required to implement this method, but must do so in order to support direct raw data ingest.

Parameters:

path : str

File path, relative to the repository root.

ref : DatasetRef

Reference to the associated Dataset.

formatter : Formatter (optional)

Formatter that should be used to retreive the Dataset. If not provided, the formatter will be constructed according to Datastore configuration.

transfer : str (optional)

If not None, must be one of ‘move’, ‘copy’, ‘hardlink’, or ‘symlink’ indicating how to transfer the file. Datastores need not support all options, but must raise NotImplementedError if the passed option is not supported. That includes None, which indicates that the file should be ingested at its current location with no transfer. If a Datastore does support ingest-without-transfer in general, but the given path is not appropriate, an exception other than NotImplementedError that better describes the problem should be raised.

Raises:

NotImplementedError

Raised if the given transfer mode is not supported.

DatasetTypeNotSupportedError

The associated DatasetType is not handled by this datastore.

put(inMemoryDataset, datasetRef)

Write a InMemoryDataset with a given DatasetRef to the store.

Parameters:

inMemoryDataset : InMemoryDataset

The Dataset to store.

datasetRef : DatasetRef

Reference to the associated Dataset.

remove(datasetRef)

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)

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()

Context manager supporting Datastore transactions.

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

transfer(inputDatastore, datasetRef)

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.

validateConfiguration(entities, logFailures=False)

Validate some of the configuration for this datastore.

Parameters:

entities : 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, entity, logFailures=False)

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.