Datastore

class lsst.daf.butler.Datastore(config, registry, butlerRoot=None)

Bases: object

Datastore interface.

Parameters

configDatastoreConfig or str

Load configuration either from an existing config instance or by referring to a configuration file.

registryRegistry

Registry to use for storing internal information about the datasets.

butlerRootstr, 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(*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.
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

abstract exists(datasetRef)

Check if the dataset exists in the datastore.

Parameters

datasetRefDatasetRef

Reference to the required dataset.

Returns

existsbool

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

refsiterable of DatasetRef

Dataset references to be exported.

directorystr, optional

Path to a directory that should contain files corresponding to output datasets. Ignored if transfer is None.

transferstr, 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

datasetiterable 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

configConfig

Configuration instance.

registryRegistry

Registry to be used by the Datastore for internal data.

butlerRootstr, optional

Butler root directory.

abstract get(datasetRef, parameters=None)

Load an InMemoryDataset from the store.

Parameters

datasetRefDatasetRef

Reference to the required Dataset.

parametersdict

StorageClass-specific parameters that specify a slice of the Dataset to be loaded.

Returns

inMemoryDatasetobject

Requested Dataset or slice thereof as an InMemoryDataset.

abstract getLookupKeys()

Return all the lookup keys relevant to this datastore.

Returns

keysset of LookupKey

The keys stored internally for looking up information based on DatasetType name or StorageClass.

abstract getUri(datasetRef)

URI to the Dataset.

Parameters

datasetRefDatasetRef

Reference to the required Dataset.

Returns

uristr

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(*datasets: FileDataset, transfer: Optional[str] = None)

Ingest one or more files into the datastore.

Parameters

datasetsFileDataset

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.

transferstr, 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”, “symlink”, and “hardlink”. Most datastores do not support all transfer modes.

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.

abstract put(inMemoryDataset, datasetRef)

Write a InMemoryDataset with a given DatasetRef to the store.

Parameters

inMemoryDatasetInMemoryDataset

The Dataset to store.

datasetRefDatasetRef

Reference to the associated Dataset.

abstract remove(datasetRef)

Indicate to the Datastore that a Dataset can be removed.

Parameters

datasetRefDatasetRef

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.

abstract 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

rootstr

Filesystem path to the root of the data repository.

configConfig

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

fullConfig

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.

overwritebool, 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.

abstract transfer(inputDatastore, datasetRef)

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

Parameters

inputDatastoreDatastore

The external Datastore from which to retreive the Dataset.

datasetRefDatasetRef

Reference to the required Dataset.

abstract validateConfiguration(entities, logFailures=False)

Validate some of the configuration for this datastore.

Parameters

entitiesDatasetRef, DatasetType, or StorageClass

Entities to test against this configuration. Can be differing types.

logFailuresbool, 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.

abstract validateKey(lookupKey, entity, logFailures=False)

Validate a specific look up key with supplied entity.

Parameters

lookupKeyLookupKey

Key to use to retrieve information from the datastore configuration.

entityDatasetRef, 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.