FileDatastore

class lsst.daf.butler.datastores.fileDatastore.FileDatastore(config: DatastoreConfig, bridgeManager: DatastoreRegistryBridgeManager, root: ResourcePath, formatterFactory: FormatterFactory, templates: FileTemplates, composites: CompositesMap, trustGetRequest: bool)

Bases: GenericBaseDatastore[StoredFileInfo]

Generic Datastore for file-based implementations.

Should always be sub-classed since key abstract methods are missing.

Parameters:

configDatastoreConfig or str

Configuration as either a Config object or URI to file.

bridgeManagerDatastoreRegistryBridgeManager

Object that manages the interface between Registry and datastores.

rootResourcePath

Root directory URI of this Datastore.

formatterFactoryFormatterFactory

Factory for creating instances of formatters.

templatesFileTemplates

File templates that can be used by this Datastore.

compositesCompositesMap

Determines whether a dataset should be disassembled on put.

trustGetRequestbool

Determine whether we can fall back to configuration if a requested dataset is not known to registry.

Raises:

ValueError

If root location does not exist and create is False in the configuration.

Attributes Summary

bridge
defaultConfigFile	Path to configuration defaults.
roots	Return the root URIs for each named datastore.

Methods Summary

addStoredItemInfo(refs, infos[, insert_mode])	Record internal storage information associated with one or more datasets.
clone(bridgeManager)	Make an independent copy of this Datastore with a different DatastoreRegistryBridgeManager instance.
computeChecksum(uri[, algorithm, block_size])	Compute the checksum of the supplied file.
emptyTrash([ignore_errors])	Remove all datasets from the trash.
exists(ref)	Check if the dataset exists in the datastore.
export(refs, *[, directory, transfer])	Export datasets for transfer to another data repository.
export_records(refs)	Export datastore records and locations to an in-memory data structure.
forget(refs)	Indicate to the Datastore that it should remove all records of the given datasets, without actually deleting them.
get(ref[, parameters, storageClass])	Load an InMemoryDataset from the store.
getLookupKeys()	Return all the lookup keys relevant to this datastore.
getManyURIs(refs[, predict, allow_missing])	Return URIs associated with many datasets.
getStoredItemsInfo(ref[, ...])	Retrieve information associated with files stored in this Datastore associated with this dataset ref.
getURI(ref[, predict])	URI to the Dataset.
getURIs(ref[, predict])	Return URIs associated with dataset.
get_opaque_table_definitions()	Make definitions of the opaque tables used by this Datastore.
import_records(data)	Import datastore location and record data from an in-memory data structure.
knows(ref)	Check if the dataset is known to the datastore.
knows_these(refs)	Check which of the given datasets are known to this datastore.
makeTableSpec()
mexists(refs[, artifact_existence])	Check the existence of multiple datasets at once.
needs_expanded_data_ids(transfer[, entity])	Test whether this datastore needs expanded data IDs to ingest.
prepare_get_for_external_client(ref)	Retrieve serializable data that can be used to execute a get().
put(inMemoryDataset, ref)	Write a InMemoryDataset with a given DatasetRef to the store.
put_new(in_memory_dataset, ref)	Write a InMemoryDataset with a given DatasetRef to the store.
removeStoredItemInfo(ref)	Remove information about the file associated with this dataset.
retrieveArtifacts(refs, destination[, ...])	Retrieve the file artifacts associated with the supplied refs.
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.
set_retrieve_dataset_type_method(method)	Specify a method that can be used by datastore to retrieve registry-defined dataset type.
transfer_from(source_datastore, refs[, ...])	Transfer dataset artifacts from another datastore to this one.
trash(ref[, ignore_errors])	Indicate to the Datastore that a Dataset can be moved to the trash.
validateConfiguration(entities[, logFailures])	Validate some of the configuration for this datastore.
validateKey(lookupKey, entity)	Validate a specific look up key with supplied entity.

Attributes Documentation

bridge

defaultConfigFile: ClassVar[str | None] = 'datastores/fileDatastore.yaml'

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

roots

Methods Documentation

addStoredItemInfo(refs: Iterable[DatasetRef], infos: Iterable[StoredFileInfo], insert_mode: DatabaseInsertMode = DatabaseInsertMode.INSERT) → None

Record internal storage information associated with one or more datasets.

Parameters:

refssequence of DatasetRef

The datasets that have been stored.

infossequence of StoredDatastoreItemInfo

Metadata associated with the stored datasets.

insert_modeDatabaseInsertMode

Mode to use to insert the new records into the table. The options are INSERT (error if pre-existing), REPLACE (replace content with new values), and ENSURE (skip if the row already exists).

clone(bridgeManager: DatastoreRegistryBridgeManager) → Datastore

Make an independent copy of this Datastore with a different DatastoreRegistryBridgeManager instance.

Parameters:

bridgeManagerDatastoreRegistryBridgeManager

New DatastoreRegistryBridgeManager object to use when instantiating managers.

Returns:

datastoreDatastore

New Datastore instance with the same configuration as the existing instance.

static computeChecksum(uri: ResourcePath, algorithm: str = 'blake2b', block_size: int = 8192) → str | None

Compute the checksum of the supplied file.

Parameters:

urilsst.resources.ResourcePath

Name of resource to calculate checksum from.

algorithmstr, optional

Name of algorithm to use. Must be one of the algorithms supported by :py:class`hashlib`.

block_sizeint

Number of bytes to read from file at one time.

Returns:

hexdigeststr

Hex digest of the file.

Notes

Currently returns None if the URI is for a remote resource.

emptyTrash(ignore_errors: bool = True) → None

Remove all datasets from the trash.

Parameters:

ignore_errorsbool

If True return without error even if something went wrong. Problems could occur if another process is simultaneously trying to delete.

exists(ref: DatasetRef) → bool

Check if the dataset exists in the datastore.

Parameters:

refDatasetRef

Reference to the required dataset.

Returns:

existsbool

True if the entity exists in the Datastore.

Notes

The local cache is checked as a proxy for existence in the remote object store. It is possible that another process on a different compute node could remove the file from the object store even though it is present in the local cache.

export(refs: Iterable[DatasetRef], *, directory: str | ParseResult | ResourcePath | Path | None = None, transfer: str | None = 'auto') → 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 explicitly 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. If “auto” is given and no directory is specified, None will be implied.

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.

export_records(refs: Iterable[DatasetIdRef]) → Mapping[str, DatastoreRecordData]

Export datastore records and locations to an in-memory data structure.

Parameters:

refsIterable [ DatasetIdRef ]

Datasets to save. This may include datasets not known to this datastore, which should be ignored. May not include component datasets.

Returns:

dataMapping [ str, DatastoreRecordData ]

Exported datastore records indexed by datastore name.

forget(refs: Iterable[DatasetRef]) → None

Indicate to the Datastore that it should remove all records of the given datasets, without actually deleting them.

Parameters:

refsIterable [ DatasetRef ]

References to the datasets being forgotten.

Notes

Asking a datastore to forget a DatasetRef it does not hold should be a silent no-op, not an error.

get(ref: DatasetRef, parameters: Mapping[str, Any] | None = None, storageClass: StorageClass | str | None = None) → Any

Load an InMemoryDataset from the store.

Parameters:

refDatasetRef

Reference to the required Dataset.

parametersdict

StorageClass-specific parameters that specify, for example, a slice of the dataset to be loaded.

storageClassStorageClass or str, optional

The storage class to be used to override the Python type returned by this method. By default the returned type matches the dataset type definition for this dataset. Specifying a read StorageClass can force a different type to be returned. This type must be compatible with the original type.

Returns:

inMemoryDatasetobject

Requested dataset or slice thereof as an InMemoryDataset.

Raises:

FileNotFoundError

Requested dataset can not be retrieved.

TypeError

Return value from formatter has unexpected type.

ValueError

Formatter failed to process the dataset.

getLookupKeys() → set[LookupKey]

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.

getManyURIs(refs: Iterable[DatasetRef], predict: bool = False, allow_missing: bool = False) → dict[lsst.daf.butler._dataset_ref.DatasetRef, lsst.daf.butler.datastore._datastore.DatasetRefURIs]

Return URIs associated with many datasets.

Parameters:

refsiterable of DatasetIdRef

References to the required datasets.

predictbool, optional

If True, allow URIs to be returned of datasets that have not been written.

allow_missingbool

If False, and predict is False, will raise if a DatasetRef does not exist.

Returns:

URIsdict of [DatasetRef, DatasetRefUris]

A dict of primary and component URIs, indexed by the passed-in refs.

Raises:

FileNotFoundError

A URI has been requested for a dataset that does not exist and guessing is not allowed.

Notes

In file-based datastores, getManyURIs does not check that the file is really there, it’s assuming it is if datastore is aware of the file then it actually exists.

getStoredItemsInfo(ref: DatasetIdRef, ignore_datastore_records: bool = False) → list[StoredFileInfo]

Retrieve information associated with files stored in this Datastore associated with this dataset ref.

Parameters:

refDatasetRef

The dataset that is to be queried.

ignore_datastore_recordsbool

If True then do not use datastore records stored in refs.

Returns:

itemsIterable [StoredDatastoreItemInfo]

Stored information about the files and associated formatters associated with this dataset. Only one file will be returned if the dataset has not been disassembled. Can return an empty list if no matching datasets can be found.

getURI(ref: DatasetRef, predict: bool = False) → ResourcePath

URI to the Dataset.

Parameters:

refDatasetRef

Reference to the required Dataset.

predictbool

If True, allow URIs to be returned of datasets that have not been written.

Returns:

uristr

URI pointing to the dataset within the datastore. If the dataset does not exist in the datastore, and if predict is True, the URI will be a prediction and will include a URI fragment “#predicted”. If the datastore does not have entities that relate well to the concept of a URI the returned URI will be descriptive. The returned URI is not guaranteed to be obtainable.

Raises:

FileNotFoundError

Raised if a URI has been requested for a dataset that does not exist and guessing is not allowed.

RuntimeError

Raised if a request is made for a single URI but multiple URIs are associated with this dataset.

Notes

When a predicted URI is requested an attempt will be made to form a reasonable URI based on file templates and the expected formatter.

getURIs(ref: DatasetRef, predict: bool = False) → DatasetRefURIs

Return URIs associated with dataset.

Parameters:

refDatasetRef

Reference to the required dataset.

predictbool, optional

If the datastore does not know about the dataset, controls whether it should return a predicted URI or not.

Returns:

urisDatasetRefURIs

The URI to the primary artifact associated with this dataset (if the dataset was disassembled within the datastore this may be None), and the URIs to any components associated with the dataset artifact. (can be empty if there are no components).

get_opaque_table_definitions() → Mapping[str, DatastoreOpaqueTable]

Make definitions of the opaque tables used by this Datastore.

Returns:

tablesMapping [ str, ddl.TableSpec ]

Mapping of opaque table names to their definitions. This can be an empty mapping if Datastore does not use opaque tables to keep datastore records.

import_records(data: Mapping[str, DatastoreRecordData]) → None

Import datastore location and record data from an in-memory data structure.

Parameters:

dataMapping [ str, DatastoreRecordData ]

Datastore records indexed by datastore name. May contain data for other Datastore instances (generally because they are chained to this one), which should be ignored.

Notes

Implementations should generally not check that any external resources (e.g. files) referred to by these records actually exist, for performance reasons; we expect higher-level code to guarantee that they do.

Implementations are responsible for calling DatastoreRegistryBridge.insert on all datasets in data.locations where the key is in names, as well as loading any opaque table data.

Implementations may assume that datasets are either fully present or not at all (single-component exports are not permitted).

knows(ref: DatasetRef) → bool

Check if the dataset is known to the datastore.

Does not check for existence of any artifact.

Parameters:

refDatasetRef

Reference to the required dataset.

Returns:

existsbool

True if the dataset is known to the datastore.

knows_these(refs: Iterable[DatasetRef]) → dict[lsst.daf.butler._dataset_ref.DatasetRef, bool]

Check which of the given datasets are known to this datastore.

This is like mexist() but does not check that the file exists.

Parameters:

refsiterable DatasetRef

The datasets to check.

Returns:

existsdict`[`DatasetRef, bool]

Mapping of dataset to boolean indicating whether the dataset is known to the datastore.

classmethod makeTableSpec() → TableSpec

mexists(refs: Iterable[DatasetRef], artifact_existence: dict[lsst.resources._resourcePath.ResourcePath, bool] | None = None) → dict[lsst.daf.butler._dataset_ref.DatasetRef, bool]

Check the existence of multiple datasets at once.

Parameters:

refsiterable of DatasetRef

The datasets to be checked.

artifact_existencedict [lsst.resources.ResourcePath, bool]

Optional mapping of datastore artifact to existence. Updated by this method with details of all artifacts tested. Can be None if the caller is not interested.

Returns:

existencedict of [DatasetRef, bool]

Mapping from dataset to boolean indicating existence.

Notes

To minimize potentially costly remote existence checks, the local cache is checked as a proxy for existence. If a file for this DatasetRef does exist no check is done for the actual URI. This could result in possibly unexpected behavior if the dataset itself has been removed from the datastore by another process whilst it is still in the cache.

needs_expanded_data_ids(transfer: str | None, entity: DatasetRef | DatasetType | StorageClass | None = None) → bool

Test whether this datastore needs expanded data IDs to ingest.

Parameters:

transferstr or None

Transfer mode for ingest.

entityDatasetRef or DatasetType or StorageClass or None, optional

Object representing what will be ingested. If not provided (or not specific enough), True may be returned even if expanded data IDs aren’t necessary.

Returns:

neededbool

If True, expanded data IDs may be needed. False only if expansion definitely isn’t necessary.

prepare_get_for_external_client(ref: DatasetRef) → FileDatastoreGetPayload | None

Retrieve serializable data that can be used to execute a get().

Parameters:

refDatasetRef

Reference to the required dataset.

Returns:

payloadobject | None

Serializable payload containing the information needed to perform a get() operation. This payload may be sent over the wire to another system to perform the get(). Returns None if the dataset is not known to this datastore.

put(inMemoryDataset: Any, ref: DatasetRef) → None

Write a InMemoryDataset with a given DatasetRef to the store.

Parameters:

inMemoryDatasetobject

The dataset to store.

refDatasetRef

Reference to the associated Dataset.

Raises:

TypeError

Supplied object and storage class are inconsistent.

DatasetTypeNotSupportedError

The associated DatasetType is not handled by this datastore.

Notes

If the datastore is configured to reject certain dataset types it is possible that the put will fail and raise a DatasetTypeNotSupportedError. The main use case for this is to allow ChainedDatastore to put to multiple datastores without requiring that every datastore accepts the dataset.

put_new(in_memory_dataset: Any, ref: DatasetRef) → Mapping[str, DatasetRef]

Write a InMemoryDataset with a given DatasetRef to the store.

Parameters:

in_memory_datasetobject

The Dataset to store.

refDatasetRef

Reference to the associated Dataset.

Returns:

datastore_refsMapping [str, DatasetRef]

Mapping of a datastore name to dataset reference stored in that datastore, reference will include datastore records. Only non-ephemeral datastores will appear in this mapping.

removeStoredItemInfo(ref: DatasetIdRef) → None

Remove information about the file associated with this dataset.

Parameters:

refDatasetRef

The dataset that has been removed.

retrieveArtifacts(refs: Iterable[DatasetRef], destination: ResourcePath, transfer: str = 'auto', preserve_path: bool = True, overwrite: bool = False) → list[lsst.resources._resourcePath.ResourcePath]

Retrieve the file artifacts associated with the supplied refs.

Parameters:

refsiterable of DatasetRef

The datasets for which file artifacts are to be retrieved. A single ref can result in multiple files. The refs must be resolved.

destinationlsst.resources.ResourcePath

Location to write the file artifacts.

transferstr, optional

Method to use to transfer the artifacts. Must be one of the options supported by lsst.resources.ResourcePath.transfer_from(). “move” is not allowed.

preserve_pathbool, optional

If True the full path of the file artifact within the datastore is preserved. If False the final file component of the path is used.

overwritebool, optional

If True allow transfers to overwrite existing files at the destination.

Returns:

targetslist of lsst.resources.ResourcePath

URIs of file artifacts in destination location. Order is not preserved.

classmethod setConfigRoot(root: str, config: Config, full: 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:

rootstr

URI 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.

set_retrieve_dataset_type_method(method: Callable[[str], DatasetType | None] | None) → None

Specify a method that can be used by datastore to retrieve registry-defined dataset type.

Parameters:

methodCallable | None

Method that takes a name of the dataset type and returns a corresponding DatasetType instance as defined in Registry. If dataset type name is not known to registry None is returned.

Notes

This method is only needed for a Datastore supporting a “trusted” mode when it does not have an access to datastore records and needs to guess dataset location based on its stored dataset type.

transfer_from(source_datastore: Datastore, refs: Collection[DatasetRef], transfer: str = 'auto', artifact_existence: dict[lsst.resources._resourcePath.ResourcePath, bool] | None = None, dry_run: bool = False) → tuple[set[lsst.daf.butler._dataset_ref.DatasetRef], set[lsst.daf.butler._dataset_ref.DatasetRef]]

Transfer dataset artifacts from another datastore to this one.

Parameters:

source_datastoreDatastore

The datastore from which to transfer artifacts. That datastore must be compatible with this datastore receiving the artifacts.

refsCollection of DatasetRef

The datasets to transfer from the source datastore.

transferstr, optional

How (and whether) the dataset should be added to the datastore. 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” (the default) is a special option that will let the data store choose the most natural option for itself. If the source location and transfer location are identical the transfer mode will be ignored.

artifact_existencedict [lsst.resources.ResourcePath, bool]

Optional mapping of datastore artifact to existence. Updated by this method with details of all artifacts tested. Can be None if the caller is not interested.

dry_runbool, optional

Process the supplied source refs without updating the target datastore.

Returns:

acceptedset [DatasetRef]

The datasets that were transferred.

rejectedset [DatasetRef]

The datasets that were rejected due to a constraints violation.

Raises:

TypeError

Raised if the two datastores are not compatible.

trash(ref: DatasetRef | Iterable[DatasetRef], ignore_errors: bool = True) → None

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

Parameters:

refDatasetRef or iterable thereof

Reference(s) to the required Dataset.

ignore_errorsbool, optional

Determine whether errors should be ignored. When multiple refs are being trashed there will be no per-ref check.

Raises:

FileNotFoundError

When Dataset does not exist and errors are not ignored. Only checked if a single ref is supplied (and not in a list).

Notes

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

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

Validate some of the configuration for this datastore.

Parameters:

entitiesiterable of DatasetRef, 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. All the problems are reported in a single exception.

Notes

This method checks that all the supplied entities have valid file templates and also have formatters defined.

validateKey(lookupKey: LookupKey, entity: DatasetRef | DatasetType | StorageClass) → None

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.