ChainedDatastore

class lsst.daf.butler.datastores.chainedDatastore.ChainedDatastore(config, registry=None, butlerRoot=None)

Bases: lsst.daf.butler.Datastore

Chained Datastores to allow read and writes from multiple datastores.

A ChainedDatastore is configured with multiple datastore configurations. A put() is always sent to each datastore. A get() operation is sent to each datastore in turn and the first datastore to return a valid dataset is used.

Parameters:

config : DatastoreConfig or str

Configuration. This configuration must include a datastores field as a sequence of datastore configurations. The order in this sequence indicates the order to use for read operations.

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. This root is sent to each child datastore.

Notes

ChainedDatastore never supports None or "move" as an ingest transfer mode. It supports "copy", "symlink", "relsymlink" and "hardlink" if and only if all its child datastores do.

Attributes Summary

containerKey	Key to specify where child datastores are configured.
defaultConfigFile	Path to configuration defaults.
isEphemeral
names	Names associated with this datastore returned as a list.

Methods Summary

emptyTrash([ignore_errors])	Remove all datasets from the trash.
exists(ref)	Check if the dataset exists in one of the datastores.
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(ref[, parameters])	Load an InMemoryDataset from the store.
getLookupKeys()	Return all the lookup keys relevant to this datastore.
getUri(ref[, predict])	URI to the Dataset.
ingest(*datasets, transfer)	Ingest one or more files into the datastore.
put(inMemoryDataset, ref)	Write a InMemoryDataset with a given DatasetRef to each datastore.
remove(ref)	Indicate to the datastore that a dataset can be removed.
setConfigRoot(root, config, full[, overwrite])	Set any filesystem-dependent config options for child Datastores to be appropriate for a new empty repository with the given root.
transaction()	Context manager supporting Datastore transactions.
transfer(inputDatastore, ref)	Retrieve a dataset from an input Datastore, and store the result in this Datastore.
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

containerKey = 'datastores'

Key to specify where child datastores are configured.

defaultConfigFile = 'datastores/chainedDatastore.yaml'

Path to configuration defaults. Relative to $DAF_BUTLER_DIR/config or absolute path. Can be None if no defaults specified.

isEphemeral = False

names

Names associated with this datastore returned as a list.

Some datastores can have child datastores.

Methods Documentation

emptyTrash(ignore_errors=True)

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(ref)

Check if the dataset exists in one of the datastores.

Parameters:

ref : DatasetRef

Reference to the required dataset.

Returns:

exists : bool

True if the entity exists in one of the child datastores.

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, 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(ref, parameters=None)

Load an InMemoryDataset from the store.

The dataset is returned from the first datastore that has the dataset.

Parameters:

ref : DatasetRef

Reference to the required Dataset.

parameters : dict

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

Returns:

inMemoryDataset : object

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()

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(ref, predict=False)

URI to the Dataset.

The returned URI is from the first datastore in the list that has the dataset with preference given to the first dataset coming from a permanent datastore. If no datastores have the dataset and prediction is allowed, the predicted URI for the first datastore in the list will be returned.

Parameters:

ref : DatasetRef

Reference to the required Dataset.

predict : bool

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

Returns:

uri : str

URI string 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”.

Raises:

FileNotFoundError

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

Notes

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, transfer: Optional[str] = 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, ref)

Write a InMemoryDataset with a given DatasetRef to each datastore.

The put() to child datastores can fail with DatasetTypeNotSupportedError. The put() for this datastore will be deemed to have succeeded so long as at least one child datastore accepted the inMemoryDataset.

Parameters:

inMemoryDataset : object

The dataset to store.

ref : DatasetRef

Reference to the associated Dataset.

Raises:

TypeError

Supplied object and storage class are inconsistent.

DatasetTypeNotSupportedError

All datastores reported DatasetTypeNotSupportedError.

remove(ref)

Indicate to the datastore that a dataset can be removed.

The dataset will be removed from each datastore. The dataset is not required to exist in every child datastore.

Parameters:

ref : DatasetRef

Reference to the required dataset.

Raises:

FileNotFoundError

Attempt to remove a dataset that does not exist. Raised if none of the child datastores removed the dataset.

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

Set any filesystem-dependent config options for child Datastores 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, ref)

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.

ref : DatasetRef

Reference to the required dataset in the input data store.

Returns:

results : list

List containing the return value from the put() to each child datastore.

trash(ref, ignore_errors=True)

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, logFailures=False)

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. All the problems are reported in a single exception.

Notes

This method checks each datastore in turn.

validateKey(lookupKey, entity)

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.