Source code for kedro.io.data_catalog

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"""``DataCatalog`` stores instances of ``AbstractDataSet`` implementations to
provide ``load`` and ``save`` capabilities from anywhere in the program. To
use a ``DataCatalog``, you need to instantiate it with a dictionary of data
sets. Then it will act as a single point of reference for your calls,
relaying load and save functions to the underlying data sets.
"""
import copy
import difflib
import logging
import re
from collections import defaultdict
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Type, Union
from warnings import warn

from kedro.io.core import (
    AbstractDataSet,
    AbstractVersionedDataSet,
    DataSetAlreadyExistsError,
    DataSetError,
    DataSetNotFoundError,
    Version,
    generate_timestamp,
)
from kedro.io.memory_data_set import MemoryDataSet
from kedro.io.transformers import AbstractTransformer
from kedro.versioning import Journal

CATALOG_KEY = "catalog"
CREDENTIALS_KEY = "credentials"


def _get_credentials(
    credentials_name: str, credentials: Dict[str, Any]
) -> Dict[str, Any]:
    """Return a set of credentials from the provided credentials dict.

    Args:
        credentials_name: Credentials name.
        credentials: A dictionary with all credentials.

    Returns:
        The set of requested credentials.

    Raises:
        KeyError: When a data set with the given name has not yet been
            registered.

    """
    try:
        return credentials[credentials_name]
    except KeyError:
        raise KeyError(
            "Unable to find credentials '{}': check your data "
            "catalog and credentials configuration. See "
            "https://kedro.readthedocs.io/en/stable/kedro.io.DataCatalog.html "
            "for an example.".format(credentials_name)
        )


def _resolve_credentials(
    config: Dict[str, Any], credentials: Dict[str, Any]
) -> Dict[str, Any]:
    """Return the dataset configuration where credentials are resolved using
    credentials dictionary provided.

    Args:
        config: Original dataset config, which may contain unresolved credentials.
        credentials: A dictionary with all credentials.

    Returns:
        The dataset config, where all the credentials are successfully resolved.
    """
    config = copy.deepcopy(config)

    def _map_value(key: str, value: Any) -> Any:
        if key == CREDENTIALS_KEY and isinstance(value, str):
            return _get_credentials(value, credentials)
        if isinstance(value, dict):
            return {k: _map_value(k, v) for k, v in value.items()}
        return value

    return {k: _map_value(k, v) for k, v in config.items()}


class _FrozenDatasets:
    """Helper class to access underlying loaded datasets"""

    def __init__(self, datasets):
        # Non-alphanumeric characters (except underscore) in dataset name
        # are replaced with `__`, for easy access to transcoded/prefixed datasets.
        datasets = {
            re.sub("[^0-9a-zA-Z_]+", "__", key): value
            for key, value in datasets.items()
        }
        self.__dict__.update(**datasets)

    # Don't allow users to add/change attributes on the fly
    def __setattr__(self, key, value):
        msg = "Operation not allowed! "
        if key in self.__dict__.keys():
            msg += "Please change datasets through configuration."
        else:
            msg += "Please use DataCatalog.add() instead."
        raise AttributeError(msg)


[docs]class DataCatalog: """``DataCatalog`` stores instances of ``AbstractDataSet`` implementations to provide ``load`` and ``save`` capabilities from anywhere in the program. To use a ``DataCatalog``, you need to instantiate it with a dictionary of data sets. Then it will act as a single point of reference for your calls, relaying load and save functions to the underlying data sets. """ # pylint: disable=too-many-arguments
[docs] def __init__( self, data_sets: Dict[str, AbstractDataSet] = None, feed_dict: Dict[str, Any] = None, transformers: Dict[str, List[AbstractTransformer]] = None, default_transformers: List[AbstractTransformer] = None, journal: Journal = None, layers: Dict[str, Set[str]] = None, ) -> None: """``DataCatalog`` stores instances of ``AbstractDataSet`` implementations to provide ``load`` and ``save`` capabilities from anywhere in the program. To use a ``DataCatalog``, you need to instantiate it with a dictionary of data sets. Then it will act as a single point of reference for your calls, relaying load and save functions to the underlying data sets. Args: data_sets: A dictionary of data set names and data set instances. feed_dict: A feed dict with data to be added in memory. transformers: A dictionary of lists of transformers to be applied to the data sets. default_transformers: A list of transformers to be applied to any new data sets. journal: Instance of Journal. layers: A dictionary of data set layers. It maps a layer name to a set of data set names, according to the data engineering convention. For more details, see https://kedro.readthedocs.io/en/stable/06_resources/01_faq.html#what-is-data-engineering-convention Raises: DataSetNotFoundError: When transformers are passed for a non existent data set. Example: :: >>> from kedro.extras.datasets.pandas import CSVDataSet >>> >>> cars = CSVDataSet(filepath="cars.csv", >>> load_args=None, >>> save_args={"index": False}) >>> io = DataCatalog(data_sets={'cars': cars}) """ self._data_sets = dict(data_sets or {}) self.datasets = _FrozenDatasets(self._data_sets) self.layers = layers self._transformers = {k: list(v) for k, v in (transformers or {}).items()} self._default_transformers = list(default_transformers or []) self._check_and_normalize_transformers() self._journal = journal # import the feed dict if feed_dict: self.add_feed_dict(feed_dict)
@property def _logger(self): return logging.getLogger(__name__) def _check_and_normalize_transformers(self): data_sets = self._data_sets.keys() transformers = self._transformers.keys() excess_transformers = transformers - data_sets missing_transformers = data_sets - transformers if excess_transformers: raise DataSetNotFoundError( "Unexpected transformers for missing data_sets {}".format( ", ".join(excess_transformers) ) ) for data_set_name in missing_transformers: self._transformers[data_set_name] = list(self._default_transformers) # pylint: disable=too-many-arguments
[docs] @classmethod def from_config( cls: Type, catalog: Optional[Dict[str, Dict[str, Any]]], credentials: Dict[str, Dict[str, Any]] = None, load_versions: Dict[str, str] = None, save_version: str = None, journal: Journal = None, ) -> "DataCatalog": """Create a ``DataCatalog`` instance from configuration. This is a factory method used to provide developers with a way to instantiate ``DataCatalog`` with configuration parsed from configuration files. Args: catalog: A dictionary whose keys are the data set names and the values are dictionaries with the constructor arguments for classes implementing ``AbstractDataSet``. The data set class to be loaded is specified with the key ``type`` and their fully qualified class name. All ``kedro.io`` data set can be specified by their class name only, i.e. their module name can be omitted. credentials: A dictionary containing credentials for different data sets. Use the ``credentials`` key in a ``AbstractDataSet`` to refer to the appropriate credentials as shown in the example below. load_versions: A mapping between dataset names and versions to load. Has no effect on data sets without enabled versioning. save_version: Version string to be used for ``save`` operations by all data sets with enabled versioning. It must: a) be a case-insensitive string that conforms with operating system filename limitations, b) always return the latest version when sorted in lexicographical order. journal: Instance of Journal. Returns: An instantiated ``DataCatalog`` containing all specified data sets, created and ready to use. Raises: DataSetError: When the method fails to create any of the data sets from their config. Example: :: >>> config = { >>> "cars": { >>> "type": "pandas.CSVDataSet", >>> "filepath": "cars.csv", >>> "save_args": { >>> "index": False >>> } >>> }, >>> "boats": { >>> "type": "pandas.CSVDataSet", >>> "filepath": "s3://aws-bucket-name/boats.csv", >>> "credentials": "boats_credentials" >>> "save_args": { >>> "index": False >>> } >>> } >>> } >>> >>> credentials = { >>> "boats_credentials": { >>> "client_kwargs": { >>> "aws_access_key_id": "<your key id>", >>> "aws_secret_access_key": "<your secret>" >>> } >>> } >>> } >>> >>> catalog = DataCatalog.from_config(config, credentials) >>> >>> df = catalog.load("cars") >>> catalog.save("boats", df) """ data_sets = {} catalog = copy.deepcopy(catalog) or {} credentials = copy.deepcopy(credentials) or {} run_id = journal.run_id if journal else None save_version = save_version or run_id or generate_timestamp() load_versions = copy.deepcopy(load_versions) or {} missing_keys = load_versions.keys() - catalog.keys() if missing_keys: warn( "`load_versions` keys [{}] are not found in the catalog.".format( ", ".join(sorted(missing_keys)) ) ) layers = defaultdict(set) # type: Dict[str, Set[str]] for ds_name, ds_config in catalog.items(): ds_layer = ds_config.pop("layer", None) if ds_layer is not None: layers[ds_layer].add(ds_name) ds_config = _resolve_credentials(ds_config, credentials) data_sets[ds_name] = AbstractDataSet.from_config( ds_name, ds_config, load_versions.get(ds_name), save_version ) dataset_layers = layers or None return cls(data_sets=data_sets, journal=journal, layers=dataset_layers)
def _get_dataset( self, data_set_name: str, version: Version = None ) -> AbstractDataSet: if data_set_name not in self._data_sets: error_msg = f"DataSet '{data_set_name}' not found in the catalog" matches = difflib.get_close_matches(data_set_name, self._data_sets.keys()) if matches: suggestions = ", ".join(matches) # type: ignore error_msg += f" - did you mean one of these instead: {suggestions}" raise DataSetNotFoundError(error_msg) data_set = self._data_sets[data_set_name] if version and isinstance(data_set, AbstractVersionedDataSet): # we only want to return a similar-looking dataset, # not modify the one stored in the current catalog data_set = data_set._copy( # pylint: disable=protected-access _version=version ) return data_set def _get_transformed_dataset_function( self, data_set_name: str, operation: str, data_set: AbstractDataSet ) -> Callable: func = getattr(data_set, operation) for transformer in reversed(self._transformers[data_set_name]): func = partial(getattr(transformer, operation), data_set_name, func) return func
[docs] def load(self, name: str, version: str = None) -> Any: """Loads a registered data set. Args: name: A data set to be loaded. version: Optional argument for concrete data version to be loaded. Works only with versioned datasets. Returns: The loaded data as configured. Raises: DataSetNotFoundError: When a data set with the given name has not yet been registered. Example: :: >>> from kedro.io import DataCatalog >>> from kedro.extras.datasets.pandas import CSVDataSet >>> >>> cars = CSVDataSet(filepath="cars.csv", >>> load_args=None, >>> save_args={"index": False}) >>> io = DataCatalog(data_sets={'cars': cars}) >>> >>> df = io.load("cars") """ load_version = Version(version, None) if version else None dataset = self._get_dataset(name, version=load_version) self._logger.info( "Loading data from `%s` (%s)...", name, type(dataset).__name__ ) func = self._get_transformed_dataset_function(name, "load", dataset) result = func() version = ( dataset.resolve_load_version() if isinstance(dataset, AbstractVersionedDataSet) else None ) # Log only if versioning is enabled for the data set if self._journal and version: self._journal.log_catalog(name, "load", version) return result
[docs] def save(self, name: str, data: Any) -> None: """Save data to a registered data set. Args: name: A data set to be saved to. data: A data object to be saved as configured in the registered data set. Raises: DataSetNotFoundError: When a data set with the given name has not yet been registered. Example: :: >>> import pandas as pd >>> >>> from kedro.extras.datasets.pandas import CSVDataSet >>> >>> cars = CSVDataSet(filepath="cars.csv", >>> load_args=None, >>> save_args={"index": False}) >>> io = DataCatalog(data_sets={'cars': cars}) >>> >>> df = pd.DataFrame({'col1': [1, 2], >>> 'col2': [4, 5], >>> 'col3': [5, 6]}) >>> io.save("cars", df) """ dataset = self._get_dataset(name) self._logger.info("Saving data to `%s` (%s)...", name, type(dataset).__name__) func = self._get_transformed_dataset_function(name, "save", dataset) func(data) version = ( dataset.resolve_save_version() if isinstance(dataset, AbstractVersionedDataSet) else None ) # Log only if versioning is enabled for the data set if self._journal and version: self._journal.log_catalog(name, "save", version)
[docs] def exists(self, name: str) -> bool: """Checks whether registered data set exists by calling its `exists()` method. Raises a warning and returns False if `exists()` is not implemented. Args: name: A data set to be checked. Returns: Whether the data set output exists. Raises: DataSetNotFoundError: When a data set with the given name has not yet been registered. """ dataset = self._get_dataset(name) return dataset.exists()
[docs] def release(self, name: str): """Release any cached data associated with a data set Args: name: A data set to be checked. Raises: DataSetNotFoundError: When a data set with the given name has not yet been registered. """ dataset = self._get_dataset(name) dataset.release()
[docs] def add( self, data_set_name: str, data_set: AbstractDataSet, replace: bool = False ) -> None: """Adds a new ``AbstractDataSet`` object to the ``DataCatalog``. Args: data_set_name: A unique data set name which has not been registered yet. data_set: A data set object to be associated with the given data set name. replace: Specifies whether to replace an existing ``DataSet`` with the same name is allowed. Raises: DataSetAlreadyExistsError: When a data set with the same name has already been registered. Example: :: >>> from kedro.extras.datasets.pandas import CSVDataSet >>> >>> io = DataCatalog(data_sets={ >>> 'cars': CSVDataSet(filepath="cars.csv") >>> }) >>> >>> io.add("boats", CSVDataSet(filepath="boats.csv")) """ if data_set_name in self._data_sets: if replace: self._logger.warning("Replacing DataSet '%s'", data_set_name) else: raise DataSetAlreadyExistsError( "DataSet '{}' has already been registered".format(data_set_name) ) self._data_sets[data_set_name] = data_set self._transformers[data_set_name] = list(self._default_transformers) self.datasets = _FrozenDatasets(self._data_sets)
[docs] def add_all( self, data_sets: Dict[str, AbstractDataSet], replace: bool = False ) -> None: """Adds a group of new data sets to the ``DataCatalog``. Args: data_sets: A dictionary of ``DataSet`` names and data set instances. replace: Specifies whether to replace an existing ``DataSet`` with the same name is allowed. Raises: DataSetAlreadyExistsError: When a data set with the same name has already been registered. Example: :: >>> from kedro.extras.datasets.pandas import CSVDataSet, ParquetDataSet >>> >>> io = DataCatalog(data_sets={ >>> "cars": CSVDataSet(filepath="cars.csv") >>> }) >>> additional = { >>> "planes": ParquetDataSet("planes.parq"), >>> "boats": CSVDataSet(filepath="boats.csv") >>> } >>> >>> io.add_all(additional) >>> >>> assert io.list() == ["cars", "planes", "boats"] """ for name, data_set in data_sets.items(): self.add(name, data_set, replace)
[docs] def add_feed_dict(self, feed_dict: Dict[str, Any], replace: bool = False) -> None: """Adds instances of ``MemoryDataSet``, containing the data provided through feed_dict. Args: feed_dict: A feed dict with data to be added in memory. replace: Specifies whether to replace an existing ``DataSet`` with the same name is allowed. Example: :: >>> import pandas as pd >>> >>> df = pd.DataFrame({'col1': [1, 2], >>> 'col2': [4, 5], >>> 'col3': [5, 6]}) >>> >>> io = DataCatalog() >>> io.add_feed_dict({ >>> 'data': df >>> }, replace=True) >>> >>> assert io.load("data").equals(df) """ for data_set_name in feed_dict: if isinstance(feed_dict[data_set_name], AbstractDataSet): data_set = feed_dict[data_set_name] else: data_set = MemoryDataSet(data=feed_dict[data_set_name]) self.add(data_set_name, data_set, replace)
[docs] def add_transformer( self, transformer: AbstractTransformer, data_set_names: Union[str, Iterable[str]] = None, ): """Add a ``DataSet`` Transformer to the``DataCatalog``. Transformers can modify the way Data Sets are loaded and saved. Args: transformer: The transformer instance to add. data_set_names: The Data Sets to add the transformer to. Or None to add the transformer to all Data Sets. Raises: DataSetNotFoundError: When a transformer is being added to a non existent data set. TypeError: When transformer isn't an instance of ``AbstractTransformer`` """ if not isinstance(transformer, AbstractTransformer): raise TypeError( "Object of type {} is not an instance of AbstractTransformer".format( type(transformer) ) ) if data_set_names is None: self._default_transformers.append(transformer) data_set_names = self._transformers.keys() elif isinstance(data_set_names, str): data_set_names = [data_set_names] for data_set_name in data_set_names: if data_set_name not in self._data_sets: raise DataSetNotFoundError( "No data set called {}".format(data_set_name) ) self._transformers[data_set_name].append(transformer)
[docs] def list(self, regex_search: Optional[str] = None) -> List[str]: """ List of all ``DataSet`` names registered in the catalog. This can be filtered by providing an optional regular expression which will only return matching keys. Args: regex_search: An optional regular expression which can be provided to limit the data sets returned by a particular pattern. Returns: A list of ``DataSet`` names available which match the `regex_search` criteria (if provided). All data set names are returned by default. Raises: SyntaxError: When an invalid regex filter is provided. Example: :: >>> io = DataCatalog() >>> # get data sets where the substring 'raw' is present >>> raw_data = io.list(regex_search='raw') >>> # get data sets which start with 'prm' or 'feat' >>> feat_eng_data = io.list(regex_search='^(prm|feat)') >>> # get data sets which end with 'time_series' >>> models = io.list(regex_search='.+time_series$') """ if regex_search is None: return list(self._data_sets.keys()) if not regex_search.strip(): logging.warning("The empty string will not match any data sets") return [] try: pattern = re.compile(regex_search, flags=re.IGNORECASE) except re.error: raise SyntaxError(f"Invalid regular expression provided: `{regex_search}`") return [dset_name for dset_name in self._data_sets if pattern.search(dset_name)]
[docs] def shallow_copy(self) -> "DataCatalog": """Returns a shallow copy of the current object. Returns: Copy of the current object. """ return DataCatalog( data_sets=self._data_sets, transformers=self._transformers, default_transformers=self._default_transformers, journal=self._journal, )
def __eq__(self, other): return ( self._data_sets, self._transformers, self._default_transformers, self._journal, ) == ( other._data_sets, other._transformers, other._default_transformers, other._journal, )
[docs] def confirm(self, name: str) -> None: """Confirm a dataset by its name. Args: name: Name of the dataset. Raises: DataSetError: When the dataset does not have `confirm` method. """ self._logger.info("Confirming DataSet '%s'", name) data_set = self._get_dataset(name) if hasattr(data_set, "confirm"): data_set.confirm() # type: ignore else: raise DataSetError( "DataSet '{}' does not have 'confirm' method".format(name) )