Source code for kedro.io.partitioned_dataset

"""``PartitionedDataSet`` loads and saves partitioned file-like data using the
underlying dataset definition. It also uses `fsspec` for filesystem level operations.
"""
import operator
from copy import deepcopy
from typing import Any, Callable, Dict, List, Type, Union
from urllib.parse import urlparse
from warnings import warn

from cachetools import Cache, cachedmethod

from kedro.io.core import (
    VERSION_KEY,
    VERSIONED_FLAG_KEY,
    AbstractDataSet,
    DataSetError,
    parse_dataset_definition,
)
from kedro.io.data_catalog import CREDENTIALS_KEY
from kedro.utils import load_obj

DATASET_CREDENTIALS_KEY = "dataset_credentials"
CHECKPOINT_CREDENTIALS_KEY = "checkpoint_credentials"

KEY_PROPAGATION_WARNING = (
    "Top-level %(keys)s will not propagate into the %(target)s since "
    "%(keys)s were explicitly defined in the %(target)s config."
)

S3_PROTOCOLS = ("s3", "s3a", "s3n")


[docs]class PartitionedDataSet(AbstractDataSet): # pylint: disable=too-many-instance-attributes,protected-access """``PartitionedDataSet`` loads and saves partitioned file-like data using the underlying dataset definition. For filesystem level operations it uses `fsspec`: https://github.com/intake/filesystem_spec. It also supports advanced features like `lazy saving <https://kedro.readthedocs.io/en/stable/data/\ kedro_io.html#partitioned-dataset-lazy-saving>`_. Example usage for the `YAML API <https://kedro.readthedocs.io/en/stable/data/\ data_catalog.html#use-the-data-catalog-with-the-yaml-api>`_: .. code-block:: yaml station_data: type: PartitionedDataSet path: data/03_primary/station_data dataset: type: pandas.CSVDataSet load_args: sep: '\\t' save_args: sep: '\\t' index: true filename_suffix: '.dat' Example usage for the `Python API <https://kedro.readthedocs.io/en/stable/data/\ data_catalog.html#use-the-data-catalog-with-the-code-api>`_: :: >>> import pandas as pd >>> from kedro.io import PartitionedDataSet >>> >>> # Create a fake pandas dataframe with 10 rows of data >>> df = pd.DataFrame([{"DAY_OF_MONTH": str(i), "VALUE": i} for i in range(1, 11)]) >>> >>> # Convert it to a dict of pd.DataFrame with DAY_OF_MONTH as the dict key >>> dict_df = { day_of_month: df[df["DAY_OF_MONTH"] == day_of_month] for day_of_month in df["DAY_OF_MONTH"] } >>> >>> # Save it as small paritions with DAY_OF_MONTH as the partition key >>> data_set = PartitionedDataSet( path="df_with_partition", dataset="pandas.CSVDataSet", filename_suffix=".csv" ) >>> # This will create a folder `df_with_partition` and save multiple files >>> # with the dict key + filename_suffix as filename, i.e. 1.csv, 2.csv etc. >>> data_set.save(dict_df) >>> >>> # This will create lazy load functions instead of loading data into memory immediately. >>> loaded = data_set.load() >>> >>> # Load all the partitions >>> for partition_id, partition_load_func in loaded.items(): # The actual function that loads the data partition_data = partition_load_func() >>> >>> # Add the processing logic for individual partition HERE >>> print(partition_data) You can also load multiple partitions from a remote storage and combine them like this: :: >>> import pandas as pd >>> from kedro.io import PartitionedDataSet >>> >>> # these credentials will be passed to both 'fsspec.filesystem()' call >>> # and the dataset initializer >>> credentials = {"key1": "secret1", "key2": "secret2"} >>> >>> data_set = PartitionedDataSet( path="s3://bucket-name/path/to/folder", dataset="pandas.CSVDataSet", credentials=credentials ) >>> loaded = data_set.load() >>> # assert isinstance(loaded, dict) >>> >>> combine_all = pd.DataFrame() >>> >>> for partition_id, partition_load_func in loaded.items(): partition_data = partition_load_func() combine_all = pd.concat( [combine_all, partition_data], ignore_index=True, sort=True ) >>> >>> new_data = pd.DataFrame({"new": [1, 2]}) >>> # creates "s3://bucket-name/path/to/folder/new/partition.csv" >>> data_set.save({"new/partition.csv": new_data}) """
[docs] def __init__( # pylint: disable=too-many-arguments self, path: str, dataset: Union[str, Type[AbstractDataSet], Dict[str, Any]], filepath_arg: str = "filepath", filename_suffix: str = "", credentials: Dict[str, Any] = None, load_args: Dict[str, Any] = None, fs_args: Dict[str, Any] = None, overwrite: bool = False, ): """Creates a new instance of ``PartitionedDataSet``. Args: path: Path to the folder containing partitioned data. If path starts with the protocol (e.g., ``s3://``) then the corresponding ``fsspec`` concrete filesystem implementation will be used. If protocol is not specified, ``fsspec.implementations.local.LocalFileSystem`` will be used. **Note:** Some concrete implementations are bundled with ``fsspec``, while others (like ``s3`` or ``gcs``) must be installed separately prior to usage of the ``PartitionedDataSet``. dataset: Underlying dataset definition. This is used to instantiate the dataset for each file located inside the ``path``. Accepted formats are: a) object of a class that inherits from ``AbstractDataSet`` b) a string representing a fully qualified class name to such class c) a dictionary with ``type`` key pointing to a string from b), other keys are passed to the Dataset initializer. Credentials for the dataset can be explicitly specified in this configuration. filepath_arg: Underlying dataset initializer argument that will contain a path to each corresponding partition file. If unspecified, defaults to "filepath". filename_suffix: If specified, only partitions that end with this string will be processed. credentials: Protocol-specific options that will be passed to ``fsspec.filesystem`` https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.filesystem and the dataset initializer. If the dataset config contains explicit credentials spec, then such spec will take precedence. All possible credentials management scenarios are documented here: https://kedro.readthedocs.io/en/stable/data/kedro_io.html#partitioned-dataset-credentials load_args: Keyword arguments to be passed into ``find()`` method of the filesystem implementation. fs_args: Extra arguments to pass into underlying filesystem class constructor (e.g. `{"project": "my-project"}` for ``GCSFileSystem``) overwrite: If True, any existing partitions will be removed. Raises: DataSetError: If versioning is enabled for the underlying dataset. """ # pylint: disable=import-outside-toplevel from fsspec.utils import infer_storage_options # for performance reasons super().__init__() self._path = path self._filename_suffix = filename_suffix self._overwrite = overwrite self._protocol = infer_storage_options(self._path)["protocol"] self._partition_cache = Cache(maxsize=1) # type: Cache dataset = dataset if isinstance(dataset, dict) else {"type": dataset} self._dataset_type, self._dataset_config = parse_dataset_definition(dataset) if VERSION_KEY in self._dataset_config: raise DataSetError( f"'{self.__class__.__name__}' does not support versioning of the " f"underlying dataset. Please remove '{VERSIONED_FLAG_KEY}' flag from " f"the dataset definition." ) if credentials: if CREDENTIALS_KEY in self._dataset_config: self._logger.warning( KEY_PROPAGATION_WARNING, {"keys": CREDENTIALS_KEY, "target": "underlying dataset"}, ) else: self._dataset_config[CREDENTIALS_KEY] = deepcopy(credentials) self._credentials = deepcopy(credentials) or {} self._fs_args = deepcopy(fs_args) or {} if self._fs_args: if "fs_args" in self._dataset_config: self._logger.warning( KEY_PROPAGATION_WARNING, {"keys": "filesystem arguments", "target": "underlying dataset"}, ) else: self._dataset_config["fs_args"] = deepcopy(self._fs_args) self._filepath_arg = filepath_arg if self._filepath_arg in self._dataset_config: warn( f"'{self._filepath_arg}' key must not be specified in the dataset " f"definition as it will be overwritten by partition path" ) self._load_args = deepcopy(load_args) or {} self._sep = self._filesystem.sep # since some filesystem implementations may implement a global cache self._invalidate_caches()
@property def _filesystem(self): # for performance reasons import fsspec # pylint: disable=import-outside-toplevel protocol = "s3" if self._protocol in S3_PROTOCOLS else self._protocol return fsspec.filesystem(protocol, **self._credentials, **self._fs_args) @property def _normalized_path(self) -> str: if self._protocol in S3_PROTOCOLS: return urlparse(self._path)._replace(scheme="s3").geturl() return self._path @cachedmethod(cache=operator.attrgetter("_partition_cache")) def _list_partitions(self) -> List[str]: return [ path for path in self._filesystem.find(self._normalized_path, **self._load_args) if path.endswith(self._filename_suffix) ] def _join_protocol(self, path: str) -> str: if self._path.startswith(self._protocol) and not path.startswith( self._protocol ): return f"{self._protocol}://{path}" return path def _partition_to_path(self, path: str): dir_path = self._path.rstrip(self._sep) path = path.lstrip(self._sep) full_path = self._sep.join([dir_path, path]) + self._filename_suffix return full_path def _path_to_partition(self, path: str) -> str: dir_path = self._filesystem._strip_protocol(self._normalized_path) path = path.split(dir_path, 1).pop().lstrip(self._sep) if self._filename_suffix and path.endswith(self._filename_suffix): path = path[: -len(self._filename_suffix)] return path def _load(self) -> Dict[str, Callable[[], Any]]: partitions = {} for partition in self._list_partitions(): kwargs = deepcopy(self._dataset_config) # join the protocol back since PySpark may rely on it kwargs[self._filepath_arg] = self._join_protocol(partition) dataset = self._dataset_type(**kwargs) # type: ignore partition_id = self._path_to_partition(partition) partitions[partition_id] = dataset.load if not partitions: raise DataSetError(f"No partitions found in '{self._path}'") return partitions def _save(self, data: Dict[str, Any]) -> None: if self._overwrite and self._filesystem.exists(self._normalized_path): self._filesystem.rm(self._normalized_path, recursive=True) for partition_id, partition_data in sorted(data.items()): kwargs = deepcopy(self._dataset_config) partition = self._partition_to_path(partition_id) # join the protocol back since tools like PySpark may rely on it kwargs[self._filepath_arg] = self._join_protocol(partition) dataset = self._dataset_type(**kwargs) # type: ignore if callable(partition_data): partition_data = partition_data() dataset.save(partition_data) self._invalidate_caches() def _describe(self) -> Dict[str, Any]: clean_dataset_config = ( {k: v for k, v in self._dataset_config.items() if k != CREDENTIALS_KEY} if isinstance(self._dataset_config, dict) else self._dataset_config ) return dict( path=self._path, dataset_type=self._dataset_type.__name__, dataset_config=clean_dataset_config, ) def _invalidate_caches(self): self._partition_cache.clear() self._filesystem.invalidate_cache(self._normalized_path) def _exists(self) -> bool: return bool(self._list_partitions()) def _release(self) -> None: super()._release() self._invalidate_caches()
[docs]class IncrementalDataSet(PartitionedDataSet): """``IncrementalDataSet`` inherits from ``PartitionedDataSet``, which loads and saves partitioned file-like data using the underlying dataset definition. For filesystem level operations it uses `fsspec`: https://github.com/intake/filesystem_spec. ``IncrementalDataSet`` also stores the information about the last processed partition in so-called `checkpoint` that is persisted to the location of the data partitions by default, so that subsequent pipeline run loads only new partitions past the checkpoint. Example: :: >>> from kedro.io import IncrementalDataSet >>> >>> # these credentials will be passed to: >>> # a) 'fsspec.filesystem()' call, >>> # b) the dataset initializer, >>> # c) the checkpoint initializer >>> credentials = {"key1": "secret1", "key2": "secret2"} >>> >>> data_set = IncrementalDataSet( >>> path="s3://bucket-name/path/to/folder", >>> dataset="pandas.CSVDataSet", >>> credentials=credentials >>> ) >>> loaded = data_set.load() # loads all available partitions >>> # assert isinstance(loaded, dict) >>> >>> data_set.confirm() # update checkpoint value to the last processed partition ID >>> reloaded = data_set.load() # still loads all available partitions >>> >>> data_set.release() # clears load cache >>> # returns an empty dictionary as no new partitions were added >>> data_set.load() """ DEFAULT_CHECKPOINT_TYPE = "kedro.extras.datasets.text.TextDataSet" DEFAULT_CHECKPOINT_FILENAME = "CHECKPOINT" # pylint: disable=too-many-arguments
[docs] def __init__( self, path: str, dataset: Union[str, Type[AbstractDataSet], Dict[str, Any]], checkpoint: Union[str, Dict[str, Any]] = None, filepath_arg: str = "filepath", filename_suffix: str = "", credentials: Dict[str, Any] = None, load_args: Dict[str, Any] = None, fs_args: Dict[str, Any] = None, ): """Creates a new instance of ``IncrementalDataSet``. Args: path: Path to the folder containing partitioned data. If path starts with the protocol (e.g., ``s3://``) then the corresponding ``fsspec`` concrete filesystem implementation will be used. If protocol is not specified, ``fsspec.implementations.local.LocalFileSystem`` will be used. **Note:** Some concrete implementations are bundled with ``fsspec``, while others (like ``s3`` or ``gcs``) must be installed separately prior to usage of the ``PartitionedDataSet``. dataset: Underlying dataset definition. This is used to instantiate the dataset for each file located inside the ``path``. Accepted formats are: a) object of a class that inherits from ``AbstractDataSet`` b) a string representing a fully qualified class name to such class c) a dictionary with ``type`` key pointing to a string from b), other keys are passed to the Dataset initializer. Credentials for the dataset can be explicitly specified in this configuration. checkpoint: Optional checkpoint configuration. Accepts a dictionary with the corresponding dataset definition including ``filepath`` (unlike ``dataset`` argument). Checkpoint configuration is described here: https://kedro.readthedocs.io/en/stable/data/kedro_io.html#checkpoint-configuration Credentials for the checkpoint can be explicitly specified in this configuration. filepath_arg: Underlying dataset initializer argument that will contain a path to each corresponding partition file. If unspecified, defaults to "filepath". filename_suffix: If specified, only partitions that end with this string will be processed. credentials: Protocol-specific options that will be passed to ``fsspec.filesystem`` https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.filesystem, the dataset dataset initializer and the checkpoint. If the dataset or the checkpoint configuration contains explicit credentials spec, then such spec will take precedence. All possible credentials management scenarios are documented here: https://kedro.readthedocs.io/en/stable/data/kedro_io.html#partitioned-dataset-credentials load_args: Keyword arguments to be passed into ``find()`` method of the filesystem implementation. fs_args: Extra arguments to pass into underlying filesystem class constructor (e.g. `{"project": "my-project"}` for ``GCSFileSystem``). Raises: DataSetError: If versioning is enabled for the underlying dataset. """ super().__init__( path=path, dataset=dataset, filepath_arg=filepath_arg, filename_suffix=filename_suffix, credentials=credentials, load_args=load_args, fs_args=fs_args, ) self._checkpoint_config = self._parse_checkpoint_config(checkpoint) self._force_checkpoint = self._checkpoint_config.pop("force_checkpoint", None) comparison_func = self._checkpoint_config.pop("comparison_func", operator.gt) if isinstance(comparison_func, str): comparison_func = load_obj(comparison_func) self._comparison_func = comparison_func
def _parse_checkpoint_config( self, checkpoint_config: Union[str, Dict[str, Any], None] ) -> Dict[str, Any]: checkpoint_config = deepcopy(checkpoint_config) if isinstance(checkpoint_config, str): checkpoint_config = {"force_checkpoint": checkpoint_config} checkpoint_config = checkpoint_config or {} for key in {VERSION_KEY, VERSIONED_FLAG_KEY} & checkpoint_config.keys(): raise DataSetError( f"'{self.__class__.__name__}' does not support versioning of the " f"checkpoint. Please remove '{key}' key from the checkpoint definition." ) default_checkpoint_path = self._sep.join( [self._normalized_path.rstrip(self._sep), self.DEFAULT_CHECKPOINT_FILENAME] ) default_config = { "type": self.DEFAULT_CHECKPOINT_TYPE, self._filepath_arg: default_checkpoint_path, } if self._credentials: default_config[CREDENTIALS_KEY] = deepcopy(self._credentials) if ( # pylint: disable=consider-iterating-dictionary CREDENTIALS_KEY in default_config.keys() & checkpoint_config.keys() ): self._logger.warning( KEY_PROPAGATION_WARNING, {"keys": CREDENTIALS_KEY, "target": "checkpoint"}, ) return {**default_config, **checkpoint_config} @cachedmethod(cache=operator.attrgetter("_partition_cache")) def _list_partitions(self) -> List[str]: checkpoint = self._read_checkpoint() checkpoint_path = ( self._filesystem._strip_protocol( # pylint: disable=protected-access self._checkpoint_config[self._filepath_arg] ) ) def _is_valid_partition(partition) -> bool: if not partition.endswith(self._filename_suffix): return False if partition == checkpoint_path: return False if checkpoint is None: # nothing was processed yet return True partition_id = self._path_to_partition(partition) return self._comparison_func(partition_id, checkpoint) return sorted( part for part in self._filesystem.find(self._normalized_path, **self._load_args) if _is_valid_partition(part) ) @property def _checkpoint(self) -> AbstractDataSet: type_, kwargs = parse_dataset_definition(self._checkpoint_config) return type_(**kwargs) # type: ignore def _read_checkpoint(self) -> Union[str, None]: if self._force_checkpoint is not None: return self._force_checkpoint try: return self._checkpoint.load() except DataSetError: return None def _load(self) -> Dict[str, Callable[[], Any]]: partitions = {} for partition in self._list_partitions(): partition_id = self._path_to_partition(partition) kwargs = deepcopy(self._dataset_config) # join the protocol back since PySpark may rely on it kwargs[self._filepath_arg] = self._join_protocol(partition) partitions[partition_id] = self._dataset_type( # type: ignore **kwargs ).load() return partitions
[docs] def confirm(self) -> None: """Confirm the dataset by updating the checkpoint value to the latest processed partition ID""" partition_ids = [self._path_to_partition(p) for p in self._list_partitions()] if partition_ids: self._checkpoint.save(partition_ids[-1]) # checkpoint to last partition