kedro.io.CachedDataset

class kedro.io.CachedDataset(dataset, version=None, copy_mode=None, metadata=None)

CachedDataset is a dataset wrapper which caches in memory the data saved, so that the user avoids io operations with slow storage media.

You can also specify a CachedDataset in catalog.yml:

test_ds:
   type: CachedDataset
   versioned: true
   dataset:
      type: pandas.CSVDataset
      filepath: example.csv

Please note that if your dataset is versioned, this should be indicated in the wrapper class as shown above.

Methods

exists()	Checks whether a dataset's output already exists by calling the provided _exists() method.
from_config(name, config[, load_version, ...])	Create a dataset instance using the configuration provided.
load()	Loads data by delegation to the provided load method.
release()	Release any cached data.
save(data)	Saves data by delegation to the provided save method.
to_config()	Converts the dataset instance into a dictionary-based configuration for serialization.

__init__(dataset, version=None, copy_mode=None, metadata=None)

Creates a new instance of CachedDataset pointing to the provided Python object.

Parameters:

• dataset (AbstractDataset | dict) – A Kedro Dataset object or a dictionary to cache.

• version (Version | None) – If specified, should be an instance of kedro.io.core.Version. If its load attribute is None, the latest version will be loaded. If its save attribute is None, save version will be autogenerated.

• copy_mode (str | None) – The copy mode used to copy the data. Possible values are: “deepcopy”, “copy” and “assign”. If not provided, it is inferred based on the data type.

• metadata (dict[str, Any] | None) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

Raises:

ValueError – If the provided dataset is not a valid dict/YAML representation of a dataset or an actual dataset.

exists()

Checks whether a dataset’s output already exists by calling the provided _exists() method.

Return type:

bool

Returns:

Flag indicating whether the output already exists.

Raises:

DatasetError – when underlying exists method raises error.

classmethod from_config(name, config, load_version=None, save_version=None)

Create a dataset instance using the configuration provided.

Parameters:

• name (str) – Data set name.

• config (dict[str, Any]) – Data set config dictionary.

• load_version (str | None) – Version string to be used for load operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

• save_version (str | None) – Version string to be used for save operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

DatasetError – When the function fails to create the dataset from its config.

load()

Loads data by delegation to the provided load method.

Return type:

Any

Returns:

Data returned by the provided load method.

Raises:

DatasetError – When underlying load method raises error.

release()

Release any cached data.

Raises:

DatasetError – when underlying release method raises error.

Return type:

None

save(data)

Saves data by delegation to the provided save method.

Parameters:

data (Any) – the value to be saved by provided save method.

Raises:

• DatasetError – when underlying save method raises error.

• FileNotFoundError – when save method got file instead of dir, on Windows.

• NotADirectoryError – when save method got file instead of dir, on Unix.

Return type:

None

to_config()

Converts the dataset instance into a dictionary-based configuration for serialization. Ensures that any subclass-specific details are handled, with additional logic for versioning and caching implemented for CachedDataset.

Adds a key for the dataset’s type using its module and class name and includes the initialization arguments.

For CachedDataset it extracts the underlying dataset’s configuration, handles the versioned flag and removes unnecessary metadata. It also ensures the embedded dataset’s configuration is appropriately flattened or transformed.

If the dataset has a version key, it sets the versioned flag in the configuration.

Removes the metadata key from the configuration if present.

Return type:

dict[str, Any]

Returns:

A dictionary containing the dataset’s type and initialization arguments.