kedro.extras.datasets.redis.PickleDataSet

class kedro.extras.datasets.redis.PickleDataSet(key, backend='pickle', load_args=None, save_args=None, credentials=None, redis_args=None)[source]

PickleDataSet loads/saves data from/to a Redis database. The underlying functionality is supported by the redis library, so it supports all allowed options for instantiating the redis app from_url and setting a value.

Example adding a catalog entry with YAML API:

my_python_object: # simple example
  type: redis.PickleDataSet
  key: my_object
  from_url_args:
    url: redis://127.0.0.1:6379

final_python_object: # example with save args
  type: redis.PickleDataSet
  key: my_final_object
  from_url_args:
    url: redis://127.0.0.1:6379
    db: 1
  save_args:
    ex: 10

Example using Python API:

from kedro.extras.datasets.redis import PickleDataSet
import pandas as pd

data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5],
                      'col3': [5, 6]})

my_data = PickleDataSet(key="my_data")
my_data.save(data)
reloaded = my_data.load()
assert data.equals(reloaded)

Attributes

DEFAULT_LOAD_ARGS

DEFAULT_REDIS_URL

DEFAULT_SAVE_ARGS

Methods

exists()

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

from_config(name, config[, load_version, …])

Create a data set 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.

DEFAULT_LOAD_ARGS: Dict[str, Any] = {}
DEFAULT_REDIS_URL = 'redis://127.0.0.1:6379'
DEFAULT_SAVE_ARGS: Dict[str, Any] = {}
__init__(key, backend='pickle', load_args=None, save_args=None, credentials=None, redis_args=None)[source]

Creates a new instance of PickleDataSet. This loads/saves data from/to a Redis database while deserialising/serialising. Supports custom backends to serialise/deserialise objects.

Example backends that are compatible (non-exhaustive):
  • pickle

  • dill

  • compress_pickle

Example backends that are incompatible:
  • torch

Parameters
Raises
  • ValueError – If backend does not satisfy the pickle interface.

  • ImportError – If the backend module could not be imported.

exists()

Checks whether a data set’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 data set instance using the configuration provided.

Parameters
  • name (str) – Data set name.

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

  • load_version (Optional[str]) – Version string to be used for load operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.

  • save_version (Optional[str]) – Version string to be used for save operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.

Return type

AbstractDataSet

Returns

An instance of an AbstractDataSet subclass.

Raises

DataSetError – When the function fails to create the data set from its config.

load()

Loads data by delegation to the provided load method.

Return type

~_DO

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 (~_DI) – 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