kedro_datasets.email.EmailMessageDataset¶
- class kedro_datasets.email.EmailMessageDataset(*, filepath, load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)[source]¶
EmailMessageDataset
loads/saves an email message from/to a file using an underlying filesystem (e.g.: local, S3, GCS). It uses theemail
package in the standard library to manage email messages.Note that
EmailMessageDataset
doesn’t handle sending email messages.Example:
from email.message import EmailMessage from kedro_datasets.email import EmailMessageDataset string_to_write = "what would you do if you were invisable for one day????" # Create a text/plain message msg = EmailMessage() msg.set_content(string_to_write) msg["Subject"] = "invisibility" msg["From"] = '"sin studly17"' msg["To"] = '"strong bad"' dataset = EmailMessageDataset(filepath=tmp_path / "test") dataset.save(msg) reloaded = dataset.load() assert msg.__dict__ == reloaded.__dict__
Attributes
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.
Compute the version the dataset should be loaded with.
Compute the version the dataset should be saved with.
save
(data)Saves data by delegation to the provided save method.
Converts the dataset instance into a dictionary-based configuration for serialization.
- DEFAULT_FS_ARGS: dict[str, Any] = {'open_args_load': {'mode': 'r'}, 'open_args_save': {'mode': 'w'}}¶
- __init__(*, filepath, load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)[source]¶
Creates a new instance of
EmailMessageDataset
pointing to a concrete text file on a specific filesystem.- Parameters:
filepath (
str
) – Filepath in POSIX format to a text file prefixed with a protocol like s3://. If prefix is not provided, file protocol (local filesystem) will be used. The prefix should be any protocol supported byfsspec
. Note: http(s) doesn’t support versioning.load_args (
Optional
[dict
[str
,Any
]]) –email
options for parsing email messages (arguments passed intoemail.parser.Parser.parse
). Here you can find all available arguments: https://docs.python.org/3/library/email.parser.html#email.parser.Parser.parse If you would like to specify options for the Parser, you can include them under the “parser” key. Here you can find all available arguments: https://docs.python.org/3/library/email.parser.html#email.parser.Parser All defaults are preserved, but “policy”, which is set toemail.policy.default
.save_args (
Optional
[dict
[str
,Any
]]) –email
options for generating MIME documents (arguments passed intoemail.generator.Generator.flatten
). Here you can find all available arguments: https://docs.python.org/3/library/email.generator.html#email.generator.Generator.flatten If you would like to specify options for the Generator, you can include them under the “generator” key. Here you can find all available arguments: https://docs.python.org/3/library/email.generator.html#email.generator.Generator All defaults are preserved.version (
Optional
[Version
]) – If specified, should be an instance ofkedro.io.core.Version
. If itsload
attribute is None, the latest version will be loaded. If itssave
attribute is None, save version will be autogenerated.credentials (
Optional
[dict
[str
,Any
]]) – Credentials required to get access to the underlying filesystem. E.g. forGCSFileSystem
it should look like {“token”: None}.fs_args (
Optional
[dict
[str
,Any
]]) – Extra arguments to pass into underlying filesystem class constructor (e.g. {“project”: “my-project”} forGCSFileSystem
), as well as to pass to the filesystem’s open method through nested keys open_args_load and open_args_save. Here you can find all available arguments for open: https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.open All defaults are preserved, except mode, which is set to r when loading and to w when saving.metadata (
Optional
[dict
[str
,Any
]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.
- exists()[source]¶
Checks whether a dataset’s output already exists by calling the provided _exists() method.
- Return type:
- 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)[source]¶
Create a dataset instance using the configuration provided.
- Parameters:
name (
str
) – Data set name.load_version (
Optional
[str
]) – Version string to be used forload
operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.save_version (
Optional
[str
]) – Version string to be used forsave
operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.
- Return type:
- Returns:
An instance of an
AbstractDataset
subclass.- Raises:
DatasetError – When the function fails to create the dataset from its config.
- load()[source]¶
Loads data by delegation to the provided load method.
- Return type:
- Returns:
Data returned by the provided load method.
- Raises:
DatasetError – When underlying load method raises error.
- release()[source]¶
Release any cached data.
- Raises:
DatasetError – when underlying release method raises error.
- Return type:
- save(data)[source]¶
Saves data by delegation to the provided save method.
- Parameters:
data (
Message
) – 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:
- to_config()[source]¶
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.