kedro_datasets.spark.SparkStreamingDataset

class kedro_datasets.spark.SparkStreamingDataset(*, filepath='', file_format='', save_args=None, load_args=None, metadata=None)

SparkStreamingDataset loads data to Spark Streaming Dataframe objects.

Example usage for the YAML API:

raw.new_inventory:
  type: spark.SparkStreamingDataset
  filepath: data/01_raw/stream/inventory/
  file_format: json
  save_args:
    output_mode: append
    checkpoint: data/04_checkpoint/raw_new_inventory
    header: True
  load_args:
    schema:
        filepath: data/01_raw/schema/inventory_schema.json

Attributes

DEFAULT_LOAD_ARGS
DEFAULT_SAVE_ARGS

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 from filepath.
release()	Release any cached data.
save(data)	Saves pyspark dataframe.
to_config()	Converts the dataset instance into a dictionary-based configuration for serialization.

DEFAULT_LOAD_ARGS: dict[str, Any] = {}

DEFAULT_SAVE_ARGS: dict[str, Any] = {}

__init__(*, filepath='', file_format='', save_args=None, load_args=None, metadata=None)

Creates a new instance of SparkStreamingDataset.

Parameters:

• filepath (str) – Filepath in POSIX format to a Spark dataframe. When using Databricks specify filepath``s starting with ``/dbfs/. For message brokers such as Kafka and all filepath is not required.

• file_format (str) – File format used during load and save operations. These are formats supported by the running SparkContext including parquet, csv, and delta. For a list of supported formats please refer to the Apache Spark documentation at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html

• load_args (Optional[dict[str, Any]]) – Load args passed to Spark DataFrameReader load method. It is dependent on the selected file format. You can find a list of read options for each selected format in Spark DataFrame read documentation, see https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html. Please note that a schema is mandatory for a streaming DataFrame if schemaInference is not True.

• save_args (Optional[dict[str, Any]]) – Save args passed to Spark DataFrameReader write options. Similar to load_args, this is dependent on the selected file format. You can pass mode and partitionBy to specify your overwrite mode and partitioning respectively. You can find a list of options for each selected format in Spark DataFrame write documentation, see https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html

• metadata (Optional[dict[str, Any]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

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 (Optional[str]) – 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 (Optional[str]) – 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 from filepath. If the connector type is kafka then no file_path is required, schema needs to be seperated from load_args. :rtype: DataFrame :returns: Data from filepath as pyspark dataframe.

release()

Release any cached data.

Raises:

DatasetError – when underlying release method raises error.

Return type:

None

save(data)

Saves pyspark dataframe. :type data: DataFrame :param data: PySpark streaming dataframe for saving

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.