kedro.extras.datasets.spark.SparkDataSet¶

class kedro.extras.datasets.spark.SparkDataSet(filepath, file_format='parquet', load_args=None, save_args=None, version=None, credentials=None)[source]¶

Bases: kedro.io.core.AbstractVersionedDataSet

SparkDataSet loads and saves Spark dataframes.

Example:

from pyspark.sql import SparkSession
from pyspark.sql.types import (StructField, StringType,
                               IntegerType, StructType)

from kedro.extras.datasets.spark import SparkDataSet

schema = StructType([StructField("name", StringType(), True),
                     StructField("age", IntegerType(), True)])

data = [('Alex', 31), ('Bob', 12), ('Clarke', 65), ('Dave', 29)]

spark_df = SparkSession.builder.getOrCreate()                                .createDataFrame(data, schema)

data_set = SparkDataSet(filepath="test_data")
data_set.save(spark_df)
reloaded = data_set.load()

reloaded.take(4)

Attributes

`SparkDataSet.DEFAULT_LOAD_ARGS`
`SparkDataSet.DEFAULT_SAVE_ARGS`

Methods

`SparkDataSet.__init__`(filepath[, …])	Creates a new instance of `SparkDataSet`.
`SparkDataSet.exists`()	Checks whether a data set’s output already exists by calling the provided _exists() method.
`SparkDataSet.from_config`(name, config[, …])	Create a data set instance using the configuration provided.
`SparkDataSet.load`()	Loads data by delegation to the provided load method.
`SparkDataSet.release`()	Release any cached data.
`SparkDataSet.resolve_load_version`()	Compute the version the dataset should be loaded with.
`SparkDataSet.resolve_save_version`()	Compute the version the dataset should be saved with.
`SparkDataSet.save`(data)	Saves data by delegation to the provided save method.

DEFAULT_LOAD_ARGS = {}¶

DEFAULT_SAVE_ARGS = {}¶

__init__(filepath, file_format='parquet', load_args=None, save_args=None, version=None, credentials=None)[source]¶

Creates a new instance of SparkDataSet.

Parameters:

filepath (str) – Filepath in POSIX format to a Spark dataframe. When using Databricks and working with data written to mount path points, specify filepath``s for (versioned) ``SparkDataSet``s starting with ``/dbfs/mnt.
file_format (str) – File format used during load and save operations. These are formats supported by the running SparkContext include parquet, csv. For a list of supported formats please refer to Apache Spark documentation at https://spark.apache.org/docs/latest/sql-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 supported format in Spark DataFrame read documentation: https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame
save_args (Optional[Dict[str, Any]]) – Save args passed to Spark DataFrame 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 format in Spark DataFrame write documentation: https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame
version (Optional[Version]) – 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.
credentials (Optional[Dict[str, Any]]) – Credentials to access the S3 bucket, such as aws_access_key_id, aws_secret_access_key, if filepath prefix is s3a:// or s3n://. Optional keyword arguments passed to hdfs.client.InsecureClient if filepath prefix is hdfs://. Ignored otherwise.

Return type:

None

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:	`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`

resolve_load_version()¶

Compute the version the dataset should be loaded with.

Return type:	`Optional`[`str`]

resolve_save_version()¶

Compute the version the dataset should be saved with.

Return type:	`Optional`[`str`]

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.
Return type:	`None`