kedro.extras.datasets.spark.SparkHiveDataSet

class kedro.extras.datasets.spark.SparkHiveDataSet(database, table, write_mode, table_pk=None)[source]

Bases: kedro.io.core.AbstractDataSet

SparkHiveDataSet loads and saves Spark dataframes stored on Hive. This data set also handles some incompatible file types such as using partitioned parquet on hive which will not normally allow upserts to existing data without a complete replacement of the existing file/partition.

This DataSet has some key assumptions: - Schemas do not change during the pipeline run (defined PKs must be present for the duration of the pipeline) - Tables are not being externally modified during upserts. The upsert method is NOT ATOMIC to external changes to the target table while executing.

Example:

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

from kedro.extras.datasets.spark import SparkHiveDataSet

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 = SparkHiveDataSet(database="test_database", table="test_table",
                            write_mode="overwrite")
data_set.save(spark_df)
reloaded = data_set.load()

reloaded.take(4)

Methods

SparkHiveDataSet.__init__(database, table, …) Creates a new instance of SparkHiveDataSet.
SparkHiveDataSet.exists() Checks whether a data set’s output already exists by calling the provided _exists() method.
SparkHiveDataSet.from_config(name, config[, …]) Create a data set instance using the configuration provided.
SparkHiveDataSet.load() Loads data by delegation to the provided load method.
SparkHiveDataSet.release() Release any cached data.
SparkHiveDataSet.save(data) Saves data by delegation to the provided save method.
__init__(database, table, write_mode, table_pk=None)[source]

Creates a new instance of SparkHiveDataSet.

Parameters:
  • database (str) – The name of the hive database.
  • table (str) – The name of the table within the database.
  • write_mode (str) – insert, upsert or overwrite are supported.
  • table_pk (Optional[List[str]]) – If performing an upsert, this identifies the primary key columns used to resolve preexisting data. Is required for write_mode="upsert".
Raises:

DataSetError – Invalid configuration supplied
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
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