kedro.extras.datasets.spark.SparkHiveDataSet

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

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. Upsert methodology works by leveraging Spark DataFrame execution plan checkpointing.

Example usage for the YAML API:

hive_dataset:
  type: spark.SparkHiveDataSet
  database: hive_database
  table: table_name
  write_mode: overwrite

Example usage for the Python API:

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)

Attributes

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_SAVE_ARGS: Dict[str, Any] = {}
__init__(database, table, write_mode='errorifexists', table_pk=None, save_args=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".

  • save_args (Optional[Dict[str, Any]]) – Optional mapping of any options, passed to the DataFrameWriter.saveAsTable as kwargs. Key example of this is partitionBy which allows data partitioning on a list of column names. Other HiveOptions can be found here: https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html#specifying-storage-format-for-hive-tables

Note

For users leveraging the upsert functionality, a checkpoint directory must be set, e.g. using spark.sparkContext.setCheckpointDir(“/path/to/dir”) or directly in the Spark conf folder.

Raises

DatasetError – Invalid configuration supplied

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 – Data set name.

  • config – Data set config dictionary.

  • load_version – 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 – 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.

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

TypeVar(_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 (TypeVar(_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